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May 9, 2019

In Fraud We Trust: Top 5 Cases of Misconduct in University Research

There’s a thin line between madness and immorality. This idea of the “mad scientist” has taken on a charming, even glorified perception in popular culture. From the campy portrayal of Nikola Tesla in the first issue of Superman, to Dr. Frankenstein, to Dr. Emmet Brown of Back to the Future, there’s no question Hollywood has softened the idea of the mad scientist. So, I will not paint the scientists involved in these five cases of research fraud as such. The immoral actions of these researchers didn’t just affect their own lives, but also the lives and careers of innocent students, patients, and colleagues. Academic fraud is not only a crime, it is a threat to the intellectual integrity upon which the evolution of knowledge rests. It also compromises the integrity of the institution, as any institution will take a blow to their reputation for allowing academic misconduct to go unnoticed under its watch. Here, you will find the top five most notorious cases of fraud in university research in only the last few years

Fraud in Psychology Research

In 2011, a Dutch psychologist named Diederik Stapel committed academic fraud in a number of publications over the course of ten years, spanning three different universities: the University of Groningen, the University of Amsterdam, and Tilburg University.

Among the dozens of studies in question, most notably, he falsified data on a study which analyzed racial stereotyping and the effects of advertisements on personal identity. The journal Science published the study, which claimed that one particular race stereotyped and discriminated against another particular race in a chaotic, messy environment, versus an organized, structured one. Stapel produced another study which claimed that the average person determined employment applicants to be more competent if they had a male voice. As a result, both studies were found to be contaminated with false, manipulated data.

Psychologists discovered Stapel’s falsified work and reported that his work did not stand up to scrutiny. Moreover, they concluded that Stapel took advantage of a loose system, under which researchers were able to work in almost total secrecy and very lightly maneuver data to reach their conclusions with little fear of being contested. A host of newspapers published Stapel’s research all over the world. He even oversaw and administered over a dozen doctoral theses; all of which have been rendered invalid, thereby compromising the integrity of former students’ degrees.

“I have failed as a scientist and a researcher. I feel ashamed for it and have great regret,” lamented Stapel to the New York Times. You can read the particulars of this fraud case here .

Duke University Cancer Research Fraud

In 2010, Dr. Anil Potti left Duke University after allegations of research fraud surfaced. The fraud came in waves. First, Dr. Potti flagrantly lied about being a Rhodes Scholar to attain hundreds of thousands of dollars in grant money from the American Cancer Society. Then, Dr. Potti was caught outright falsifying data in his research, after he discovered one of his theories for personalized cancer treatment was disproven. This theory was intended to justify clinical trials for over a hundred patients. Because it was disproven, the trials could no longer take place. Dr. Potti falsified data in order to continue with these trials and attain further funding.

Over a dozen papers that he published were retracted from various medical journals, including the New England Journal of Medicine.

Dr. Potti had been working on personalized cancer treatment he hailed as “the holy grail of cancer.” There are a lot of people whose bodies fail to respond to more traditional cancer treatments. Personalized treatments, however, offer hope because patients are exposed to treatments that are tailored to their own unique body constitution, and the type of tumors they have. Because of this, patients flocked to Duke to register for trials for these drugs. They were even told there was an 80% chance that they would find the right drug for them. The patients who partook in these trials filed a lawsuit against Duke, alleging that the institution performed ill-performed chemotherapy on participants. Patients were so excited that there was renewed hope for their cancer treatment, that they trusted Dr. Potti’s trials and drugs. Sadly, many of these cancer patients suffered from unusual side effects like blood clots and damaged joints.

Duke settled these lawsuits with the families of the patients. You can read details of the case here .

Plagiarism in Kansas

Mahesh Visvanathan and Gerald Lushington, two computer scientists from the University of Kansas, confessed to accusations of plagiarism. They copied large chunks of their research from the works of other scientists in their field. The plagiarism was so ubiquitous that even the summary statement of their presentation was lifted from another scientist’s article in a renowned journal.

Visvanathan and Lushington oversaw a program at the University of Kansas in which researchers reviewed and processed large amounts of data for DNA analysis. In this case, Visvanathan committed the plagiarism and Lushington knowingly refrained from reporting it to the university. Learn more about this case here .

Columbia University Research Misconduct

The year was 2010. Bengü Sezen was finally caught falsifying data after ten years of continuously committing fraud. Her fraudulent activity was so blatant that she even made up fake people and organizations in an effort to support her research results. Sezen was found guilty of committing over 20 acts of research misconduct, with about ten research papers recalled for redaction due to plagiarism and outright fabrication.

Sezen’s doctoral thesis was fabricated entirely in order to produce her desired results. Additionally, her misconduct greatly affected the careers of other young scientists who worked with her. These scientists dedicated a large portion of their graduate careers trying to reproduce Sezen’s desired results.

Columbia University moved to retract her Ph.D in chemistry. Sezen fled the country during her investigation.   Read further details about this case here .

Penn State Fraud

In 2012, Craig Grimes ripped off the U.S. government to the tune of $3 million. He pleaded guilty to wire fraud, money laundering, and engaging in fraudulent statements to attain grant money.

Grimes bamboozled the National Institute of Health (NIH) and the National Science Foundation (NSF) into granting him $1.2 million for research on gases in blood, which helps detect disorders in infants. Sadly, it was revealed by the Attorney’s Office that Grimes never carried out this research, and instead used the majority of his granted funds for personal expenditures. In addition to that $1.2 million, Grimes also falsified information that helped him attain $1.9 million in grant money via the American Recovery and Reinvestment Act. Consequently, a federal judge ruled that Grimes spend 41 months in prison and pay back over $660,000 to Penn State, the NIH, and the NSF.

Check out the details about this case here .

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Journals in a library

Research findings that are probably wrong cited far more than robust ones, study finds

Academics suspect papers with grabby conclusions are waved through more easily by reviewers

Scientific research findings that are probably wrong gain far more attention than robust results, according to academics who suspect that the bar for publication may be lower for papers with grabbier conclusions.

Studies in top science, psychology and economics journals that fail to hold up when others repeat them are cited, on average, more than 100 times as often in follow-up papers than work that stands the test of time.

The finding – which is itself not exempt from the need for scrutiny – has led the authors to suspect that more interesting papers are waved through more easily by reviewers and journal editors and, once published, attract more attention.

“It could be wasting time and resources,” said Dr Marta Serra-Garcia, who studies behavioural and experimental economics at the University of California in San Diego. “But we can’t conclude that something is true or not based on one study and one replication.” What is needed, she said, is a simple way to check how often studies have been repeated, and whether or not the original findings are confirmed.

The study in Science Advances is the latest to highlight the “replication crisis” where results, mostly in social science and medicine, fail to hold up when other researchers try to repeat experiments. Following an influential paper in 2005 titled Why most published research findings are false , three major projects have found replication rates as low as 39% in psychology journals , 61% in economics journals , and 62% in social science studies published in the Nature and Science, two of the most prestigious journals in the world.

Working with Uri Gneezy, a professor of behavioural economics at UCSD, Serra-Garcia analysed how often studies in the three major replication projects were cited in later research papers. Studies that failed replication accrued, on average, 153 more citations in the period examined than those whose results held up. For the social science studies published in Science and Nature, those that failed replication typically gained 300 more citations than those that held up. Only 12% of the citations acknowledged that replication projects had failed to confirm the relevant findings.

The academic system incentivises journals and researchers to publish exciting findings, and citations are taken into account for promotion and tenure. But history suggests that the more dramatic the results , the more likely they are to be wrong . Dr Serra-Garcia said publishing the name of the overseeing editor on journal papers might help to improve the situation.

Prof Gary King, a political scientist at Harvard University, said the latest findings may be good news. He wants researchers to focus their efforts on claims that are subject to disagreement, so that they can gather more data and figure out the truth. “In some ways, then, we should regard the results of this interesting article as great news for the health of the scholarly community,” he said.

Prof Brian Nosek at the University of Virginia, who runs the Open Science Collaboration to assess reproducibility in psychology research, urged caution. “We presume that science is self-correcting. By that we mean that errors will happen regularly, but science roots out and removes those errors in the ongoing dialogue among scientists conducting, reporting, and citing each others research. If more replicable findings are less likely to be cited, it could suggest that science isn’t just failing to self-correct; it might be going in the wrong direction.’

“The evidence is not sufficient to draw such a conclusion, but it should get our attention and inspire us to look more closely at how the social systems of science foster self-correction and how they can be improved,” he added.

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Time to assume that health research is fraudulent until proven otherwise?

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Health research is based on trust. Health professionals and journal editors reading the results of a clinical trial assume that the trial happened and that the results were honestly reported. But about 20% of the time, said Ben Mol, professor of obstetrics and gynaecology at Monash Health, they would be wrong. As I’ve been concerned about research fraud for 40 years, I wasn’t that surprised as many would be by this figure, but it led me to think that the time may have come to stop assuming that research actually happened and is honestly reported, and assume that the research is fraudulent until there is some evidence to support it having happened and been honestly reported. The Cochrane Collaboration, which purveys “trusted information,” has now taken a step in that direction.

As he described in a webinar last week, Ian Roberts, professor of epidemiology at the London School of Hygiene & Tropical Medicine, began to have doubts about the honest reporting of trials after a colleague asked if he knew that his systematic review showing the mannitol halved death from head injury was based on trials that had never happened. He didn’t, but he set about investigating the trials and confirmed that they hadn’t ever happened. They all had a lead author who purported to come from an institution that didn’t exist and who killed himself a few years later. The trials were all published in prestigious neurosurgery journals and had multiple co-authors. None of the co-authors had contributed patients to the trials, and some didn’t know that they were co-authors until after the trials were published. When Roberts contacted one of the journals the editor responded that “I wouldn’t trust the data.” Why, Roberts wondered, did he publish the trial? None of the trials have been retracted.

Later Roberts, who headed one of the Cochrane groups, did a systematic review of colloids versus crystalloids only to discover again that many of the trials that were included in the review could not be trusted. He is now sceptical about all systematic reviews, particularly those that are mostly reviews of multiple small trials. He compared the original idea of systematic reviews as searching for diamonds, knowledge that was available if brought together in systematic reviews; now he thinks of systematic reviewing as searching through rubbish. He proposed that small, single centre trials should be discarded, not combined in systematic reviews.

Mol, like Roberts, has conducted systematic reviews only to realise that most of the trials included either were zombie trials that were fatally flawed or were untrustworthy. What, he asked, is the scale of the problem? Although retractions are increasing, only about 0.04% of biomedical studies have been retracted, suggesting the problem is small. But the anaesthetist John Carlisle analysed 526 trials submitted to Anaesthesia and found that 73 (14%) had false data, and 43 (8%) he categorised as zombie. When he was able to examine individual patient data in 153 studies, 67 (44%) had untrustworthy data and 40 (26%) were zombie trials. Many of the trials came from the same countries (Egypt, China, India, Iran, Japan, South Korea, and Turkey), and when John Ioannidis, a professor at Stanford University, examined individual patient data from trials submitted from those countries to Anaesthesia during a year he found that many were false: 100% (7/7) in Egypt; 75% (3/ 4) in Iran; 54% (7/13) in India; 46% (22/48) in China; 40% (2/5) in Turkey; 25% (5/20) in South Korea; and 18% (2/11) in Japan.  Most of the trials were zombies. Ioannidis concluded that there are hundreds of thousands of zombie trials published from those countries alone.

Others have found similar results, and Mol’s best guess is that about 20% of trials are false. Very few of these papers are retracted.

We have long known that peer review is ineffective at detecting fraud, especially if the reviewers start, as most have until now, by assuming that the research is honestly reported. I remember being part of a panel in the 1990s investigating one of Britain’s most outrageous cases of fraud, when the statistical reviewer of the study told us that he had found multiple problems with the study and only hoped that it was better done than it was reported. We asked if had ever considered that the study might be fraudulent, and he told us that he hadn’t.

We have now reached a point where those doing systematic reviews must start by assuming that a study is fraudulent until they can have some evidence to the contrary. Some supporting evidence comes from the trial having been registered and having ethics committee approval. Andrew Grey, an associate professor of medicine at the University of Auckland, and others have developed a checklist with around 40 items that can be used as a screening tool for fraud (you can view the checklist here ). The REAPPRAISED checklist (Research governance, Ethics, Authorship, Plagiarism, Research conduct, Analyses and methods, Image manipulation, Statistics, Errors, Data manipulation and reporting) covers issues like “ethical oversight and funding, research productivity and investigator workload, validity of randomisation, plausibility of results and duplicate data reporting.” The checklist has been used to detect studies that have subsequently been retracted but hasn’t been through the full evaluation that you would expect for a clinical screening tool. (But I must congratulate the authors on a clever acronym: some say that dreaming up the acronym for a study is the most difficult part of the whole process.)

Roberts and others wrote about the problem of the many untrustworthy and zombie trials in The BMJ six years ago with the provocative title: “The knowledge system underpinning healthcare is not fit for purpose and must change.” They wanted the Cochrane Collaboration and anybody conducting systematic reviews to take very seriously the problem of fraud. It was perhaps coincidence, but a few weeks before the webinar the Cochrane Collaboration produced guidelines on reviewing studies where there has been a retraction, an expression of concern, or the reviewers are worried about the trustworthiness of the data. 

Retractions are the easiest to deal with, but they are, as Mol said, only a tiny fraction of untrustworthy or zombie studies. An editorial in the Cochrane Library accompanying the new guidelines recognises that there is no agreement on what constitutes an untrustworthy study, screening tools are not reliable, and “Misclassification could also lead to reputational damage to authors, legal consequences, and ethical issues associated with participants having taken part in research, only for it to be discounted.”  The Collaboration is being cautious but does stand to lose credibility—and income—if the world ceases to trust Cochrane Reviews because they are thought to be based on untrustworthy trials.

Research fraud is often viewed as a problem of “bad apples,” but Barbara K Redman, who spoke at the webinar insists that it is not a problem of bad apples but bad barrels if not, she said, of rotten forests or orchards. In her book Research Misconduct Policy in Biomedicine: Beyond the Bad-Apple Approach she argues that research misconduct is a systems problem—the system provides incentives to publish fraudulent research and does not have adequate regulatory processes. Researchers progress by publishing research, and because the publication system is built on trust and peer review is not designed to detect fraud it is easy to publish fraudulent research. The business model of journals and publishers depends on publishing, preferably lots of studies as cheaply as possible. They have little incentive to check for fraud and a positive disincentive to experience reputational damage—and possibly legal risk—from retracting studies. Funders, universities, and other research institutions similarly have incentives to fund and publish studies and disincentives to make a fuss about fraudulent research they may have funded or had undertaken in their institution—perhaps by one of their star researchers. Regulators often lack the legal standing and the resources to respond to what is clearly extensive fraud, recognising that proving a study to be fraudulent (as opposed to suspecting it of being fraudulent) is a skilled, complex, and time consuming process. Another problem is that research is increasingly international with participants from many institutions in many countries: who then takes on the unenviable task of investigating fraud? Science really needs global governance.

Everybody gains from the publication game, concluded Roberts, apart from the patients who suffer from being given treatments based on fraudulent data.

Stephen Lock, my predecessor as editor of The BMJ , became worried about research fraud in the 1980s, but people thought his concerns eccentric. Research authorities insisted that fraud was rare, didn’t matter because science was self-correcting, and that no patients had suffered because of scientific fraud. All those reasons for not taking research fraud seriously have proved to be false, and, 40 years on from Lock’s concerns, we are realising that the problem is huge, the system encourages fraud, and we have no adequate way to respond. It may be time to move from assuming that research has been honestly conducted and reported to assuming it to be untrustworthy until there is some evidence to the contrary.

Richard Smith  was the editor of  The BMJ  until 2004.

Competing interest: RS was a cofounder of the Committee on Medical Ethics (COPE), for many years the chair of the Cochrane Library Oversight Committee, and a member of the board of the UK Research Integrity Office.

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21 findings from the Reuters Institute’s research in 2021 still relevant in 2022

A health worker administers a dose of Johnson & Johnson vaccine against the coronavirus disease (COVID-19) to a resident as he takes a selfie, during mass vaccination at the Ilha Grande island, one of the most famous tourist spots in Rio de Janeiro state, Brazil, July 10, 2021. REUTERS/Lucas Landau

A health worker administers a dose of a COVID-19 vaccine in Rio de Janeiro, Brazil. REUTERS/Lucas Landau

2021 has been an important year for journalism. The pandemic has raised new questions about coverage and has accelerated the shift to remote and hybrid work. Companies have grappled with familiar challenges such as debunking false information and tackling the lack of diversity in terms of output, leadership and staff. More newsrooms have embraced reader revenue to fight declines in advertising and print sales. 

The academic researchers of the Reuters Institute have published factsheets, reports and academic articles about many of these issues. As the year draws to a close, here are 21 findings from our research in 2021 that will be still relevant in 2022.

1. Trust in news is up in the wake of the pandemic

Data from this year’s Digital News Report shows that trust in news has grown, on average, by six points during the pandemic, with 44% of our total sample saying they trust most news most of the time. This reverses, to some extent, recent falls in average trust, bringing levels back to those of 2018. Finland remains the country with the highest levels of overall trust (65%). The US (29%) has the lowest trust levels in our survey. | Learn more

2. People who don’t trust the news tend to be older, less educated and less interested in politics

Original survey data from Brazil, India, the UK and the US that we published as part of the Trust in News project in September shows that the 'generally untrusting' toward news tend to be older, less educated and less interested in politics. In India, the UK and the US they are also less connected to urban centres. | Learn more

3. More people are concerned about misinformation: COVID-19 is their main concern

Our data shows that concern about misinformation is a little higher this year (58%). There is most concern in Africa (74%), followed by Latin America (65%) and North America (63%). On average, people claim to have seen more false and misleading information about COVID-19 (54%) than about politics (43%). When asked which sources of misinformation they are most concerned about, 29% point to national politicians, a higher percentage than any other source. 

When it comes to the channels through which COVID misinformation is spread, we find that there is most concern about Facebook (28%), followed by news websites and apps (17%), and WhatsApp and other messaging apps (15%). In much of the Global South, messaging apps such as WhatsApp draw the most concern. | Learn more

Proportion that finds each most concerning for COVID-19 misinformation – all markets

4. Using news organisations as a news source about COVID-19 is associated with lower belief in vaccine misinformation

Original survey data we published in May suggests that using news organisations as a source for news and information about coronavirus decreases the rate by which people believe in vaccine misinformation in all eight countries studied. In contrast, the source in our data that is most consistently associated with higher misinformation belief is relying on messaging apps such as WhatsApp and Telegram.

Around half of our respondents think the news media have done a good job of explaining how the vaccines work and how the population will be vaccinated. These figures are typically lower for the national governments. In most countries people are more likely to think the media have exaggerated the risks than the government, but (with the exception of Spain) most people do not think this has happened. | Learn more

5. Visuals in COVID-19 misinformation are usually mislabelled, not manipulated

Our peer-reviewed analysis of visuals used in COVID-19 misinformation showed that the majority were simply mislabelled as opposed to manipulated--and even those that were manipulated were created using simple tools rather than high-tech methods like AI or deep fakes. These findings come from a mixed-methods analysis of ninety-six examples of visuals in misinformation rated false or misleading by independent professional fact-checkers from the first three months of 2020. | Learn more

6. Most people want news to be neutral and reflect a wide range of views

Our data shows that a clear majority of people in all markets want news outlets to reflect a range of different views when reporting on social and political issues. Most people also want news outlets to remain neutral, but a large minority says that sometimes this makes no sense. Among people under 25, this percentage is 40% in Brazil, 34% in Germany, 38% in the UK, and 30% in the US. As for those on the political left, the percentage of people who think sometimes neutrality doesn’t make sense ranges from 36% in Germany to 54% in the US. | Learn more

7. Access to news is becoming more distributed, especially for young audiences

Across all markets covered by this year’s Digital News Report, only 25% of the people in our global survey prefer to start their news journeys with a website or news app. Those aged 18–24 have an even weaker connection with websites and apps and are almost twice as likely to prefer to access news via social media, aggregators, or mobile alerts. | Learn more

8. People that use search, social and aggregators have more diverse news diets

Using web tracking data from the UK, our peer-reviewed research found that people who more often use social media, search engines, and news aggregators to get news have more diverse news diets than people who mainly access news by going directly to news websites. This is because platforms tend to surface news from a range of sources, whereas people who go direct return to the same news websites over and over again. However, this also means that people who more often use search, social and aggregators are also more likely to have news diets that contain a mixture of more partisan outlets on both the left and the right. | Learn more

Estimated diversity of people’s news repertoires by number of news accesses for each mode

9. Just 5% live in politically partisan online news echo chambers

Despite concern over online news echo chambers being a consistent theme in recent debates, our peer-reviewed research found that just ~5% only use partisan news outlets of a particular political leaning across the seven different countries studied. Although estimates can vary depending on what news outlets are considered sufficiently left- or right-leaning to count, the size of the echo chamber is usually considerably smaller than the 15-30% that say they do not use any online news at all. | Learn more

10. Mainstream media struggle to get noticed at newer, more visual social networks

Our data suggests many social media news users pay the most attention to mainstream media on both Facebook and Twitter. But even here, news brands and journalists have to compete with a range of voices that can often be more engaging and strident. Politicians and political activists, who often use social media to bypass mainstream media, receive a significant share of news attention on social networks like Twitter.

​​The democratising impact of social media is also laid bare in the following chart, with significant attention going to the views of ordinary people across all networks. In platforms such as Instagram, Snapchat, and TikTok, the focus is firmly on celebrities and other influencers, leaving journalists playing second fiddle, even when it comes to news. | Learn more

11. Accounts suspended on Twitter are mostly human-operated, focusing on divisive issues

Our peer-reviewed research based on analysis of political tweets during elections in France, Germany, and the UK found that Twitter appears to suspend accounts that focus on amplifying divisive issues like immigration and religion and systematic activities increasing the visibility of specific political figures (often but not always on the right). We also found that suspended accounts were overwhelmingly human operated and no more likely than other accounts to share “fake news.” | Learn more

12. The pandemic has accelerated the demise of print

This year’s Digital News Report shows that the proportion of people who use print as a news source has fallen even further as a result of lockdowns and behavioural changes. The percentage of people using print to get their news has fallen in Switzerland from 63% in 2016 to 37% in 2021. Our figures show similar declines in countries such as Poland, Portugal, Germany and Spain. | Learn more

13. Reader revenue is now considered more important than ads by the news leaders we surveyed

The COVID-19 shock has reinforced a view that the industry needs to break the dependence on digital advertising. Our survey of news leaders for the report we published in January reflects this shift, with more respondents saying they would focus on subscription (76%) and fewer saying they would emphasise advertising (66%) than when we last asked this question in 2018. It also shows how important diversification has become, with commercial publishers citing, on average, four different revenue streams as being important or very important to them. | Learn more

14. More people pay for news online, but most people don’t

The last year has seen more quality journalism go behind paywalls. El País in Spain, El Tiempo in Colombia, and News 24 in South Africa are amongst those to have started their paywall journeys in the midst of the pandemic. Overall progress remains slow. Across 20 countries where publishers have been actively pushing digital subscriptions we find 17% saying that they have paid for some kind of online news in the last year. That’s up by two percentage points in the last year and up five since 2016 (12%). 

Despite this, it is important to note that the vast majority of consumers in these countries continue to resist paying for any online news. The most successful countries are Norway 45% (+3) and Sweden 30% (+3) - though some countries like Switzerland 17% (+4) and the Netherlands 17% (+3) also saw increases in 2021. Around a fifth (21%) now pay for at least one online news outlet in the United States, 20% in Finland, and 13% in Australia. By contrast, just 9% say they pay in Germany and 8% in the UK. | Learn more

Proportion that paid for any online news in the last year - selected markets

Pay DNR.

15. Most people don’t want governments to step in to help news organisations

Across 33 markets surveyed for this year’s Digital News Report, just 27% think that governments should step in to help commercial news organisations that can’t make enough money on their own, compared to 44% that think they should not. If we look at the data market-by-market, we do see some national variation, but on the whole the picture is quite consistent. In all but a handful of markets, the proportion opposed to government intervention is larger than the proportion that supports it. | Learn more

16. Only 22% of the 180 top editors across 240 major outlets in 12 markets are women

In 11 out of 12 markets covered in a factsheet we published in March, the majority of top editors are men, including countries like Brazil and Finland where women outnumber men among working journalists. The percentage of women in top editorial positions varies significantly from market to market. In Japan none of the major news outlets in our sample has a woman as their top editor. In South Africa a majority of the top editors (60%) are women. | Learn more

17. Only 15% of the 80 top editors across 100 brands in five markets are non-white

In Brazil, Germany and the UK, none of the outlets in our sample have a non-white top editor. In the US, there are three non-white top editors in our sample (18%). In South Africa a majority (60%) are non-white. There has been no significant overall increase in the number of non-white top editors over the last year across the markets covered in the factsheet we published in March. | Learn more

18. News leaders feel the industry is not doing enough to tackle its diversity problem

Up to 27% of the respondents to our survey of 132 news leaders from 42 countries work for news organisations not doing any of the initiatives listed in the chart shown here. Around 41% say their companies have someone in charge of diversity and inclusion, but only 29% have a budget to actively promote this in their newsrooms and beyond. When asked to rate their outlets in different diversity areas, most of our respondents (78%) think they are doing well in terms of gender diversity. A much smaller proportion say the same about ethnic diversity (38%) and political diversity (33%). | Learn more

19. Younger women are more likely to say the news media covers them unfairly

Data from this year’s Digital News Report shows that both women and men are more likely to say that the media covers them fairly rather than unfairly. However, younger women are much more likely to say that the news media covers them unfairly than younger men. The difference between perceptions of men and women under 25 is especially large in Brazil, Spain, and the US. There are large generational differences in how women think they are covered by the news media, with younger women offering a much less favourable assessment. | Learn more

20. Newsrooms embrace hybrid working, but many are still figuring out how to make it work

One-third of the leaders surveyed say their companies have moved to hybrid working, with 57% suggesting they are still working out the best way to do it and only 9% saying their companies are going back to a model similar to the one before the pandemic. Our respondents say remote working has made them more efficient and has improved employee well-being. However, it has been negative for collaboration, creativity and communication. | Learn more

21. Many people hold cynical views about how journalists do their jobs

Survey data from Brazil, India, the UK and the US suggest that large minorities in all four countries have very negative views about basic journalistic practices, including deliberately seeking to manipulate the public. Remarkably, these views vary only somewhat between those who otherwise exhibit low and high trust in news. Here are a few figures from Brazil: 78% think journalists try to cover up mistakes, 36% think they often accept undisclosed payments from sources and 35% think they often allow opinions to influence coverage. You can check out more figures in the chart below. | Learn more

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Why Most Published Research Findings Are False

  • John P. A. Ioannidis


Published: August 30, 2005

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25 Aug 2022: Ioannidis JPA (2022) Correction: Why Most Published Research Findings Are False. PLOS Medicine 19(8): e1004085. View correction

Table 1

There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

Citation: Ioannidis JPA (2005) Why Most Published Research Findings Are False. PLoS Med 2(8): e124.

Copyright: © 2005 John P. A. Ioannidis. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Competing interests: The author has declared that no competing interests exist.

Abbreviation: PPV, positive predictive value

Published research findings are sometimes refuted by subsequent evidence, with ensuing confusion and disappointment. Refutation and controversy is seen across the range of research designs, from clinical trials and traditional epidemiological studies [ 1–3 ] to the most modern molecular research [ 4 , 5 ]. There is increasing concern that in modern research, false findings may be the majority or even the vast majority of published research claims [ 6–8 ]. However, this should not be surprising. It can be proven that most claimed research findings are false. Here I will examine the key factors that influence this problem and some corollaries thereof.

Modeling the Framework for False Positive Findings

Several methodologists have pointed out [ 9–11 ] that the high rate of nonreplication (lack of confirmation) of research discoveries is a consequence of the convenient, yet ill-founded strategy of claiming conclusive research findings solely on the basis of a single study assessed by formal statistical significance, typically for a p -value less than 0.05. Research is not most appropriately represented and summarized by p -values, but, unfortunately, there is a widespread notion that medical research articles should be interpreted based only on p -values. Research findings are defined here as any relationship reaching formal statistical significance, e.g., effective interventions, informative predictors, risk factors, or associations. “Negative” research is also very useful. “Negative” is actually a misnomer, and the misinterpretation is widespread. However, here we will target relationships that investigators claim exist, rather than null findings.

It can be proven that most claimed research findings are false

As has been shown previously, the probability that a research finding is indeed true depends on the prior probability of it being true (before doing the study), the statistical power of the study, and the level of statistical significance [ 10 , 11 ]. Consider a 2 × 2 table in which research findings are compared against the gold standard of true relationships in a scientific field. In a research field both true and false hypotheses can be made about the presence of relationships. Let R be the ratio of the number of “true relationships” to “no relationships” among those tested in the field. R is characteristic of the field and can vary a lot depending on whether the field targets highly likely relationships or searches for only one or a few true relationships among thousands and millions of hypotheses that may be postulated. Let us also consider, for computational simplicity, circumscribed fields where either there is only one true relationship (among many that can be hypothesized) or the power is similar to find any of the several existing true relationships. The pre-study probability of a relationship being true is R /( R + 1). The probability of a study finding a true relationship reflects the power 1 - β (one minus the Type II error rate). The probability of claiming a relationship when none truly exists reflects the Type I error rate, α. Assuming that c relationships are being probed in the field, the expected values of the 2 × 2 table are given in Table 1 . After a research finding has been claimed based on achieving formal statistical significance, the post-study probability that it is true is the positive predictive value, PPV. The PPV is also the complementary probability of what Wacholder et al. have called the false positive report probability [ 10 ]. According to the 2 × 2 table, one gets PPV = (1 - β) R /( R - βR + α). A research finding is thus more likely true than false if (1 - β) R > α. Since usually the vast majority of investigators depend on a = 0.05, this means that a research finding is more likely true than false if (1 - β) R > 0.05.


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What is less well appreciated is that bias and the extent of repeated independent testing by different teams of investigators around the globe may further distort this picture and may lead to even smaller probabilities of the research findings being indeed true. We will try to model these two factors in the context of similar 2 × 2 tables.

First, let us define bias as the combination of various design, data, analysis, and presentation factors that tend to produce research findings when they should not be produced. Let u be the proportion of probed analyses that would not have been “research findings,” but nevertheless end up presented and reported as such, because of bias. Bias should not be confused with chance variability that causes some findings to be false by chance even though the study design, data, analysis, and presentation are perfect. Bias can entail manipulation in the analysis or reporting of findings. Selective or distorted reporting is a typical form of such bias. We may assume that u does not depend on whether a true relationship exists or not. This is not an unreasonable assumption, since typically it is impossible to know which relationships are indeed true. In the presence of bias ( Table 2 ), one gets PPV = ([1 - β] R + u β R )/( R + α − β R + u − u α + u β R ), and PPV decreases with increasing u , unless 1 − β ≤ α, i.e., 1 − β ≤ 0.05 for most situations. Thus, with increasing bias, the chances that a research finding is true diminish considerably. This is shown for different levels of power and for different pre-study odds in Figure 1 . Conversely, true research findings may occasionally be annulled because of reverse bias. For example, with large measurement errors relationships are lost in noise [ 12 ], or investigators use data inefficiently or fail to notice statistically significant relationships, or there may be conflicts of interest that tend to “bury” significant findings [ 13 ]. There is no good large-scale empirical evidence on how frequently such reverse bias may occur across diverse research fields. However, it is probably fair to say that reverse bias is not as common. Moreover measurement errors and inefficient use of data are probably becoming less frequent problems, since measurement error has decreased with technological advances in the molecular era and investigators are becoming increasingly sophisticated about their data. Regardless, reverse bias may be modeled in the same way as bias above. Also reverse bias should not be confused with chance variability that may lead to missing a true relationship because of chance.


Panels correspond to power of 0.20, 0.50, and 0.80.


Testing by Several Independent Teams

Several independent teams may be addressing the same sets of research questions. As research efforts are globalized, it is practically the rule that several research teams, often dozens of them, may probe the same or similar questions. Unfortunately, in some areas, the prevailing mentality until now has been to focus on isolated discoveries by single teams and interpret research experiments in isolation. An increasing number of questions have at least one study claiming a research finding, and this receives unilateral attention. The probability that at least one study, among several done on the same question, claims a statistically significant research finding is easy to estimate. For n independent studies of equal power, the 2 × 2 table is shown in Table 3 : PPV = R (1 − β n )/( R + 1 − [1 − α] n − R β n ) (not considering bias). With increasing number of independent studies, PPV tends to decrease, unless 1 - β < a, i.e., typically 1 − β < 0.05. This is shown for different levels of power and for different pre-study odds in Figure 2 . For n studies of different power, the term β n is replaced by the product of the terms β i for i = 1 to n , but inferences are similar.




A practical example is shown in Box 1 . Based on the above considerations, one may deduce several interesting corollaries about the probability that a research finding is indeed true.

Box 1. An Example: Science at Low Pre-Study Odds

Let us assume that a team of investigators performs a whole genome association study to test whether any of 100,000 gene polymorphisms are associated with susceptibility to schizophrenia. Based on what we know about the extent of heritability of the disease, it is reasonable to expect that probably around ten gene polymorphisms among those tested would be truly associated with schizophrenia, with relatively similar odds ratios around 1.3 for the ten or so polymorphisms and with a fairly similar power to identify any of them. Then R = 10/100,000 = 10 −4 , and the pre-study probability for any polymorphism to be associated with schizophrenia is also R /( R + 1) = 10 −4 . Let us also suppose that the study has 60% power to find an association with an odds ratio of 1.3 at α = 0.05. Then it can be estimated that if a statistically significant association is found with the p -value barely crossing the 0.05 threshold, the post-study probability that this is true increases about 12-fold compared with the pre-study probability, but it is still only 12 × 10 −4 .

Now let us suppose that the investigators manipulate their design, analyses, and reporting so as to make more relationships cross the p = 0.05 threshold even though this would not have been crossed with a perfectly adhered to design and analysis and with perfect comprehensive reporting of the results, strictly according to the original study plan. Such manipulation could be done, for example, with serendipitous inclusion or exclusion of certain patients or controls, post hoc subgroup analyses, investigation of genetic contrasts that were not originally specified, changes in the disease or control definitions, and various combinations of selective or distorted reporting of the results. Commercially available “data mining” packages actually are proud of their ability to yield statistically significant results through data dredging. In the presence of bias with u = 0.10, the post-study probability that a research finding is true is only 4.4 × 10 −4 . Furthermore, even in the absence of any bias, when ten independent research teams perform similar experiments around the world, if one of them finds a formally statistically significant association, the probability that the research finding is true is only 1.5 × 10 −4 , hardly any higher than the probability we had before any of this extensive research was undertaken!

Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true. Small sample size means smaller power and, for all functions above, the PPV for a true research finding decreases as power decreases towards 1 − β = 0.05. Thus, other factors being equal, research findings are more likely true in scientific fields that undertake large studies, such as randomized controlled trials in cardiology (several thousand subjects randomized) [ 14 ] than in scientific fields with small studies, such as most research of molecular predictors (sample sizes 100-fold smaller) [ 15 ].

Corollary 2: The smaller the effect sizes in a scientific field, the less likely the research findings are to be true. Power is also related to the effect size. Thus research findings are more likely true in scientific fields with large effects, such as the impact of smoking on cancer or cardiovascular disease (relative risks 3–20), than in scientific fields where postulated effects are small, such as genetic risk factors for multigenetic diseases (relative risks 1.1–1.5) [ 7 ]. Modern epidemiology is increasingly obliged to target smaller effect sizes [ 16 ]. Consequently, the proportion of true research findings is expected to decrease. In the same line of thinking, if the true effect sizes are very small in a scientific field, this field is likely to be plagued by almost ubiquitous false positive claims. For example, if the majority of true genetic or nutritional determinants of complex diseases confer relative risks less than 1.05, genetic or nutritional epidemiology would be largely utopian endeavors.

Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true. As shown above, the post-study probability that a finding is true (PPV) depends a lot on the pre-study odds (R) . Thus, research findings are more likely true in confirmatory designs, such as large phase III randomized controlled trials, or meta-analyses thereof, than in hypothesis-generating experiments. Fields considered highly informative and creative given the wealth of the assembled and tested information, such as microarrays and other high-throughput discovery-oriented research [ 4 , 8 , 17 ], should have extremely low PPV.

Corollary 4: The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true. Flexibility increases the potential for transforming what would be “negative” results into “positive” results, i.e., bias, u . For several research designs, e.g., randomized controlled trials [ 18–20 ] or meta-analyses [ 21 , 22 ], there have been efforts to standardize their conduct and reporting. Adherence to common standards is likely to increase the proportion of true findings. The same applies to outcomes. True findings may be more common when outcomes are unequivocal and universally agreed (e.g., death) rather than when multifarious outcomes are devised (e.g., scales for schizophrenia outcomes) [ 23 ]. Similarly, fields that use commonly agreed, stereotyped analytical methods (e.g., Kaplan-Meier plots and the log-rank test) [ 24 ] may yield a larger proportion of true findings than fields where analytical methods are still under experimentation (e.g., artificial intelligence methods) and only “best” results are reported. Regardless, even in the most stringent research designs, bias seems to be a major problem. For example, there is strong evidence that selective outcome reporting, with manipulation of the outcomes and analyses reported, is a common problem even for randomized trails [ 25 ]. Simply abolishing selective publication would not make this problem go away.

Corollary 5: The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true. Conflicts of interest and prejudice may increase bias, u . Conflicts of interest are very common in biomedical research [ 26 ], and typically they are inadequately and sparsely reported [ 26 , 27 ]. Prejudice may not necessarily have financial roots. Scientists in a given field may be prejudiced purely because of their belief in a scientific theory or commitment to their own findings. Many otherwise seemingly independent, university-based studies may be conducted for no other reason than to give physicians and researchers qualifications for promotion or tenure. Such nonfinancial conflicts may also lead to distorted reported results and interpretations. Prestigious investigators may suppress via the peer review process the appearance and dissemination of findings that refute their findings, thus condemning their field to perpetuate false dogma. Empirical evidence on expert opinion shows that it is extremely unreliable [ 28 ].

Corollary 6: The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true. This seemingly paradoxical corollary follows because, as stated above, the PPV of isolated findings decreases when many teams of investigators are involved in the same field. This may explain why we occasionally see major excitement followed rapidly by severe disappointments in fields that draw wide attention. With many teams working on the same field and with massive experimental data being produced, timing is of the essence in beating competition. Thus, each team may prioritize on pursuing and disseminating its most impressive “positive” results. “Negative” results may become attractive for dissemination only if some other team has found a “positive” association on the same question. In that case, it may be attractive to refute a claim made in some prestigious journal. The term Proteus phenomenon has been coined to describe this phenomenon of rapidly alternating extreme research claims and extremely opposite refutations [ 29 ]. Empirical evidence suggests that this sequence of extreme opposites is very common in molecular genetics [ 29 ].

These corollaries consider each factor separately, but these factors often influence each other. For example, investigators working in fields where true effect sizes are perceived to be small may be more likely to perform large studies than investigators working in fields where true effect sizes are perceived to be large. Or prejudice may prevail in a hot scientific field, further undermining the predictive value of its research findings. Highly prejudiced stakeholders may even create a barrier that aborts efforts at obtaining and disseminating opposing results. Conversely, the fact that a field is hot or has strong invested interests may sometimes promote larger studies and improved standards of research, enhancing the predictive value of its research findings. Or massive discovery-oriented testing may result in such a large yield of significant relationships that investigators have enough to report and search further and thus refrain from data dredging and manipulation.

Most Research Findings Are False for Most Research Designs and for Most Fields

In the described framework, a PPV exceeding 50% is quite difficult to get. Table 4 provides the results of simulations using the formulas developed for the influence of power, ratio of true to non-true relationships, and bias, for various types of situations that may be characteristic of specific study designs and settings. A finding from a well-conducted, adequately powered randomized controlled trial starting with a 50% pre-study chance that the intervention is effective is eventually true about 85% of the time. A fairly similar performance is expected of a confirmatory meta-analysis of good-quality randomized trials: potential bias probably increases, but power and pre-test chances are higher compared to a single randomized trial. Conversely, a meta-analytic finding from inconclusive studies where pooling is used to “correct” the low power of single studies, is probably false if R ≤ 1:3. Research findings from underpowered, early-phase clinical trials would be true about one in four times, or even less frequently if bias is present. Epidemiological studies of an exploratory nature perform even worse, especially when underpowered, but even well-powered epidemiological studies may have only a one in five chance being true, if R = 1:10. Finally, in discovery-oriented research with massive testing, where tested relationships exceed true ones 1,000-fold (e.g., 30,000 genes tested, of which 30 may be the true culprits) [ 30 , 31 ], PPV for each claimed relationship is extremely low, even with considerable standardization of laboratory and statistical methods, outcomes, and reporting thereof to minimize bias.


Claimed Research Findings May Often Be Simply Accurate Measures of the Prevailing Bias

As shown, the majority of modern biomedical research is operating in areas with very low pre- and post-study probability for true findings. Let us suppose that in a research field there are no true findings at all to be discovered. History of science teaches us that scientific endeavor has often in the past wasted effort in fields with absolutely no yield of true scientific information, at least based on our current understanding. In such a “null field,” one would ideally expect all observed effect sizes to vary by chance around the null in the absence of bias. The extent that observed findings deviate from what is expected by chance alone would be simply a pure measure of the prevailing bias.

For example, let us suppose that no nutrients or dietary patterns are actually important determinants for the risk of developing a specific tumor. Let us also suppose that the scientific literature has examined 60 nutrients and claims all of them to be related to the risk of developing this tumor with relative risks in the range of 1.2 to 1.4 for the comparison of the upper to lower intake tertiles. Then the claimed effect sizes are simply measuring nothing else but the net bias that has been involved in the generation of this scientific literature. Claimed effect sizes are in fact the most accurate estimates of the net bias. It even follows that between “null fields,” the fields that claim stronger effects (often with accompanying claims of medical or public health importance) are simply those that have sustained the worst biases.

For fields with very low PPV, the few true relationships would not distort this overall picture much. Even if a few relationships are true, the shape of the distribution of the observed effects would still yield a clear measure of the biases involved in the field. This concept totally reverses the way we view scientific results. Traditionally, investigators have viewed large and highly significant effects with excitement, as signs of important discoveries. Too large and too highly significant effects may actually be more likely to be signs of large bias in most fields of modern research. They should lead investigators to careful critical thinking about what might have gone wrong with their data, analyses, and results.

Of course, investigators working in any field are likely to resist accepting that the whole field in which they have spent their careers is a “null field.” However, other lines of evidence, or advances in technology and experimentation, may lead eventually to the dismantling of a scientific field. Obtaining measures of the net bias in one field may also be useful for obtaining insight into what might be the range of bias operating in other fields where similar analytical methods, technologies, and conflicts may be operating.

How Can We Improve the Situation?

Is it unavoidable that most research findings are false, or can we improve the situation? A major problem is that it is impossible to know with 100% certainty what the truth is in any research question. In this regard, the pure “gold” standard is unattainable. However, there are several approaches to improve the post-study probability.

Better powered evidence, e.g., large studies or low-bias meta-analyses, may help, as it comes closer to the unknown “gold” standard. However, large studies may still have biases and these should be acknowledged and avoided. Moreover, large-scale evidence is impossible to obtain for all of the millions and trillions of research questions posed in current research. Large-scale evidence should be targeted for research questions where the pre-study probability is already considerably high, so that a significant research finding will lead to a post-test probability that would be considered quite definitive. Large-scale evidence is also particularly indicated when it can test major concepts rather than narrow, specific questions. A negative finding can then refute not only a specific proposed claim, but a whole field or considerable portion thereof. Selecting the performance of large-scale studies based on narrow-minded criteria, such as the marketing promotion of a specific drug, is largely wasted research. Moreover, one should be cautious that extremely large studies may be more likely to find a formally statistical significant difference for a trivial effect that is not really meaningfully different from the null [ 32–34 ].

Second, most research questions are addressed by many teams, and it is misleading to emphasize the statistically significant findings of any single team. What matters is the totality of the evidence. Diminishing bias through enhanced research standards and curtailing of prejudices may also help. However, this may require a change in scientific mentality that might be difficult to achieve. In some research designs, efforts may also be more successful with upfront registration of studies, e.g., randomized trials [ 35 ]. Registration would pose a challenge for hypothesis-generating research. Some kind of registration or networking of data collections or investigators within fields may be more feasible than registration of each and every hypothesis-generating experiment. Regardless, even if we do not see a great deal of progress with registration of studies in other fields, the principles of developing and adhering to a protocol could be more widely borrowed from randomized controlled trials.

Finally, instead of chasing statistical significance, we should improve our understanding of the range of R values—the pre-study odds—where research efforts operate [ 10 ]. Before running an experiment, investigators should consider what they believe the chances are that they are testing a true rather than a non-true relationship. Speculated high R values may sometimes then be ascertained. As described above, whenever ethically acceptable, large studies with minimal bias should be performed on research findings that are considered relatively established, to see how often they are indeed confirmed. I suspect several established “classics” will fail the test [ 36 ].

Nevertheless, most new discoveries will continue to stem from hypothesis-generating research with low or very low pre-study odds. We should then acknowledge that statistical significance testing in the report of a single study gives only a partial picture, without knowing how much testing has been done outside the report and in the relevant field at large. Despite a large statistical literature for multiple testing corrections [ 37 ], usually it is impossible to decipher how much data dredging by the reporting authors or other research teams has preceded a reported research finding. Even if determining this were feasible, this would not inform us about the pre-study odds. Thus, it is unavoidable that one should make approximate assumptions on how many relationships are expected to be true among those probed across the relevant research fields and research designs. The wider field may yield some guidance for estimating this probability for the isolated research project. Experiences from biases detected in other neighboring fields would also be useful to draw upon. Even though these assumptions would be considerably subjective, they would still be very useful in interpreting research claims and putting them in context.

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  • 20 July 2022

Exclusive: investigators found plagiarism and data falsification in work from prominent cancer lab

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Over the past decade, questions have swirled around the work coming out of a prominent US cancer-research laboratory run by Carlo Croce at the Ohio State University (OSU). Croce, a member of the US National Academy of Sciences, made his name with his work on the role of genes in cancer. But for years, he has faced allegations of plagiarism and falsified images in studies from his group. All told, 11 papers he has co-authored have been retracted, and 21 have required corrections.

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KFF COVID-19 Vaccine Monitor: Media and Misinformation

Liz Hamel , Lunna Lopes , Ashley Kirzinger , Grace Sparks , Mellisha Stokes, and Mollyann Brodie Published: Nov 08, 2021

  • Methodology

The KFF COVID-19 Vaccine Monitor is an ongoing research project tracking the public’s attitudes and experiences with COVID-19 vaccinations. Using a combination of surveys and qualitative research, this project tracks the dynamic nature of public opinion as vaccine development and distribution unfold, including vaccine confidence and acceptance, information needs, trusted messengers and messages, as well as the public’s experiences with vaccination.

Key Findings

  • Misinformation about health care topics is nothing new, but social media, the polarization of news sources, and the pace of scientific development on COVID-19 have all contributed to an environment that makes it easier than ever for ambiguous information, misinterpretation, and deliberate disinformation to spread. 1 We find in the latest Vaccine Monitor that belief in pandemic-related misinformation is widespread, with 78% of adults saying they have heard at least one of eight different false statements about COVID-19 and that they believe it to be true or are unsure if it is true or false. One-third (32%) of all adults believe or are uncertain about at least four false statements. Belief in COVID-19 misinformation is correlated with both vaccination status and partisanship, with unvaccinated adults and Republicans much more likely to believe or be unsure about false statements compared to vaccinated adults and Democrats.
  • With the public’s trust in news media declining over many years , we find that no news media source garners the trust of a majority of the public when it comes to COVID-19 information. While nearly half trust information about COVID-19 that they see on network and local television news, trust is lower for other news outlets and diverges in expected ways along partisan lines. Unvaccinated adults are far less likely than vaccinated adults to trust most of the news sources included in the survey for information on COVID-19, with the exception of conservative news sources.
  • People’s trusted news sources are correlated with their belief in COVID-19 misinformation. The share who hold at least four misconceptions is small (between 11-16%) among those who say they trust COVID-19 information from network news, local TV news, CNN, MSNBC, and NPR. This share rises to nearly four in ten among those who trust COVID-19 information from One America News (37%) and Fox News (36%), and to nearly half (46%) among those who trust information from Newsmax. One thing this study cannot disentangle is whether this is because people are exposed to misinformation from those news sources, or whether the types of people who choose those news sources are the same ones who are pre-disposed to believe certain types of misinformation for other reasons.
  • These findings suggest a challenge for reaching people with accurate information about COVID-19. While that challenge is particularly acute when it comes to reaching those who remain unvaccinated, the partisan divisions in misinformation and trusted news sources also have implications for those who are vaccinated, as we have reported a growing partisan divide in intention to get COVID-19 booster shots , even among the fully vaccinated.

Belief In COVID-19 Misinformation

Numerous studies have documented the prevalence of misinformation and disinformation about COVID-19, often fueled by social media 2 . The latest KFF COVID-19 Vaccine Monitor sheds light on how common it is for people to hear and believe certain “myths” about the disease and the vaccine, and how these beliefs correlate with individuals’ trusted media sources.

Belief or uncertainty about COVID-19 misinformation is widespread, with nearly eight in ten adults saying they have heard at least one of eight different pieces of misinformation and either believe them to be true or are not sure whether they are true or false. Most commonly, six in ten adults have heard that the government is exaggerating the number of COVID-19 deaths by counting deaths due to other factors as coronavirus deaths and either believe this to be true (38%) or aren’t sure if it’s true or false (22%). 3 About four in ten have heard that pregnant women should not get the COVID-19 vaccine and think this is true (17%) or aren’t sure (22%). Among women ages 18-44, 18% believe this to be true and 29% are uncertain.

Among other common myths, one-third believe or are unsure whether deaths due to the COVID-19 vaccine are being intentionally hidden by the government (35%), and about three in ten each believe or are unsure whether COVID-19 vaccines have been shown to cause infertility (31%) or whether Ivermectin is a safe and effective treatment for COVID-19 (28%). In addition, between a fifth and a quarter of the public believe or are unsure whether the vaccines can give you COVID-19 (25%), contain a microchip (24%), or can change your DNA (21%).

Overall, about one in five adults (22%) do not believe any of the eight pieces of information tested in the survey, while nearly half (46%) believe or are unsure about between one and three false statements. One-third of adults (32%) say they have heard at least four of these statements and believe them to be true or are uncertain if they’re true or false. There are notable differences in misinformation belief by vaccination status and partisan identity and smaller differences by community type and education level.

Among adults who have not gotten a COVID-19 vaccine, nearly two-thirds (64%) believe or are uncertain about four or more false statements about the virus. Among vaccinated adults, most believe or are unsure about at least one false statement, but just 19% say this about four or more statements. Unvaccinated adults are at least 20 percentage points more likely than vaccinated adults to lack knowledge about each piece of misinformation tested, with the largest gap on the statement that “Deaths due to the COVID-19 vaccine are being intentionally hidden by the government” (61% of unvaccinated adults believe or are unsure if this is true compared to 25% of vaccinated adults).

Nearly half (46%) of Republicans compared to just 14% of Democrats believe or are unsure about four or more misstatements about COVID-19. Strikingly, 84% of Republicans believe or are unsure whether the government is exaggerating the number of COVID-19 deaths by including deaths due to other causes, compared to just one third of Democrats. In addition, there are large gaps between Republicans and Democrats in the shares who believe or are unsure whether pregnant women should not get the vaccine (52% vs. 28%), whether the vaccines have been shown to cause infertility (43% vs. 15%), and whether Ivermectin is a safe and effective treatment for COVID-19 (44% vs. 10%).

In addition to these differences by partisanship and vaccination status, believing or having doubts about four or more pieces of COVID-19 misinformation is also more prevalent among rural residents compared to those living in urban and suburban areas, among those without a college degree compared to college graduates, and among those ages 18-49 compared to those ages 50 and over.

Trusted News Media Sources For COVID-19 Information

Previous Vaccine Monitor reports have shown that television news and social media are both prominent sources where people get information about COVID-19, while among non-media sources of information, health care providers are the most trusted . In this latest survey, we sought to understand how much people trust specific news sources when it comes to COVID-19 information.

Overall, there is no news source that garners trust from a majority of the public on the topic of COVID-19. At the top of the list, nearly half say they have “a great deal” or “a fair amount” of trust in COVID-19 information that they see or hear on their local TV news station (47%) and on network news like ABC, NBC, and CBS (45%). About a third put a similar level of trust in information they see on CNN (36%), MSNBC (33%), and NPR (32%) while three in ten say the same about Fox News (29%). A smaller share put at least a fair amount of trust in COVID-19 information from One America News and Newsmax (13% each).

Overall, far fewer people say they trust information about COVID-19 that they see on social media compared to traditional news platforms (13% say they trust information they see on YouTube, 9% on Facebook, 6% each on Twitter and TikTok, and 5% on Instagram). The group that is influenced by information they see on these platforms may be larger than the share that says they trust information they see there, as we previously found in January that 31% of adults got information about COVID-19 vaccines from social media over a two-week period, nearly as large as the share who got information from cable, network, and local TV news.

As has been well documented (in particular by the Pew Research Center ), the U.S. media environment has become increasingly polarized in recent years, with Democrats and Republicans placing trust in completely different news sources. This is true when it comes to trust in COVID-19 information as well. Majorities of Democrats say they trust information about COVID-19 from network news (72%), local TV news (66%), CNN (65%), MSNBC (56%), and NPR (51%), while none of these sources is trusted by a majority of independents or Republicans. Republicans’ most trusted sources of COVID-19 information is Fox News (49%) followed by smaller shares who trust local TV news (34%), network news (25%), and Newsmax (22%).

Trusted news sources for COVID-19 information differ by vaccination status in addition to partisanship. Among mainstream news sources, vaccinated adults are at least twice as likely as unvaccinated adults to say they trust COVID-19 information from their local TV news station, network news, CNN, MSNBC, and NPR. Similar shares of vaccinated and unvaccinated adults say they trust COVID-19 information they see on Fox News (29% and 30%, respectively). The one news source that is trusted by a larger share of unvaccinated adults compared to vaccinated adults is Newsmax (17% vs. 11%), though the shares who trust Newsmax are relatively small for both groups.

Relationship Between Trusted News Sources and Belief In COVID-19 Misinformation

People’s trusted news sources are correlated with their belief in COVID-19 misinformation. Among those who say they trust COVID-19 information from CNN, MSNBC, network news, NPR, and local TV news, between three in ten and four in ten do not believe any of the eight pieces of misinformation tested in the survey, while small shares (between 11%-16%) believe or are unsure about at least four falsehoods.

Belief in misinformation is higher among those who say they trust COVID-19 information from conservative news sources, with nearly four in ten of those who trust Fox News (36%) and One America News (37%) and nearly half (46%) of those who trust Newsmax for such information saying they have heard at least four of the falsehoods tested in the survey and either believe them to be true or are unsure if they’re true or false. One thing this study cannot disentangle is whether this is because people are exposed to misinformation from those news sources, or whether the types of people who choose those news sources are the same ones who are pre-disposed to believe certain types of misinformation for other reasons.

  • Coronavirus (COVID-19)
  • COVID-19 Vaccine Monitor
  • Coronavirus
  • Misinformation and Trust

news release

  • COVID-19 Misinformation is Ubiquitous: 78% of the Public Believes or is Unsure About At Least One False Statement, and Nearly a Third Believe At Least Four of Eight False Statements Tested

Also of Interest

  • KFF COVID-19 Vaccine Monitor: October 2021
  • KFF COVID-19 Vaccine Monitor: The Increasing Importance of Partisanship in Predicting COVID-19 Vaccination Status
  • KFF COVID-19 Vaccine Monitor: Views On The U.S. Role In Global Vaccine Distribution
  • Persistent Vaccine Myths
  • KFF COVID-19 Vaccine Monitor Dashboard

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Evaluating Published Research for False Findings and Related Considerations

examples of false research findings 2021

W. Harrison Webb

It is a bold and almost frightening conjecture that “most published research findings are false.” In the current COVID-19 era, most of us have noticed an uptick in the popular prevalence of published scientific and medical research, making us all more keenly attuned to the causal relationships established by peer-reviewed scientific publications. News headlines are replete with breaking medical discoveries, causal connections between diseases and their genesis, and all the opinions and recommendations accompanying these postulations.  Of course, news headlines and social media are equally populated with reported disagreements and dissenting opinions on the validity of the scientific research.  As a result, more people can intelligently analyze medical research using vocabulary such as “double-blind,” “peer-reviewed,” and “anecdotal.”  The same can be said for safety and efficacy recommendations issued by the Food and Drug Administration; the phrase “emergency approval” has been prolific in recent months.

But how reliable are these published research findings?  Does peer-reviewed medical literature deserve uncompromising deference?  At least one research scientist disagrees dramatically, and his bold proposition has garnered significant support since his opinion was published.  In his 2005 article in the PLoS Medicine Journal, Stanford University Professor Dr. John P.A. Ioannidis, M.D. [1] , empirically quantified the reliability of published medical research and reached the following conclusion:  most peer-reviewed medical literature may, in fact, be false . 

In his article titled “Why Most Published Research Findings are False,” [2] Dr. Ioannidis prefaces his findings by noting, “[t]here is increasing concern that in modern research, false findings may be the majority or even the vast majority of published research claims.” In fact, Dr. Ioannidis states that ‘[i]t can be proven that most claimed research findings are false,” and “[t]he probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field.” Based on these considerations, Dr. Ioannidis charges that as much as 80% of non-randomized studies (the most common) turn out to be incorrect.  He also notes that up to 25% of randomized trials (the gold standard) are wrong and perhaps even 10% of the largest randomized trials produced inaccurate relationships.  His mathematical quantification of this phenomena has been widely accepted in the scientific community, with the British Medical Journal once calling him “the scourge of sloppy science.” [3]

Dr. Ioannidis posits several corollaries in defense of his findings:

  • The smaller the studies conducted in the scientific field, the less likely the research findings are to be true.
  • The smaller the effect sizes in a scientific field, the less likely the research findings are to be true.
  • The greater the number and lesser the selection of test of relationships in the scientific field, the less likely the research findings are to be true.
  • The greater the flexibility in design, definitions, outcomes, and analytical modes in the scientific field, the less likely the research findings are to be true.
  • The greater the financial and other interests in the scientific field, the less likely the research findings are to be true.
  • The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true.

If most scientific studies are misleading, exaggerated or just completely wrong, then why do medical practitioners, scientists and attorneys continue to rely on peer-reviewed medical research in each of their respective fields?  In November 2010, five years after his published study, Dr. Ioannidis sat down with The Atlantic to discuss why the scientific community continues to rely on misinformation.  As discussed in the article, Dr. Ioannidis states that “[t]here is an intellectual conflict of interest that pressures researchers to find whatever it is that most likely to get them funded.”  Moreover, “[a]t every step in the process, there is room to distort results, a way to make a stronger claim or to select what is going to be concluded.” [4]

Surprisingly (or not), attorneys are also keenly aware of this phenomenon, particularly those attorneys who routinely work with experts and rely on peer-reviewed scientific research to support complex scientific allegations.  The reliability of probative medical and scientific research has long been a topic in heated expert battles and Daubert challenges. Attorneys and their retained experts routinely rely on published medical research to educate courts and juries on causal scientific relationships beyond the understanding of the average layman.  As such, a single peer-reviewed study can make or break a case, particularly in the realm of pharmaceutical and medical device litigation.

If Dr. Ioannidis’ propositions are true and most published research findings are false, are attorneys fighting a fictitious battle over the reliability of scientific research?  Perhaps.  But instead of tossing all medical and scientific research, attorneys can apply Dr. Ioannidis’ findings and corollaries to identify published research that is most reliable, likely to reach an accurate conclusion, and able to survive a Daubert challenge. 

Accordingly, the following factors may be helpful for attorneys in identifying the most reliable research to support their claims:

  • Larger scientific studies are more reliable than smaller studies.   Logically, this proposition makes sense – the more data that has been analyzed, the more reliable the causal relationships established by that data.  When evaluating your medical literature arsenal, peer-reviewed research with large test groups should rank higher than other studies with smaller test groups.  As Dr. Ioannidis notes, “other factors being equal, research findings are more likely true in scientific fields, such as randomized controlled trials in cardiology (several thousand subjects randomized) than in scientific fields with small studies, such as most research of molecular predictors (sample sizes 100-fold smaller).”  Thus, if your argument relies on a singular scientific study with a small test group, it is more likely that the relationships established by that study may be inherently inaccurate.  Likewise, potential errors in these findings are magnified by the increased ratio of inaccurate results to the total test population. Simply stated, a few bad apples could potentially spoil the whole bunch. The least scrutinized scientific studies are those where correlations are established in the largest of populations.
  • The larger the effect of a causal relationship in a scientific study, the more likely the published findings are to be true.   Generally, probative scientific research seeking to identify a large causal effect is more reliable than a study postulating a connection between two minute items.  Dr. Ioannidis stated his article that “research findings are more likely true in scientific fields with large effects, such as the impact of smoking on cancer or cardiovascular disease, than in scientific fields where postulated effect are small, such as risk factors for multigenetic diseases.”  Studies targeting small effects will undoubtedly be plagued with false-positive claims that distort the accuracy of the results, thus making them more vulnerable to challenges. 
  • Probative medical and scientific research with a single tested hypothesis is inherently more reliable than studies seeking to establish multiple causal relationships .  In the scientific community, studies that test a single proposition are more easily replicated and validated, lending support to the reliability of the results.  Conversely, attempting to test multiple theories in a single study may introduce additional errors that are more difficult to identify and isolate.   Accordingly, attorneys and retained experts should minimize their reliance on studies that establish multiple causal relationships and instead focus on studies that simply address the single causal relationship needed to prove their claim.
  • Medical and scientific research with rigid, uncompromising design, definitions and categorical results are more reliable than those with broader positive result definitions .  Scientific studies that permit flexibility in their definitions can easily be discredited and will be an easy target for opposing experts to attack the sufficiency and reliability of the related study.  Scientific studies that loosely define the predicted or hypothesized positive result may be criticized for postulating a causal relationship where one does not exist.  Instead, utilize medical research that strictly defines the test parameters, result matrices and predicted outcomes.
  • Probative scientific research funded for the purposes of litigation or by a biased third party should be avoided, as such studies are unreliable. This analysis is one that is familiar to most attorneys in the products liability realm. However, its importance should not go unstated. Relying on scientific studies financed by manufacturers, or even your client, will expose your claim to an inevitable and potentially fatal Daubert challenge. Instead, choose scientific studies prepared by reputable and scholarly bodies.  Most peer-reviewed scientific articles will note if the study was financed by any group with an interest in the outcome or may otherwise introduce an impermissible level of bias.

Dr. Ioannidis also makes a surprising corollary of particular interest to pharmaceutical or products liability attorneys:  the hotter a scientific field (with more scientific teams involved), the less likely the research findings are true.  As he explained, “[t]his seemingly paradoxical corollary follows because . . . the [positive predictive value] of isolated findings decreases when many investigators are involved in the same field.”  Dr. Ioannidis attributes this relationship to the desire of research teams to disseminate the most impressive positive results, or, similarly, for a research team to publish findings negating a positive result created by a competing research team.  With litigation often arising from the newest published research findings where a cause of action could accrue, it’s important for attorneys to be aware of this bias and use it to their advantage when defending those claims.

Of course, the validity of Dr. Ioannidis’ corollaries has been scrutinized since their publication in 2005.  Several scientists and medical doctors have criticized the mathematical model Dr. Ioannidis employed to quantify the rate of falsity in published research, responding that his bold conclusions may be exaggerated. [5]  However, the underlying considerations continue to provide useful guidance for attorneys and their retained experts.  In fact, Dr. Ioannidis states in the conclusion of his report: “Even though these assumptions would be considerably subjective, they would still be very useful interpreting research claims and putting them into context.”  For attorneys, these principles can empower the savvy litigator to prepare expert reports supported by the most reliable scientific research and attack those claims bolstered by more dubious published findings.

[1] Dr. Ioannadis is a Greek-American physician-scientist, writer, and Stanford University professor who has made contributions to evidence-based medicine, epidemiology, and clinical research. Ioannidis studies scientific research itself, meta-research primarily in clinical medicine and the social sciences. See  His paper on “Why Most Published Research Findings are False” has been the most-accessed article in the history of Public Library of Science (over 3 million views in 2020).

[2] Ioannidis JPA (2005) Why most published research findings are false. PLoS Med 2(8): e124.

[3] “John Ioannidis: Uncompromising gentle maniac”. BMJ. 351: h4992. doi:10.1136/bmj.h4992. ISSN 1756-1833. PMID 26404555. S2CID 10953475 .

[4] David H. Freedman, Lies, Damned Lies, and Medical Science , The Atlantic, November 2010 Issue.

[5] See, e.g., Goodman S, Greenland S. Why most published research findings are false: problems in the analysis.  PLoS Med . 2007;4(4):e168. doi:10.1371/journal.pmed.0040168

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COVID-19 false dichotomies and a comprehensive review of the evidence regarding public health, COVID-19 symptomatology, SARS-CoV-2 transmission, mask wearing, and reinfection

  • Kevin Escandón   ORCID: 1 ,
  • Angela L. Rasmussen 2 , 3 ,
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BMC Infectious Diseases volume  21 , Article number:  710 ( 2021 ) Cite this article

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Scientists across disciplines, policymakers, and journalists have voiced frustration at the unprecedented polarization and misinformation around coronavirus disease 2019 (COVID-19) pandemic. Several false dichotomies have been used to polarize debates while oversimplifying complex issues. In this comprehensive narrative review, we deconstruct six common COVID-19 false dichotomies, address the evidence on these topics, identify insights relevant to effective pandemic responses, and highlight knowledge gaps and uncertainties. The topics of this review are: 1) Health and lives vs. economy and livelihoods, 2) Indefinite lockdown vs. unlimited reopening, 3) Symptomatic vs. asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, 4) Droplet vs. aerosol transmission of SARS-CoV-2, 5) Masks for all vs. no masking, and 6) SARS-CoV-2 reinfection vs. no reinfection. We discuss the importance of multidisciplinary integration (health, social, and physical sciences), multilayered approaches to reducing risk (“Emmentaler cheese model”), harm reduction, smart masking, relaxation of interventions, and context-sensitive policymaking for COVID-19 response plans. We also address the challenges in understanding the broad clinical presentation of COVID-19, SARS-CoV-2 transmission, and SARS-CoV-2 reinfection. These key issues of science and public health policy have been presented as false dichotomies during the pandemic. However, they are hardly binary, simple, or uniform, and therefore should not be framed as polar extremes. We urge a nuanced understanding of the science and caution against black-or-white messaging, all-or-nothing guidance, and one-size-fits-all approaches. There is a need for meaningful public health communication and science-informed policies that recognize shades of gray, uncertainties, local context, and social determinants of health.

Peer Review reports

The coronavirus disease 2019 (COVID-19) pandemic has posed unparalleled challenges to society and upended life in a myriad of devastating ways. With over 180 million confirmed infection cases and over 3.9 million related deaths as of early July 2021 [ 1 ], severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to spread globally. COVID-19 has stretched healthcare system capacity, negatively impacted mental health, exacerbated socioeconomic disparities, and devastated economies. Scientists across disciplines, policymakers, and journalists continue to operate on “Pandemic Standard Time”—struggling to meaningfully advance science, policy, and communication in real time with rapidly emerging data, while countering the unprecedented “infodemic” Footnote 1 , polarization, and politicization in pandemic response plans [ 3 – 10 ]. The global community is not used to seeing rapidly emerging science and changing policy, and has therefore been desperate for immediate, unambiguous answers. Naturally, intolerance of uncertainty has driven some people to fill this void with deceptive narratives [ 11 , 12 ].

Misinformation and disinformation Footnote 2 come in endless guises and spread via different mechanisms, including campaigns of persistent inaccurate beliefs and falsehoods, deceptive messages, and engagement echo chambers Footnote 3 [ 13 , 14 ]. The pandemic has brought a paper tsunami with widespread misinterpretation of both peer-reviewed research and preprints, press releases without scrutinizable data, sensationalized media reporting, and endless conspiracy theories [ 5 , 11 , 15 , 16 ]. As a result, finding trustworthy sources of information and guidance on COVID-19 has been difficult for the public. Over the past months, logical fallacies and cognitive biases have relentlessly distracted from critical appraisal and transparent communication of the scientific evidence related to COVID-19 [ 17 ]. Confirmation bias, availability bias, motivated reasoning, the Dunning-Kruger effect, black-or-white fallacy (also known as false dilemma, false dichotomy, either/or fallacy, or false choice), straw man fallacy, ad hominem fallacy, appeal to emotion, appeal to ignorance, and appeal to authority fallacies have all run rampant across social media.

False dichotomies—statements erroneously posited as two simple, mutually exclusive options—have sparked hot debates stemming from different views on evaluating the content and sufficiency of the evidence on which to draw conclusions (Fig. 1 ). Opponents for either side of these conundrums see whatever data through the lens of their preconceptions, cherry-pick scientific research, and fit polarizing narratives with the perils of black-or-white messaging and reductionist frameworks. Their rigid views, fueled by misinformation, often polarize alongside the increasing certainty with which they are expressed [ 18 , 19 ]. Some academics and politicians navigating the public scrutiny of COVID-19 response have been concerned that communicating scientific uncertainty undermines trustworthiness [ 20 , 21 ].

figure 1

A false dichotomy is a logical fallacy that involves presenting two opposing facts, views, or options as though they were the only possibilities. The false dichotomy fallacy is often committed when someone thinks one of the two options is obviously true while the other is obviously false. In reality, many more facts, views, and options exist in between, which can be represented as a gradient of gray shades between the extremes of black and white. While reasoning in binaries may feel easier and reassuring, people unaware of false dichotomies distract from the fact that there are many alternatives

The COVID-19 pandemic has been riddled with false dichotomies, which have been used to shut down or polarize debates while oversimplifying complex issues and obfuscating the accompanying nuances. In this review, we aimed to deconstruct six common COVID-19-related false dichotomies (Fig. 2 ) by reviewing the evidence thoughtfully and thoroughly: 1) Health and lives vs. economy and livelihoods , 2) Indefinite lockdown vs. unlimited reopening , 3) Symptomatic vs. asymptomatic SARS-CoV-2 infection , 4) Droplet vs. aerosol transmission of SARS-CoV-2 , 5) Masks for all vs. no masking , and 6) SARS-CoV-2 reinfection vs. no reinfection . At least three trade-offs exist at the interface of science and policy related to this pandemic: clarity-complexity (simple messages vs. conveying uncertainty), speed-quality (timely responses vs. in-depth quality assessment), and data-assumption (data availability vs. required set of assumptions) [ 22 , 23 ]. Therefore, while exploring challenging and contentious topics, we make the case for a nuanced understanding of COVID-19 science, identify insights relevant to effective pandemic responses, and highlight important research gaps. We also provide examples that echo the importance of interdisciplinary integration, epistemic uncertainty in risk communication, and public health during pandemics [ 20 , 22 , 24 ].

figure 2

This infographic depicts the simplistic black-or-white framing and the scientific, political, and social polarization of the topics covered in this review: 1) Health and lives vs. economy and livelihoods, 2) Indefinite lockdown vs. unlimited reopening, 3) Symptomatic vs. asymptomatic SARS-CoV-2 infection, 4) Droplet vs. aerosol transmission of SARS-CoV-2, 5) Masks for all vs. no masking, and 6) SARS-CoV-2 reinfection vs. no reinfection

A summary of key recommendations and insights is provided in the Table 1 and a lay summary is provided in the Table 2 .

A comprehensive, narrative literature review of the health, social, and physical sciences was undertaken to tackle six COVID-19 dichotomies. These topics were chosen by the researchers as relevant to COVID-19 science, public health, and policy given the emerging polemics around them during 2020. Although we mention COVID-19 vaccination in several sections of this manuscript, it was not a main topic of our review given that initial versions of this manuscript were written and submitted before December 2020 (when the first real-world reports of COVID-19 vaccinations occurred). From database inception to June 3, 2021 (updated search), authors explored different databases (PubMed, Google Scholar) and preprint servers (medRxiv, bioRxiv, PsyArXiv, OSF Preprints) for all types of articles using the terms “public health,” “economy,” “lockdown,” “symptomatic,” “asymptomatic,” “presymptomatic,” “paucisymptomatic,” “severity,” “droplet,” “aerosol,” “airborne,” “mask,” “masking,” “face covering,” “reinfection,” “recrudescence,” and “immunity.” Various combinations of these terms were entered along with “COVID-19,” “SARS-CoV-2,” “2019-nCoV,” “coronavirus,” “false dichotomy,” “false dilemma,” “uncertainty,” and “risk communication.” Some authors shared known articles and gray literature otherwise not retrieved in the searches. Handsearching of articles’ bibliographies led to the identification of further studies. Because of the diverse and rapidly expanding COVID-19 research, preprints and gray literature were considered but interpreted with caution given their lack of peer-review. Included articles were mutually agreed upon by the authors. The team of authors included a mix of academics and scientists with diverse backgrounds (infectious diseases, epidemiology, virology, public health, anthropology), which allowed a science-driven and fine-grained discussion of the evidence. Insights and implications for public health were carefully analyzed.

False dichotomy 1: Health and lives vs. economy and livelihoods

COVID-19 response plans have often been framed in terms of a health-economy zero-sum thinking [ 25 ]. That is, public health strategies necessarily hurt a nation’s economic well-being and vice versa. The false dilemma about these two competing priorities has been extended to include civil health, for instance, the right to protest against measures such as societal lockdowns, and public health threats such as systemic racism and police brutality [ 54 , 66 – 69 ].

There is no such dichotomy between health and the economy or between saving lives and saving livelihoods as all these concepts are intimately intertwined [ 23 , 25 ]. The ongoing pandemic is both a public health and economic crisis with dreadful consequences on morbidity and mortality [ 26 , 70 ]. Globally, economic contraction and growth closely mirror increases and decreases in COVID-19 cases [ 70 ]. Appropriate public health strategies that reduce SARS-CoV-2 transmission also safeguard the economy since the toll of widespread illness in workers can lead to disability and death. Aggregate data have shown that many countries that suffered severe economic hardship performed worse in protecting their population’s health from COVID-19 over the past months [ 71 ].

However, the physical and mental health effects and the profound socioeconomic impact of COVID-19 and the related countermeasures must not be overlooked [ 8 , 27 , 72 ]. Health disparities driven by existing socioeconomic and racial/ethnic inequities are prevailing challenges during this pandemic [ 8 , 25 , 27 , 28 , 73 – 75 ]. Disadvantaged, rural, low-paid, and non-salaried individuals, blue-collar workers, informal workers, daily-wage earners, migrants, and people with mental health and addiction problems are more likely to be harmed by both the pandemic and the response. Healthcare and socioeconomic disparities differentially impact the capacity of vulnerable populations to engage in physical distancing responses [ 76 ].

Therefore, public health experts, economists, social scientists, and bioethicists must work jointly to assist governments in developing interventions that protect the overall societal well-being [ 8 , 23 , 27 ]. For example, governments should mitigate the wider impact of COVID-19 by considering universal healthcare coverage, basic income protection and payment freezes on rents and loans for individuals affected by lockdowns and interpersonal physical distancing measures, paid sick leave and paid quarantine leave for infected and exposed workers, stimulus payments for high-risk and essential Footnote 4 workers, and mental health support. The International Monetary Fund also highlights the importance of identifying and supporting workers in informal employment sectors [ 70 ]. A clause like “we are going into lockdown” should be followed by a second clause like “and this is how we are going to support you during this time” [ 77 ]. The COVID-19 pandemic has painfully revealed the importance of caring for vulnerable populations, ensuring food and medicine supply chains, keeping non-COVID-19-related healthcare services, generating employment, adapting businesses, and addressing children deprived of learning and subjected to psychological distress caused by the pandemic [ 8 , 27 ].

False dichotomy 2: Indefinite lockdown vs. unlimited reopening

Stringent public health measures vs. natural herd immunity.

Early in an infectious disease epidemic, public health responses mainly rest on our capacity to separate infectious, exposed, and susceptible individuals. Yet, inconsistencies in pandemic preparedness plans and delays in implementing robust testing and contact tracing prohibited reliance on the isolation of infectious individuals and quarantine of exposed individuals to bring SARS-CoV-2 under control [ 23 ]. Given the progression to community transmission (where numerous cases are not linkable to transmission chains or clusters), many governments enacted lockdowns Footnote 5 , stay-at-home orders, travel bans, curfews, and closing of workplaces, schools, and other community gathering spaces such as gyms and entertainment venues [ 25 ].

Such blunt measures were deployed by governments during times of unabated community transmission and high surges in cases [ 23 ]. Many public health experts viewed them as stopgap tools needed in unprepared regions with widespread virus transmission to restrict SARS-CoV-2 transmission chains during the first moments of the pandemic, while the test-trace-isolate infrastructure, personal protective equipment (PPE) supplies, and hospital capacity were scaled up and strengthened [ 23 , 78 – 81 ]. However, because of inconsistent messaging about the purpose of lockdowns and the uncertain duration of the pandemic and response, many people believed COVID-19 was no longer a threat when lockdowns were lifted [ 23 ].

Alternative approaches were proposed when the second wave emerged in many countries. In particular, the Great Barrington Declaration (GBD) signatories proposed a dangerous and impractical approach that relied on focused protection of “high-risk” individuals while allowing uncontrolled viral transmission among “low-risk” individuals [ 82 – 84 ]. They argued that such a strategy would eventually lead to natural herd immunity at the population level, but this only reflected a misunderstanding of virology and immunology principles and management of public health emergencies [ 85 – 87 ]. The GBD strategy turned out to be an illusory way to rush back to normality, which understandably gained community and government supporters as a result of public discontent over lockdowns and diminishing trust in public health agencies [ 82 – 84 ]. Their rhetoric stoked, if not created, a false choice between total lockdown and a wholesale return to pre-pandemic life [ 84 ].

The harmful effect of stringent public health measures

Many models designed to predict the benefits of public health interventions ignored the potential harms [ 8 ]. This occurred because the earliest research on COVID-19 predominantly focused on the immediate and direct consequences of interventions such as reducing SARS-CoV-2 transmission. Currently, a growing number of reports substantiate the socioeconomic and psychological impact of both the COVID-19 pandemic and response, in addition to competing health risks [ 8 , 27 , 88 ].

The unintended consequences of several stringent public health interventions are massive and risk turning one public health crisis into many others [ 8 , 23 , 27 , 89 ]. Stringent measures deeply aggravate hardship for the poor and those whose economy depends on daily informal work. Unfortunately, amid the pandemic, lockdowns and mobility restrictions were implemented globally and for extended periods, without appropriate communication to allow for public health preparedness. Furthermore, social, mental, and financial support to alleviate the negative impact of lockdowns was not provided to citizens in many countries. As a result, these unmitigated repercussions fueled calls and marches to demand the lift of lockdowns.

Adverse effects of stringent public health measures include financial downturn, unemployment, mental illness, child abuse, domestic violence, hunger, and disruption to education, child development, immunization programs, contraception, and family planning [ 8 , 27 , 89 – 95 ]. Discontinuation of clinical services and prevention efforts regarding chronic non-communicable diseases [ 96 , 97 ] and infectious diseases other than COVID-19 (e.g., HIV infection, tuberculosis, malaria) has been reported [ 88 , 98 , 99 ]. Because the current pandemic is risking decades of progress in other infectious diseases and existing public health threats, strengthening of healthcare systems and a reassessment of global health funding and policies are urgently needed [ 88 ].

Finding a balance between lockdowns and unlimited reopening

In the presence of widespread community transmission, regions reopening prematurely without a coordinated, robust plan will face COVID-19 resurgence. This can force societies to go back to general or targeted lockdowns after uncontrolled outbreaks, as repetitively happened in countries that underwent staggering rises in COVID-19 cases, hospitalizations, and deaths following unfettered reopening. Robust policies with continued monitoring, non-pharmaceutical interventions (NPIs), and plans to avert overwhelming healthcare systems are critical from the beginning of an epidemic to avoid catastrophic scenarios. Alert level systems, informed by the level of community transmission and impact of COVID-19, are useful tools for escalating or de-escalating restrictions based on their impact and the response goal.

Rather than posing an all-or-nothing dilemma, striking a balance between continuing indefinite shutdowns and returning to pre-COVID-19 normality is needed. A stepwise, cautious lifting of lockdowns and loosening of other restrictions that help economies and social life continue are possible through the implementation of multipronged NPIs with lesser economic, societal, and quality-of-life costs [ 8 , 29 , 30 , 80 ]. Tens of NPIs have been described in the pre-COVID-19 literature and have been reassessed during this pandemic as countries have tailored their response plans. Examples of NPIs are physical distancing, mask wearing (discussed in section 5), natural or mechanical ventilation of indoor spaces, limiting non-essential social contact, avoiding crowded indoor spaces, hand hygiene, respiratory etiquette Footnote 6 , avoiding touching the face, cleaning and disinfection of surfaces, air filtration, robust testing (with short turnaround times), rigorous contact tracing, isolation of infected individuals, quarantine of close contacts, mass gathering bans, travel restrictions (e.g., entry and exit restrictions, travel advice and warnings), temperature and health checks, staggered work shifts, rotational groups, telework initiatives, and redesign of living, teaching, and working environments to prevent crowding [ 30 – 32 , 100 , 101 ].

During 2020, several regional economies were able to progressively resume to varying extents and worked to overcome logistical hurdles and implement combinations of preventive measures. However, controlling the spread of SARS-CoV-2 has proven challenging. Since December 2020, when the first reports of COVID-19 vaccinations outside clinical trials were published [ 102 ], the world has gained hope and seen the tangible benefits of vaccination. COVID-19 vaccines are a ground-breaking achievement that will help to end the pandemic [ 33 ]. However, the world will require complementary NPIs as long as a large share of the population is not vaccinated. The current global situation of more transmissible genetic variants of SARS-CoV-2 has raised concerns, but the remarkably high effectiveness of available vaccines is encouraging ([ 33 , 103 ], Escandón K., Flocco G., Hodcroft E.B. et al., unpublished data). No effective SARS-CoV-2 antiviral is currently available.

Multilayered prevention and additive risk reduction

The additive nature of risk reduction poses challenges for science communication. Education on multilayered prevention and public-facing communication efforts are negatively impacted by false dichotomies that confuse, distract, or give the appearance that only certain layers of risk reduction are important. The Emmentaler Cheese Footnote 7 Respiratory Pandemic Defense Model, based on the “Swiss cheese model” for understanding system accidents and improving safety management in healthcare, engineering, and aviation fields [ 105 – 107 ], is useful to understand the importance of multilayered prevention in COVID-19 response through personal and shared public health interventions (Fig. 3 ) [ 34 , 108 – 111 ]. No single cheese slice or layer of defense (risk-reducing intervention) is sufficient and perfectly protective (100% effective), but a suite of personal and shared interventions forms a robust prevention strategy [ 30 , 31 , 101 ]. Importantly, there are systemic factors that may contribute toward either risk reduction or risk increase of SARS-CoV-2 transmission and infection, by favoring or undermining the uptake and compliance of strategies. For instance, while misinformation and socioeconomic inequities erode trust in public health and compliance with interventions, effective risk communication and harm reduction approaches promote awareness and sensible use of NPIs to mitigate both the risk of infection and pandemic fatigue. Of note, this model is not intended to explain the complex factors involved in SARS-CoV-2 transmission or suggest a hierarchy of effectiveness of the preventive measures. This limitation does not detract from its usefulness as a means to communicate multilayered prevention and additive risk reduction. Pandemic response plans rely on the healthcare infrastructure, technical expertise, and political will across countries and regions. The combination of measures deployed will therefore vary substantially depending on dedicated resources, community transmission levels, and a close examination of their costs and benefits. Measures may have varying degrees of effectiveness and different costs.

figure 3

The “Swiss cheese model” of accident causation (more accurately called Emmental or Emmentaler cheese model [ 104 ]) originated with James T. Reason and Rob Lee in the 1990s (and was potentially influenced by other researchers) [ 105 , 106 , 107 ]. As applied to COVID-19 [ 34 , 108 , 109 , 110 , 111 ], this model recognizes the additive success of using multiple preventive interventions to reduce the risk of SARS-CoV-2 infection. No single slice of cheese (public health strategy) is perfect or sufficient at preventing the spread of SARS-CoV-2. Each slice has holes (inherent weaknesses or limitations) with variable number, size, and location over circumstances or time, which may allow viral transmission. SARS-CoV-2 infection occurs when multiple holes happen to align at the same time permitting a trajectory of successful transmission. When several interventions are used together and consistently and properly, the weaknesses in any one of them should be offset by the strengths of another. The preventive interventions can be broken into personal and shared, although some interventions may be both. The order of the slices and holes in the illustration are not reflective of the degree of effectiveness of the interventions, given that the scenarios of transmission are variable and complex. The black rats eating the slices of cheese represent factors undermining prevention efforts while the extra cheese represents a source of factors and opportunities favoring prevention efforts. This infographic was designed for this manuscript and was inspired by previous illustrations by the Cleveland Clinic [ 108 ], Sketchplanations [ 109 ], and virologist Ian M. Mackay, who proposed the Swiss Cheese Respiratory Pandemic Defense [ 34 , 110 ]

Transmission dynamics and risk assessment

Transmission dynamics should inform policy decisions about risk mitigation strategies and recommendations for safer socializing and reopening [ 28 , 46 ]. Targeted policies should consider the scenarios where transmission is more likely. Contact tracing provides valuable information about transmission dynamics. SARS-CoV-2 infection risk depends on physical proximity, location, type of activity, and duration of contact [ 28 ], with transmission dominated by superspreading events (SSEs) or contexts Footnote 8 , crowded spaces, indoor venues, and unventilated places. There is solid evidence on the clustering and superspreading (overdispersion Footnote 9 ) potential of SARS-CoV-2, which suggests that a small part of cases (5%–29%) is responsible for the majority of transmission events (~80%) [ 112 – 114 , 116 , 117 ]. The transmission heterogeneity or superspreading of SARS-CoV-2 is both the Achilles’ heel and the cornerstone of COVID-19 control efforts [ 35 , 112 , 118 ].

Higher-risk scenarios include residential congregate settings (e.g., nursing homes, homeless shelters, correctional facilities, university dormitories), high-density workplaces (e.g., meat and poultry processing plants, warehouses, manufacturing and distribution companies), public transportation, family/friend/work gatherings in indoor settings, mass gatherings (especially indoors), entertainment and leisure venues, religious events, and any other unventilated places [ 35 – 37 , 113 , 119 , 120 ]. All these scenarios are relevant to risk communication and mitigation efforts. Conversely, low-risk settings and activities, such as outdoor and uncrowded environments where physical distancing and ventilation may be ensured, do not drive SARS-CoV-2 transmission in significant ways.

Education and consistent risk communication with the public are critical for an effective pandemic response. Public health agencies and policymakers can educate people about the spectrum of risk and how to differentiate between higher-risk and lower-risk activities [ 28 ]. A notable example of clear and effective public health messaging is that of Japan, consisting in avoiding the “3 Cs” driving transmission—closed spaces (with poor ventilation), crowded places, and close-contact settings (such as face-to-face conversations) [ 121 ]. On the other hand, misguided policies can undermine public trust and jeopardize engagement in effective public health measures. Inaccurate accounts of transmission can lead to harmful policies and may cause individuals to fixate on inefficient or unnecessary interventions [ 33 ]. Amid the pandemic, many outdoor activities and settings (e.g., parks, beaches, hiking trails, playgrounds, skiing spots, other outdoor recreational spaces) have been discouraged or even prohibited [ 122 – 126 ]. In 2020, it was common that some politicians and the media called out seemingly dangerous behavior by spotlighting people frolicking on beaches, picnicking in parks, or participating in protests [ 66 , 127 , 128 ]. Also, overcautious people picked some studies and media reports to warn against going outdoors and spark alarm about walkers, runners, and cyclists spreading the virus via a slipstream effect over long distances [ 129 – 132 ]. These claims were mainly based on studies with no virological considerations and limited environmental assumptions [ 129 , 130 ]. All these aspects greatly influence viral transmission (addressed in section 4).

Harm reduction and the low risk of outdoor transmission

Since long-term restrictive measures come with enormous collateral damage and real-world conditions lead individuals to take some risks, the way forward is to advocate a harm reduction approach instead of social abstinence-only policy [ 29 , 38 , 77 ]. Applied to COVID-19, harm reduction entails enhancing awareness about SARS-CoV-2 transmission and infection risk mitigation, self-assessment of risk related to personal activities, and engagement through alternatives of safer socializing. Although finding balance in the response plans is not an easy task, harm reduction is a sustainable and realistic strategy and a way of negotiating a middle ground. Allowing people to make their own compromises and informed judgments make harm reduction an ethically correct approach that enhances community engagement and trust [ 30 , 77 ]. In contrast, COVID-19 absolutism Footnote 10 is not a viable or reasonable strategy [ 133 ].

Scolding and moral outrage are counterproductive to the COVID-19 response and can perpetuate stigma. Casting shame and blame on people violating public health measures should be avoided [ 29 , 134 , 135 ]. Incentivized messaging works better than “pandemic shaming” and condescending messaging (e.g., #covidiots, #dontbestupid, #letthemdie) [ 77 , 134 – 136 ]. Effective risk communication and education campaigns are therefore central to harm reduction. Harm reduction strategies may also encourage infected individuals to self-isolate and their contacts to self-quarantine in order to prevent further transmission [ 28 ].

Outdoor activities are arguably one of the mainstays of COVID-19 harm reduction by supporting mental and physical welfare and alleviating the pandemic response fatigue Footnote 11 , while curtailing infection risk [ 29 , 33 , 38 , 39 , 122 , 123 , 137 ]. The costs of not encouraging outdoor activities should not be overlooked. Policies that prohibit outdoor activities Footnote 12 may result in the movement of behaviors that are objectively safe outdoors to less-safe indoor settings [ 29 , 134 ]. Outdoor activities are unlikely to drive SARS-CoV-2 transmission substantially because of the higher viral particle dispersion, reduced person-to-person contact, and external environmental factors [ 40 , 138 , 139 ]. The scarce instances of outdoor SARS-CoV-2 transmission suggest an extremely low risk of transmission [ 40 , 138 , 139 ]. Four studies have found that 0.03% [ 36 ], 0.11% [ 140 ], 0.87% [ 119 ], and 2.3% [ 37 ] of reported SARS-CoV-2 cases seem to have occurred in outdoor settings. One study reported that 3.7% of cases were acquired outdoors; however, the definition of indoor setting was poorly limited to mass accommodation and residential facilities, with all other categories defined as strict outdoor settings [ 141 ]. Other studies reported that 5.3% of SARS-CoV-2 cases were associated with outdoor environments or mixed environments (with indoor and outdoor components) [ 37 ], and 9.7% of cases were related to partly outdoor occupations (construction laborers and tour guides with 4.85 percentage points each) [ 142 ]. In a preprint study, both the odds of overall transmission and the odds of SSEs were much lower outdoors (18.7-fold and 32.6-fold, respectively) [ 143 ]. A study among attendees of an overnight camp provided little information about the risk of outdoors vs. indoors, but the fact that the outbreak was clustered by cabin assignments suggests a high likelihood of transmission in indoor spaces during overnight cabin stays rather than during outdoor activities during the day [ 144 ].

A systematic review on outdoor transmission reported finding <10% of SARS-CoV-2 cases occurring outdoors [ 138 ]. However, the real figure of outdoor SARS-CoV-2 infection proportion is certainly lower. In the study by Lan et al. [ 142 ], the cases in construction laborers and tour guides may have occurred in indoor locations. Likewise, in three publications based on a crowdsourced database led by the London School of Hygiene and Tropical Medicine [ 37 , 119 , 120 ], there may be an overestimation since construction workers could have been infected indoors (the most updated article is the one published by Lakha et al [ 37 ]). The unreviewed paper by Nishiura et al., though widely cited, warrants caution given the lack of descriptive detail and raw data [ 143 ]. Considering the studies cited here and the potential overestimation due to misclassification of setting, it seems likely that the risk of outdoor transmission is <1%. In summary, despite the high heterogeneity in the studies describing outdoor SARS-CoV-2 transmission (i.e., non-uniformity of outdoor definition, non-systematic testing of occupational groups, reporting bias, misclassification of outdoor exposure locations) and the difficulties in linking an infection to a specific exposure or transmission source, the existing evidence consistently highlights outdoor transmission as a negligible driver of the pandemic, compared with indoor transmission [ 40 , 138 , 139 ].

Mass gatherings Footnote 13 deserve discussion. The risk in mass gatherings is expected to come from unplanned, informal, unregulated, and unmitigated events or activities that lack consideration of risk mitigation measures [ 40 , 139 ]. Several factors influence transmission in these settings [ 40 , 139 , 146 , 147 ]: 1) the environment (i.e., outdoor or indoor), since it contributes ventilation; 2) the geographic scope of the event and the extent to which vulnerable or susceptible individuals may be present (e.g., local vs. international event, attendee ages); 3) event-specific behaviors that influence transmission (e.g., communal travel, indoor congregation in other venues, congregate accommodations, face-to-face vs. side-to-side arrangement, loud conversations, shouting, singing); 4) gathering size, density, duration, and attendee circulation; 5) preparedness to conduct rapid contact tracing in the event of an outbreak; and 6) the multilayered prevention approach adopted. In addition, the underlying transmission levels or infection rates in a community are likely to influence the impact of either permitting or prohibiting mass gatherings. As for outdoor gatherings, upon consideration of crowd density, size, duration, circulation, and preventive interventions, public health officials may balance and mitigate risk across different factors mentioned [ 40 , 139 ]. That is, an increase in one risk factor may be offset or mitigated by decreasing other risk factors. Therefore, all mass gatherings will not generate equal risks of SARS-CoV-2 transmission and will not need homogenous mitigations [ 148 ]. Since mass gatherings may have sociocultural, economic, physical, and mental health implications, it is critical to consider the societal needs. For instance, Black Lives Matter protests in the USA were illustrative of the trade-offs offered by harm reduction. No evidence supported a growth in COVID-19 cases following the protests [ 66 , 68 ], which may have been due to the outdoor environment and compensating behaviors such as the observed increase in stay-at-home and masking compliance during the protests.

The need for reassessing health policies in the name of safety

Successful COVID-19 experiences of some countries have encouraged others to incorporate new elements into their plans and reassess existing elements that may be causing harm or may be ineffective. As this pandemic is not over, it is necessary to constantly revisit policies in the name of safety, so that their benefits always outweigh the harms [ 33 ]. The negative impact of blanket measures such as shutdowns and school/workplace closures is expected to be worse in the poorest regions [ 8 , 27 , 149 ], making the quintessential case for interventions targeted to the local context rather than generalized closures. In general, a combination of context-sensitive measures should be favored over blanket measures.

One topic that has caused intense debate is the closure of schools. International organisms and public health advocates have warned about the negative impact on children’s learning, mental well-being, social support, nutrition, and safety [ 33 , 150 ]. School closures should be the last resort in the COVID-19 response that countries and states pick and rely on. Evidence has emerged regarding limited SARS-CoV-2 spread within schools when sufficient preventive measures are in place, which has encouraged school reopening initiatives [ 151 – 153 ]. Of note, in-person schooling plans in the setting of high community transmission must include well-implemented alternative school-based mitigation strategies to not risk accelerating the pandemic [ 153 , 154 ]. These considerations may allow schools to safely reopen and stay open.

Other interventions that should be de-emphasized given their limited or relatively low utility are excessive surface cleaning and disinfection, temperature checks (particularly with inaccurate techniques), and some travel-related measures [ 30 , 33 , 155 ].

Relaxation of NPIs in the context of robust vaccination

Increasing vaccination rollout followed by decreasing local infection rates may allow the progressive easing of restrictions [ 33 ]. Gradual relaxation of interventions is essential to gain and recover trust in public health. This must consider the local impact of guidance and social disparities in addition to metrics of vaccination status and COVID-19 deaths. For instance, the US Centers for Disease Control and Prevention (CDC) issued on March 8, 2021 a set of public health recommendations, where they acknowledged that fully vaccinated people (those with ≥2 weeks after receiving a full vaccination scheme) could visit other fully vaccinated people indoors without NPIs, visit with unvaccinated people at low risk for severe COVID-19 without NPIs, and refrain from testing and quarantine following a known exposure if asymptomatic [ 156 ]. Recently, in May 2021, these guidelines were updated to reflect the successful vaccination rollout and the subsequent drop in cases and deaths in the USA [ 157 , 158 ]. As of writing, the CDC is supporting that fully vaccinated people no longer wear a mask or physically distance in any setting in the country, except where required by local regulations and workplace guidance, and refrain from quarantine and testing following a known exposure, if asymptomatic, with some exceptions for specific settings. Thus far, the effects of such policy decisions are illustrative of positive reinforcement in the context of efficient vaccine rollout. Publicly available data suggest that lifting mask mandates can allow a continued decrease in cases while leading to an increase in vaccine shots [ 159 ]. Vaccines and the subsequent relaxation of NPIs are contexts where messaging hope (since it is grounded in reality) has proven its value.

Another example that has been overlooked is the possibility of relaxing visitor restrictions in hospitals, provided that visitors assess their own risk and take precautions (e.g., vaccination, use of PPE, hospital screening) [ 160 , 161 ]. Given the endless benefits of visitors in patient-centered care, some authors have called for more accommodating hospital policies with careful use of PPE and monitoring, even before COVID-19 vaccination was made available [ 160 ]. Currently, in places where vaccination rates are high, COVID-19 cases and deaths are decreasing and non-essential community indoor venues are open. In this context, keeping inflexible no-visitor policies in hospitals makes no sense [ 161 ].

Public support and the need for an explicit pandemic response goal

One of the biggest challenges in pandemic response for many countries has been the lack of a clearly articulated goal. In infectious disease response, the potential goals are control at acceptable levels, (local) elimination Footnote 14 , or (global) eradication Footnote 15 [ 162 ]. Few countries, including New Zealand, Taiwan, Australia, China, and Vietnam, have articulated a goal of elimination as their official pandemic policy [ 163 ]. This goal has spurred leadership to enact stringent and robust COVID-19 responses including quarantine, contact tracing, and travel restrictions, among other measures, and a clear target goal appears to have aided in buy-in from the public. As a result, some countries and regions have achieved elimination and resumed pre-pandemic life, with only intermittent response to imported cases needed.

However, “Zero COVID-19” Alliance, an initiative by vocal proponents of the goal of elimination, lists several inconsistent goals, for example aiming for zero cases, hospitalizations, and deaths, stopping the spread of SARS-CoV-2 regionally, and having a world without COVID-19 (i.e., eradication) [ 165 ]. Further, critiques of elimination goals point to several shared features of successful countries. In particular, many countries that have achieved elimination of COVID-19 are island nations that deployed early, widespread, and stringent mitigation strategies. Indeed, elimination of COVID-19 appears to require an optimal surveillance system and extreme measures and may not be feasible in countries where border control is more challenging [ 163 ].

Eradication of COVID-19 is unlikely. Only two infectious diseases have ever been eradicated (smallpox and the animal disease rinderpest) [ 162 ]. Without wide-scale coordination and consensus for eradication, elimination will continue to require intensive case surveillance, quarantine or testing of travelers, and intermittent reinstatement of control measures. Despite this, local and national governments can engage in dialogue about their COVID-19 goals [ 163 ]. When elimination is not the target, control of infection below acceptable levels is the main alternative. However, the level of infection that is deemed “acceptable” is not a scientific or objective fact—rather, it is a sociological and political objective. The public must be provided with information about the target levels of infection and allowed to weigh in on whether this level is acceptable to them in order to ensure acceptance of, and cooperation with, required restrictions and interventions.

In 2020, in the absence of vaccines, COVID-19 elimination was unrealistic for most countries. Nevertheless, COVID-19 elimination is now more feasible with approved vaccines. Vaccination can purposefully lower the threshold to achieve elimination by generating low incidence infection rates and high population immunity [ 163 , 164 ], without the need for stringent NPIs. Unfortunately, even with vaccines, elimination is an unrealistic goal for countries suffering from a lack of resources, political commitment, public engagement, and coordinated response plans. Vaccine inequity further complicates the situation.

False dichotomy 3: Symptomatic vs. asymptomatic SARS-CoV-2 infection

Since the beginning of the pandemic, there has been confusion and debate over the clinical presentation of COVID-19 and asymptomatic SARS-CoV-2 infection (ASI). It is necessary to look beyond readily observable symptomatic individuals and those completely asymptomatic yet presumed to be infected. Reviewing the terminology needed to differentiate infected individuals and the infection stages is therefore the right first step before diving into the complexities between the poles of this false dichotomy.

Terminology: asymptomatic, symptomatic, presymptomatic, postsymptomatic, and paucisymptomatic

Asymptomatic individuals experience no symptoms throughout the entire course of infection [ 41 ]. The remaining individuals, referred to as symptomatic (in its broad sense), initially demonstrate no symptoms during the incubation period Footnote 16 (presymptomatic stage), then develop symptoms (symptomatic stage), and later become symptomless again during convalescence (postsymptomatic stage). As illustrated in Fig. 4 , the terms presymptomatic, symptomatic (in its strict sense), and postsymptomatic refer to different stages of infection in the same infected individual rather than to different infected individuals. While classification into these three categories is only possible through retrospective and prospective symptom assessment, the stage is defined at the time of first positive test or diagnosis (i.e., presymptomatic individuals have not yet developed symptoms at the time of testing and postsymptomatic individuals experienced prior symptoms). Among individuals with active symptoms, paucisymptomatic (sometimes referred to as oligosymptomatic) individuals are regarded as those who experience mild or few symptoms attributable to the infection. A population-based study arbitrarily defined paucisymptomatic individuals as those having one or two COVID-19 symptoms (except for anosmia and ageusia) [ 167 ].

figure 4

There are two types of SARS-CoV-2-infected individuals: those that develop symptoms at some point (symptomatic in a broad sense, ~75%–84%) and those that never develop symptoms (asymptomatic, ~16%–25%). The former individuals undergo three stages of infection: presymptomatic (where viral RNA is detectable but there are no symptoms), symptomatic (in a strict sense), and postsymptomatic (symptoms are gone but viral RNA is still detectable). They are often referred to as presymptomatic, symptomatic, or postsymptomatic individuals. These stages have distinct implications for transmission. Since all SARS-CoV-2-infected individuals are initially symptomless, testing, follow-up, and a thorough symptom assessment are required to truly differentiate asymptomatic from presymptomatic, paucisymptomatic (individuals experiencing mild or few symptoms), and postsymptomatic infection

COVID-19 clinical presentation

SARS-CoV-2 infection can present with a broad spectrum of clinical manifestations and disease severity. COVID-19 symptoms and signs include fever, cough, fatigue, chemosensory dysfunction (i.e., anosmia/hyposmia and ageusia/hypogeusia/dysgeusia), dyspnea, headache, gastrointestinal symptoms, among others [ 168 , 169 ]. COVID-19 can be categorized into mild, moderate, severe, and critical [ 170 , 171 ]. COVID-19 is mild in most individuals, with no evidence of viral pneumonia or hypoxia and with symptoms that are not significant enough to seek medical attention [ 172 ]. Patients with moderate COVID-19 have evidence of non-severe pneumonia and therefore may present with dyspnea but not hypoxemia [ 172 ]. Severe COVID-19 indicates pneumonia in the presence of marked tachypnea, hypoxemia, and/or progression of lung infiltrates in chest imaging [ 170 , 171 ]. Patients with critical COVID-19 are those who progress to complications such as respiratory failure, shock, and multiple organ dysfunction, often accompanied by high mortality [ 170 , 171 ]. Few studies have estimated the proportions of COVID-19 across the entire spectrum of severity using the ordinal classification above. Among a cohort that included over 44,000 confirmed COVID-19 cases from China (individuals of all ages), 81% of patients developed mild or moderate COVID-19, 14% developed severe COVID-19, and 5% developed critical COVID-19 [ 173 ]. All fatal outcomes were consistently reported among critical cases. The case fatality rate was 2.3% (49% of critical cases).

Some SARS-CoV-2-infected individuals experience persistent symptoms following recovery of acute illness, which is frequently referred to as post-acute sequelae of COVID-19 (PASC) or “long COVID-19” [ 174 – 179 ]. Many features of PASC resemble chronic fatigue syndrome/myalgic encephalomyelitis [ 175 , 180 ]. The most common symptoms of PASC are fatigue, neuropsychiatric symptoms (e.g., “brain fog,” headache, sleep difficulties, attention disorder), hair loss, dyspnea, and persistent smell or taste impairment [ 174 , 175 , 179 ]. There are also rare reports of hyperinflammatory syndromes (e.g., multisystem inflammatory syndrome in children [MIS-C] and adults [MIS-A] [ 181 – 183 ]), potentially associated with cytokine storm/release syndrome [ 184 ].

The proportion of asymptomatic SARS-CoV-2-infected individuals

The true occurrence of ASI is difficult to evaluate. The percentage of truly asymptomatic SARS-CoV-2-infected individuals has been variably estimated from less than 1% to as high as 96% [ 41 , 185 , 186 ]. Earlier reviews and opinion pieces reported wide ASI ranges (1%–88%) [ 187 – 189 ]. Others concluded that the overall ASI was approximately 40%–45% [ 186 ] or even conjectured that rising trends (e.g., 81%–95%) of ASI in some populations were the result of mask wearing [ 190 – 192 ] (further discussed in section 5). However, several concerns with these studies may result in overestimation or underestimation of the true asymptomatic fraction [ 41 , 42 , 193 ].

Issues related to determining the true fraction of ASI stem from multiple factors. First, many studies reporting on ASI were cross-sectional surveys, often with convenience sampling and different testing eligibility criteria and settings, and were not designed to estimate the prevalence of ASI. Therefore, they are prone to significant selection biases. Second, the paucity of adequate follow-up hampers distinguishing between presymptomatic and asymptomatic individuals in many of these studies [ 41 ]. It is crucial to account for the development of symptoms not only at the time of virological testing since it is well established that symptoms can occur days after testing positive [ 43 , 44 , 194 ]. Based on the incubation period of SARS-CoV-2 [ 118 , 166 ], a follow-up of 14 days from the last possible exposure to an index case (or first positive test if exposure is unknown) is recommended to exclude most presymptomatic cases [ 41 ]. Also, if the timing of SARS-CoV-2 exposure is unknown, assessment of prior symptoms is recommended to identify postsymptomatic cases, given the potential for long-lasting positivity of quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) testing in upper respiratory tract specimens following symptom onset (for weeks or even months) [ 43 , 44 , 194 – 197 ]. For example, it was reported that 43% of residents in countrywide screening in Iceland [ 198 ], 76% of pregnant women in a labor and delivery ward [ 199 ], and 81% of passengers and crew in an Antarctic-bound cruise ship [ 200 ] were asymptomatic. Due to the cross-sectional nature of these studies, it is not clear what proportion of these individuals were presymptomatic or postsymptomatic. In contrast, a study in a skilled nursing facility reported 56% of residents initially asymptomatic at the time of SARS-CoV-2 testing, of whom 89% went on to develop symptoms within one week, resulting in only 6% as truly asymptomatic [ 201 ]. Similarly, in a SARS-CoV-2 outbreak at a refugee shelter, 80% of individuals were asymptomatic at the time of testing but only 12% were asymptomatic during the 14-day follow-up period [ 202 ].

Third, some studies reporting a high prevalence of ASI only evaluated a narrow range of symptoms [ 41 ], leading to information biases. This usually happened in early 2020 when smell and taste disturbances and gastrointestinal symptoms were not widely documented. Not only are symptoms subjective and variably ascertained by screening questionnaires or self-reported symptom tracking, but patients may also be unaware of atypical, mild, and prodromal symptoms, may not recall symptoms upon retrospective assessment (recall bias), or may recount symptoms caused by other conditions. For instance, a high prevalence of ASI (88%) was reported in a homeless shelter, but occupants were asked only about the presence of cough or shortness of breath, with optional reporting of other symptoms [ 203 ]. Similarly, the initial report from Vo’, Italy noted that 43% of positive cases were asymptomatic individuals; however, symptomatic individuals were narrowly categorized as those with fever, cough, or at least two minor symptoms among a predefined list [ 204 ]. Both an inadequate follow-up and information biases in estimating exposure and symptom onset times lead to misclassification of some presymptomatic, paucisymptomatic, and postsymptomatic individuals as asymptomatic, likely resulting in an overestimation of the ASI prevalence.

Fourth, ASI estimates from serosurveys with uncertain timing of suspected exposure and antibody testing, and coupled with insufficient retrospective symptom assessment deserve caution given concerns with recall bias and the duration of detectable antibodies [ 41 , 205 ]. Recall bias in serological studies may occur due to interviews or questionnaires gathering symptom information during a prior period, which might be particularly problematic with long or unspecified time windows. Antibodies are detectable in most individuals two to four weeks following symptom onset [ 206 – 208 ], hence positive IgG titers are out of the presymptomatic period and seropositivity results exclude recent infection. ASI percentages from serological studies have been variably reported. For example, serological studies have estimated an ASI fraction of 32% in England [ 209 ], 33% in Spain [ 167 ], 44% in hospital staff from Michigan, USA [ 205 , 210 ], and 90% in Argentina [ 211 ]. Although two nationwide serosurveys on antibody testing [ 167 , 209 ] were designed to achieve representative samples of community-dwelling individuals [ 212 ], their accuracy heavily relies on measurement-related factors (e.g., timing of testing, antibody test performance, retrospective symptom ascertainment), as discussed elsewhere [ 41 ]. Unlike serological tests, SARS-CoV-2 nucleic acid assays detect viral RNA and are useful for virological diagnosis and modeling transmission potential. Nevertheless, when better understood [ 213 ] and planned, seroprevalence studies may assist in identifying previously unrecognized infections and, alongside virological tests, allow more accurate estimates of the population-wide ASI prevalence rather than of the qRT-PCR-positive population [ 214 ]. Also, serial serological testing may help define antibody decay trends, which is useful to estimate ASI proportion in serological studies [ 41 ].

Fifth, confusing methodological definitions, different settings, and language barriers during international clinical assessment affect the generalizability of ASI estimates. Greater care and standardization with case definitions is justified to avoid misinterpretation of research findings, as occurred when a high rate of “undocumented infection” (86%)—apparently an admixture of ASIs, unreported symptomatic infections, and undiagnosed mild infections—was reported to be the source of 79% of documented cases [ 215 ]. This was misconstrued across scientific papers and social networks as ASI being responsible for the majority of SARS-CoV-2 infections [ 216 ]. Another unconventional and unnecessary term is “covert infection” [ 217 ], which was used in place of ASI in a systematic review [ 218 ]. Further, in a modeling study, researchers used the term “silent infections” to merge presymptomatic and asymptomatic infections [ 219 ]. Lastly, studies testing at a single time point or disregarding the time-changing sensitivity of qRT-PCR assays will rule out individuals with initial false-negative qRT-PCR results [ 220 – 222 ], thereby likely underestimating the ASI prevalence.

Of note, a well-defined cohort study of 47 SARS-CoV-2-infected individuals among 195 household contacts reported an ASI percentage at the time of testing of 17% (8/47) [ 185 ]. Five of the eight infected individuals were qRT-PCR negative at enrollment but positive during follow-up testing. Upon repeat qRT-PCR testing, ambispective granular symptom assessment, and 14-day follow-up of all participants, 13% (6/47) were classified as presymptomatic and 4% (2/47) were classified as postsymptomatic by date of sample collection, indicating that no individuals proceeded asymptomatically.

Several systematic reviews and meta-analyses addressing the conundrum of ASI have provided summary prevalence estimates from 16% to 25% [ 42 , 214 , 223 – 226 ]. Given the inclusion criteria used (clinical follow-up, quality of included studies, case definitions), these systematic reviews are more reliable and accurate figures than those from highly publicized narrative reviews [ 186 ] and opinion pieces [ 187 , 188 , 190 – 192 ]. Despite the scientific rigor of the articles cited above, generalizability is unclear and the wide prediction intervals of their pooled estimates reflect the considerable methodological and clinical heterogeneity among the studies included. Other systematic reviews with problematic inclusion criteria and definitions published much lower or higher estimates (e.g., 8% [ 218 ], 31% [ 227 ], 39% [ 228 ]) or did not meta-analyze data yet concluded that the proportion of ASI was “at least one third” [ 212 ] or was “not negligible in any population” [ 229 ].

The proportion of symptomatic SARS-CoV-2 individuals (in a broad sense)

Since all SARS-CoV-2-infected individuals are initially symptomless, the proportion of those with presymptomatic, symptomatic, postsymptomatic infection (the same individuals) can be indirectly estimated in the range of 75%–84% by subtracting higher-quality ASI proportions reported in available systematic reviews from the totality of infections [ 42 , 214 , 223 – 226 ].

Pooled estimates of the proportion of presymptomatic SARS-CoV-2 infection published in three systematic reviews are dissimilar (8% [ 218 ], 15% [ 228 ], 49% [ 223 ]). This raises several concerns. While pooled ASI proportions may be valid and useful when a systematic review meta-analyzes high-quality evidence, the case of presymptomatic infection is a different one. Meta-analyzing proportions of the stages of infection of the symptomatic individuals makes little sense not only because of the variable testing times, definitions, and follow-up in individual studies, but also because the presence of symptoms is not a fixed feature of infection. The pooled proportion of presymptomatic infection of an individual study usually reflects the specific moment of testing or study assessment (i.e., PCR testing) rather than exposure. As a result, the pooled proportion of presymptomatic infection might, at best, give an idea about how often infected individuals that will develop symptoms are symptomless by the date of testing across heterogeneous studies. Therefore, systematic reviews should instead analyze the methodological aspects of original studies and epidemiological parameters and timelines that influence both clinical presentation and transmission. Aggregate analyses of timelines detailing key events (e.g., exposure, symptom onset, changes in NPIs, contacts) and serial virological data are valuable to estimate infectiousness and transmission risk.

Differential transmission of symptomatic, presymptomatic, and asymptomatic infection

From a public health and clinical standpoint, the relevance of using the term “presymptomatic” in addition to “symptomatic” and “asymptomatic” lies in differential transmissibility features between infected individuals depending on symptom status and stage of infection. These features include secondary attack rates Footnote 17 (higher for symptomatic and presymptomatic individuals) [ 42 , 45 , 224 , 230 – 232 ], viral RNA shedding dynamics (longer viral RNA shedding and occasionally higher viral loads in symptomatic and presymptomatic individuals) [ 43 , 44 , 233 ], and modeling estimates of the contribution to transmission (higher proportions of SARS-CoV-2 infections are estimated to originate from presymptomatic and symptomatic individuals) [ 219 , 234 – 238 ]. While these findings support a higher transmission risk for symptomatic and presymptomatic individuals compared with asymptomatic individuals, the latter cannot be dismissed as inconsequential to SARS-CoV-2 transmission [ 239 , 240 ]. Symptom-based strategies (e.g., case detection and isolation, self-isolation) are necessary but insufficient given the difficulties in recognizing the onset of mild or atypical symptoms in addition to the risk of symptomless transmission. While vaccination rates progressively increase worldwide, multipronged preventive measures that do not depend on identifying symptoms (e.g., physical distancing, mask wearing, ventilation, hand hygiene) continue to be essential for controlling SARS-CoV-2 spread.

Accurate messaging and further research

Misclassification of infected individuals continues to cloud our understanding of COVID-19 and may impact policies to control SARS-CoV-2 transmission. In acknowledging the definitions reviewed in this section and the existing evidence on the proportions of infected individuals and their differential transmission risk, some claims in scientific articles and opinion pieces are misleading (e.g., “Most coronavirus cases are spread by people without symptoms,” “Asymptomatic persons are playing a major role in the transmission of SARS-CoV-2”) [ 241 , 242 ]. Examples of more accurate and informative statements are “Most individuals with SARS-CoV-2 infection experience symptoms during the course of infection,” “About one in five infected people are completely asymptomatic,” and “SARS-CoV-2 cases are substantially spread by infected people both when they have symptoms and when they do not.”

Further research that incorporates nuanced definitions and systematic methods will enable a wider understanding of factors potentially influencing SARS-CoV-2 transmission such as viral load and the presence and onset of symptoms. Despite important advances toward understanding SARS-CoV-2 transmission dynamics, estimating the contribution of transmission is tricky and specific scenarios of transmission are extremely complex. Many aspects remain uncertain including the dual role of social behavior and biological features on transmission, evidence of presymptomatic viral load peak from empiric studies, and viral RNA shedding dynamics and infectious timeline of individuals with ASI. New studies will have to conduct rigorous analyses considering the influence of increasing vaccination rates on the clinical presentation of COVID-19. Also, there is a need for carefully designed studies that document persistent symptoms after acute illness, help understand COVID-19 aftermath, and improve care interventions, quality of life, and return to usual health of COVID-19 survivors with lingering symptoms.

False dichotomy 4: Droplet vs. aerosol transmission of SARS-CoV-2

The long-standing dichotomy of droplets and aerosols.

The COVID-19 pandemic has reawakened the long-standing dichotomy of respiratory droplets and aerosols in terms of their size and transmission distance [ 47 , 243 ]. Droplets and aerosols are erroneously seen as categorical transmission modes instead of a continuum of respiratory particles influenced by particle size and density, emission composition, turbulence and direction of the exhaled jet plume, and interacting environmental conditions [ 48 , 244 ]. Larger droplets (traditionally defined as >5–10 μm in diameter) stay aloft for shorter periods of time relative to their size, settle on the ground within seconds to minutes because of gravitational force, and are transmitted over short distances (usually < 6 ft or 2 m), although airflow can propel them farther across a room. Small-particle aerosols or droplet nuclei (traditionally defined as <5 μm) generally evaporate and disperse faster than they fall, remain in the air for minutes to hours, and travel longer distances. This outdated distinction between droplets and aerosols has been revised by aerosol scientists arguing that the correct size threshold to differentiate these particles should be 100–200 μm [ 245 – 247 ]. “Aerosols,” a term commonly used as a shorthand for “aerosol particles,” are defined as a stable suspension of solid and/or liquid particles in air smaller than the above size cutoff, whereas droplets are defined as liquid particles larger than aerosols [ 247 ].

SARS-CoV-2 transmission cannot be separated into the earlier dichotomy of stationary droplets vs. suspended aerosols or the newer dichotomy airborne vs. non-airborne. Transmission patterns are on a continuum rather than dichotomous [ 48 ]. Although several issues need clarification and discussion to achieve scientific understanding and effective public communication, no debate exists as to whether respiratory particles of varying sizes can be generated from an individual. Both aerosol-generating behaviors (e.g., coughing, sneezing, speaking, singing, shouting, breathing) [ 248 – 250 ] and medical aerosol-generating procedures (AGPs) Footnote 18 [ 253 ] lead to the production of respiratory particles spanning a wide spectrum of sizes. To avoid dichotomization and better describe the behavior of respiratory particles, some researchers have referred to the continuum of aerosols and droplets of all sizes as a multiphase turbulent gas cloud (“puff”) of exhaled air [ 244 ].

The modes of transmission of SARS-CoV-2

Transmission of SARS-CoV-2 may occur via several biologically plausible routes and depends on multiple factors, including the infectious dose (or inoculum), virus viability, exposure distance and duration, environmental factors (temperature, humidity, precipitation, pH, airflow/ventilation, solar ultraviolet radiation, chemicals), and host factors (breathing rate, respiratory tract morphology, target tissues, receptor distribution, host barriers and immune responses) [ 49 , 254 – 256 ]. Transmission risk in specific settings is further influenced by existing infection prevention and control (IPC) practices and public health interventions [ 257 , 258 ].

As acknowledged by the CDC, SARS-CoV-2 transmission occurs through three non-exclusive modes of exposure to infectious respiratory fluids: 1) inhalation of infectious small fine droplets and aerosol particles, 2) deposition of these particles onto mucous membranes (nose, mouth, or eyes), and 3) by touching mucous membranes with hands contaminated by respiratory fluids or indirectly by touching inanimate surfaces with virus on them [ 50 ]. As transmission of infectious agents is complex and dependent on several factors, awareness of such distinctions is important for NPIs and public communication. Although the relative contribution of all transmission modes remains unquantified [ 49 ], substantial evidence exists in support of specific transmission modes. Close-contact respiratory transmission, via short-range (inhalable) aerosols and droplets, is the primary mode of SARS-CoV-2 transmission [ 48 , 49 ]. Direct contact (physical) transmission and indirect contact transmission (or fomite transmission) play a minor role in propagating SARS-CoV-2 [ 46 , 51 , 155 , 259 ]. Long-range aerosol transmission (traditionally known as airborne transmission) occurs situationally, under certain conditions such as prolonged exposure in enclosed spaces with inadequate ventilation [ 47 , 50 ]. SARS-CoV-2 infections through inhalation at distances greater than 6 ft are less likely to occur than at close distances. The CDC has also emphasized that transmission due to inhalation and mucosal deposition of virus is effectively mitigated by existing intervention recommendations [ 50 ], such as well-fitted masks, adequate ventilation, physical distancing, and avoidance of crowded indoor spaces. Other transmission routes (e.g., conjunctival, vertical, fecal-oral, zoonotic), though possible or suggested, are regarded as insignificant based on existing evidence [ 46 ].

Airborne transmission—taken in its traditional definition of long-distance and respirable aerosols—is not the dominant or exclusive route for SARS-CoV-2 transmission [ 48 , 49 ]. Conflicting and polarizing messages pertaining to SARS-CoV-2 transmission modes jeopardize pandemic response plans, resulting in public unwillingness to adhere to risk reduction practices. Exaggerating the frequency of a transmission route [ 260 ] prioritizes unnecessary IPC measures and social behaviors in hospital and community settings at the expense of effective interventions in place and undercuts public trust. Infectious disease transmission has important implications for deploying cost-effective IPC protocols and allocating resources to achieve the largest impact possible. Overstated evidence can lead to harmful policies. By amplifying findings from studies with methodological concerns and limited transferability of results [ 261 , 262 ], some academics and laypeople have advocated the use of filtering facepiece respirators (FFRs) in routine healthcare or even in community scenarios [ 263 – 266 ], despite evidence showing that FFRs may not be necessary in some settings to reduce transmission risk [ 267 ]. This has led to risk perception disparities and public confusion.

Epidemiological evidence

Epidemiological data (outbreak, cohort, and case-control studies) help determine SARS-CoV-2 transmission mechanisms in real-world conditions. Theoretical modeling, laboratory-based, and in silico studies are useful as complementary sources of knowledge but are not necessarily reflective of the frequency of a transmission mode and the real-life situations, especially if they do not consider SARS-CoV-2 infectivity or are simulated in vastly different scenarios.

Several arguments support transmission through close contact with the infectious source [ 48 , 50 , 52 ]. First, the basic reproduction number Footnote 19 (R 0 , 2–3) [ 268 , 269 ] and household secondary attack rates (generally 10%–20%) [ 230 – 232 ] for SARS-CoV-2 are compatible with predominant close contact transmission rather than long-range aerosol transmission [ 47 , 270 ]. Second, several observational reports of COVID-19 hospital cases and outbreaks have indicated that transmission-based precautions (TBPs) for routine care of patients generally work if instituted timely and consistently [ 48 , 257 , 271 – 284 ]. Hospital-acquired SARS-CoV-2 is rare in healthcare settings with robust IPC programs. The findings of some studies [ 285 , 286 ] reporting an increased risk for SARS-CoV-2 infection among healthcare workers (HCWs), even when wearing adequate PPE, compared to non-HCWs do not immediately translate into predominant long-range aerosol transmission, especially when there is little or no consideration of the variation in IPC practices and PPE types [ 48 ], definitions of compliance and consistent wearing, AGP care exposure, breakroom or changing room exposure [ 48 , 287 – 289 ], and community SARS-CoV-2 exposure of HCWs [ 290 ]. Medical masks have been demonstrated to reduce infectious titers of other respiratory viruses with similar transmission patterns [ 291 ]. Meta-analyses of clinical studies comparing medical masks with FFRs have reported no statistically significant difference in preventing respiratory viral infections (including those caused by seasonal/endemic coronaviruses and influenza) in HCWs [ 292 – 297 ]. The problem is that the evidence is heterogeneous and hindered by suboptimal PPE adherence and underpowered study designs. The need for higher-rated PPE should be calibrated to the degree of risk [ 298 ]. As many HCWs in clinical care (and potentially other essential workers) are at the highest risk for exposure due to proximity, duration, and infectiousness of patients [ 267 ], access to fit-tested FFRs is indicated for their safety. Medical masks reduce but do not eliminate aerosol exposure and therefore may offer incomplete protection for frontline HCWs and other HCWs that engage in near-range, face-to-face, sustained encounters with patients with known or suspected COVID-19, untested individuals, and/or individuals that are unable to wear masks [ 298 , 299 ]. The value of FFRs outside of these circumstances is likely marginal but more research is needed [ 298 ]. Third, community-based reports generally support the effectiveness of the existing TBPs (if consistently and adequately instituted) [ 300 – 309 ]. Accordingly, both the World Health Organization (WHO) and the CDC have reiterated that current recommendations are in general effective against both inhalation and mucosal deposition of respiratory particles [ 50 , 52 ].

Several SARS-CoV-2 outbreak studies have been published in different settings, including restaurants [ 310 , 311 ], call centers [ 312 ], choir rehearsals [ 313 , 314 ], indoor fitness and sports facilities [ 315 – 319 ], long-term care facilities [ 201 , 320 – 324 ], correctional facilities [ 325 ], malls [ 326 ], churches [ 327 , 328 ], flights [ 304 , 329 ], social gatherings [ 330 , 331 ], camps [ 144 ], ships [ 200 , 303 , 332 ], bus transportation [ 333 ], and acute care hospital settings [ 299 , 334 ]. Many of these outbreak studies have been often cited by other reviews as evidence of airborne transmission. However, long-range aerosol transmission is a plausible explanation in only some of these settings [ 48 ]. Other modes of transmission cannot be ruled out and may fit the particular transmission conditions. In general, published clusters associated with long-range aerosol transmission are singular events with preventable circumstances, such as prolonged duration of exposure, lapses in the use of PPE, increased exhalation, indoor settings, and poor ventilation.

Laboratory studies and modeling data

Different types of laboratory studies have been conducted in an attempt to elucidate SARS-CoV-2 transmission. Some laboratory studies (e.g., using a 3-jet Collison nebulizer) have shown that experimentally-generated SARS-CoV-2 aerosols may remain infectious for up to 3–16 hours [ 335 , 336 ]. Unfortunately, such studies under controlled laboratory conditions do not reflect physiological host processes and real-world environmental conditions related to viral transmission [ 270 , 337 ]. Respiratory particle transmission and viability over long distances are subject to changes in ambient temperature, relative humidity, airflow/ventilation, solar ultraviolet radiation (sunlight), and chemicals leading to evaporation, supersaturation, dilution, or inactivation [ 49 , 254 – 256 ]. Aerosol transmission, direct contact transmission, and fomite transmission have been experimentally demonstrated in multiple animal models [ 49 , 338 – 343 ]. Furthermore, studies in non-human primates, and confirmed in humans, demonstrate that infected individuals exhale infectious aerosols, but this is highly variable across individuals and activities [ 344 , 345 ].

Experimental, computational fluid dynamics simulation, and mathematical/numerical modeling studies have found that respiratory particles floating in the air can reach distances of 20–26 ft (6–8 m) or thereabouts [ 244 , 265 , 346 , 347 ]. However, this does not mean predominant long-range aerosol transmission of infectious viral particles. While respiratory particles have a great capacity to travel long distances or linger in the air for some time, transmission risk hinges greatly on how much infectious virus those particles contain and the conditions of the environment. These particles will diffuse and dilute in the surrounding air leading to progressively lower virus concentrations.

Droplet dispersion experiments (e.g., using laser light scattering) have shown that aerosols can travel for long distances [ 265 , 348 – 350 ]. However, these studies did not quantify infectious SARS-CoV-2 concentrations, which are likely substantially lower over long distances and under dynamic environmental conditions. Findings from Stadnytskyi et al. [ 349 ] relied on the independent action hypothesis, which states that each virion has an equal, nonzero probability of causing an infection (i.e., even a single virion can establish infection). This hypothesis remains scarcely tested and is unknown to be valid for humans and their infecting viruses including SARS-CoV-2 [ 270 , 349 ].

Many studies have looked for evidence of viral RNA in ambient air samples and ventilation systems of hospitals [ 351 – 385 ]. Some of these studies detected SARS-CoV-2 RNA in some air samples [ 351 – 374 ], but other studies did not [ 375 – 384 ]. Several of the qRT-PCR-positive studies were not successful in isolating viable SARS-CoV-2 [ 351 , 354 , 357 , 364 , 366 , 367 , 371 , 372 ], while others did not attempt to culture SARS-CoV-2 [ 355 , 356 , 358 – 363 , 365 , 368 – 370 , 373 , 374 ]. Two hospital-based studies have reported infectious SARS-CoV-2 in ambient air. The study by Santarpia et al. collected aerosol samples around six patients admitted into medical wards, characterized the size distribution of aerosol particles, and assessed the presence of infectious virus in different particle size ranges in the patient environment [ 352 ]. The authors demonstrated the presence of SARS-CoV-2 RNA and increases in viral RNA during cell culture of the virus from recovered aerosol samples, especially in particles with size < 1 μm. In another study, Lednicky et al. used an air sampling technology based on water vapor condensation to determine the presence of viable SARS-CoV-2 in hospital room air of two COVID-19 patients [ 353 ]. Viable SARS-CoV-2 was isolated from air samples collected 2 to 4.8 m away from the patients, with estimates ranging from 6 to 74 median tissue culture infectious dose (TCID 50 ) per L of air. It is yet unclear the extent to which these findings represent an unmitigated risk in healthcare settings where PPE and other TBPs are properly applied. Identification of SARS-CoV-2 RNA and viable SARS-CoV-2 in air samples from healthcare settings lend credence for aerosol transmission in these settings but does not provide straightforward information on its frequency as a transmission mode for SARS-CoV-2. Nor is a hospital setting, with robust ventilation, air filtration, and PPE, comparable to risk or frequency in the community [ 257 ]. This similarly applies to fomite transmission, which is not considered a major transmission mode despite numerous laboratory-based studies conducting environmental sampling and reporting SARS-CoV-2 surface contamination and stability [ 386 ]. Nuance is needed when examining the evidence of air sampling studies instead of calling the retrieval of infectious SARS-CoV-2 a “smoking gun” [ 387 ].

Some studies conducting community-based SARS-CoV-2 RNA detection in air samples have reported negative findings, including those from cruise ship cabins [ 388 ], quarantined households [ 389 ], residential areas [ 354 , 370 ], open public areas [ 354 , 368 ], and transportation [ 368 , 390 ]. In contrast, other studies have reported positive qRT-PCR-positive air samples from a variety of indoor or crowded public spaces [ 370 , 391 ] and transportation [ 391 , 392 ], with SARS-CoV-2 viability not assessed. Three additional studies assessing the presence of SARS-CoV-2 RNA in outdoor particulate matter (PM) in Italy and Spain found all air samples negative [ 393 – 395 ]. A modeling study estimated a very low average outdoor concentration of SARS-CoV-2 RNA (<1 RNA copy/m 3 ) in uncrowded outdoor public areas in Italy, even in the worst-case scenario [ 396 ]. Conversely, researchers of one Italian study found that 20 out of 34 PM 10 (PM with diameter < 10 μm) samples were qRT-PCR-positive [ 397 ]; however, concentrations of virus-laden particles were not examined and culture data were not provided. Although the implications of atmospheric pollutants on transmission remain elusive [ 53 , 398 ], several studies (mostly ecological) and commentaries arguing about an association between air pollution and SARS-CoV-2 airborne transmission and mortality [ 347 , 399 – 404 ] have sparked concern about PM acting as a carrier of SARS-CoV-2 and diffusing the virus in open environments. An ecological study about PM in several Italian provinces found a positive correlation between daily PM 10 exceedances and COVID-19 cases [ 403 ]. The authors of this study hypothesized that the growth and severity of cases in Milan could be attributed to airborne diffusion and a “boost effect on the viral infectivity corresponding to the peaks of PM.” They also illustrated the “airborne route of transmission as a ‘highway’ enhancing viral transmission over 8 m.” No scientific evidence suggests or supports such claims. Available air pollution studies point to correlation rather than causation (i.e., highly polluted areas in some countries are characterized by large populations and increased rates of human interaction, and lockdowns reduce both air pollution and SARS-CoV-2 spread) [ 53 , 398 ]. Furthermore, upon theoretical examination, the probability that atmospheric pre-existing PM scavenges virus aerosols is low [ 396 ]. Monitoring of SARS-CoV-2 RNA in outdoor PM is therefore unlikely to be an early suitable indicator of viral diffusion or pandemic recurrence [ 393 , 394 ]. Some scientists have also speculated that airborne pollen [ 405 ] and sea spray [ 131 , 132 ] may act as a modulating factor of SARS-2 infection and transmission, with only ecological data supporting an association for the former [ 406 ]. However, there is enormous potential for confounding due to several factors implicated in transmission of respiratory viruses, including well-known environmental factors such as ambient temperature. In addition, no evidence supports that pollen grains are carriers of SARS-CoV-2, much less does it provide information on their frequency and risk of transmission. A study of air samples collected in Germany and experiments to examine potential complexes between purified pollen of various taxa and SARS-CoV-2 reported negative findings—in terms of both viral RNA and virus-induced cytopathic effects [ 407 ]. While environmental exposome deserves further examination, evidence must be accurately communicated to avoid panic and misunderstandings.

In summary, a low level of air contamination has been demonstrated in both healthcare and non-healthcare settings thus far. The findings of the air sampling studies are related to the sampling methods and duration, storage and transferring conditions, the environmental setting, low viral concentrations, dilution effects, and ongoing IPC measures [ 408 , 409 ]. Further, pressing issues concerning virological testing warrant discussion. qRT-PCR cycle threshold (Ct) values have been increasingly used as informative proxies for probable infectivity [ 196 , 197 , 410 , 411 ]. However, viral nucleic acid detection by qRT-PCR-based assays does not equate to shedding of infectious, viable, culturable, or replication-competent virions [ 412 , 413 ]. Viral load and Ct values have limitations [ 222 , 414 , 415 ]; their correlation depends on the gene targets used, the nucleic acid extraction system, among other factors. Detectable viral RNA exceeds infectious viral clearance [ 43 , 44 , 194 – 197 ] likely because genomic and subgenomic RNA persists as residual viral fragments or is protected by cellular membranes, and degrades slowly after the immune system has neutralized or lysed virions [ 412 , 416 ]. Demonstrating virus amplification or cytopathic effect in cell culture, or virus quantification by plaque assays or TCID 50 endpoint dilution assays are needed to infer viral replication and infectious virus [ 417 ]. Therefore, these are better surrogates for assessing transmission competency, although the sensitivity of viral culture may be a concern as well [ 222 ]. Unfortunately, infectious titer assays must be conducted in biosafety level 3 (BSL-3) containment, so routine measurement of infectious SARS-CoV-2 in clinical settings cannot be done. Further methods to quantify infectiousness [ 415 ] and reproducible research with emerging technologies to sample air particles are needed.

Unknowns in SARS-CoV-2 transmission

There are virological and aerobiological unknowns of SARS-CoV-2 that are germane to elucidating transmission modes, including the minimum infectious dose, the size of particles with major relevance for transmission, and virus concentrations and viability in respiratory particles. In addition, several factors that influence transmission warrant study: particle emission and composition, particle size transformation and distribution over time, and environmental parameters (e.g., temperature, humidity, indoor/outdoor setting). High-quality research is needed to better understand these aspects and attempt to estimate the relative contribution and importance of the transmission routes of SARS-CoV-2. However, this is challenging because of the complexities in transmission [ 49 ], including the fact that respiratory particles containing infectious SARS-CoV-2 are highly variable in different individuals and with different activities [ 344 , 345 ].

The use of the term “airborne,” the lack of nuance, and inaccurate analogies

It has become clear that aerosol transmission is an important transmission mode. However, there is controversy about using the term “airborne” due to varied existing definitions, meanings, and implications [ 418 ], including the ordinary meaning of the word (carried in the air) and scientific conventions and specialized meanings referring to long-distance aerosol-based transmission.

While some scientists advocate the use of the term “airborne” as a simple term to use in risk communication with the public, the plain usage of this word when referring to SARS-CoV-2 transmission is technically reductionist and ambiguous. The flagrant use of the term “airborne” without providing nuance can be misinterpreted. For example, if the public wrongly believes that transmission occurs overwhelmingly from aerosols over an extended distance and time, they may reject guidance to wear medical masks or cloth face coverings (given their limited aerosol filtering efficiency in comparison with other facepieces), hoard FFRs, or feel that distancing precautions are futile. Likewise, if the public believes that the virus spreads extensively in the outdoor air and travels down blocks or across buildings, this may lead to potentially dangerous practices such as closing all windows in residential areas.

From a public health standpoint, the term “airborne” is not actionable on its own because it offers no clear guidance on how to curtail exposure risk. Simplistic messages and press article headlines, such as “The coronavirus is airborne,” "It is in the air,” and “Coastal breezes likely carry coronavirus” [ 131 , 132 , 419 – 422 ] require nuance to provide effective and accurate risk communication in public health and to avoid misunderstandings of viral transmission and airborne fearmongering. This has been exacerbated by scientific commentaries claiming with selective citations that airborne transmission is the predominant mode of SARS-CoV-2 transmission, without addressing terminology, practical implications, and critical aspects in public health risk communication and community engagement [ 260 , 423 ]. Miscommunication of transmission modes precludes harm reduction approaches (e.g., enjoying outdoor spaces such as beaches [ 132 ], and avoiding indoor gatherings) by failing to acknowledge that outdoor airborne transmission is low, particularly if the setting is uncrowded [ 40 , 138 , 139 ].

Inaccurate analogies have also been increasingly used. Cigarette smoke has been mentioned as a proxy for SARS-CoV-2 infection risk [ 216 ]. While this may meet the physical properties for aerosol scientists, analogies that intertwine sensory reception, such as smelling volatile organic compounds in smoke, can be misleading in terms of respiratory protection efficacy. The possibility to smell a vapor while wearing a fitted N95 FFR (or equivalent PPE) can mislead HCWs into thinking that their PPE is not effective.

Toward a multidisciplinary agreement on actionable terminology

Given the societal challenges of COVID-19, never has there been greater need for meaningful interdisciplinary dialogue. Agreement on actionable terminology that respects different fields is long overdue. The pandemic has underscored the continuum and spectrum that is viral transmission. Such complexities should be addressed with collaborative efforts to communicate in a way that meets the needs of all parties. Nuance and complexity can be understood by the public if communicated clearly and transparently. Public health messaging and risk communication should mention that respiratory pathogens may transmit over long distances via the air under specific conditions, while making clear recommendations about effective mitigation measures. Central to the use of accurate terminology is the risk assessment of indoor vs. outdoor spaces and banishing the thinking of viral transmission as miasma or an insidious trail containing endless infectious virions.

Rather than droplet vs. aerosol or airborne vs. non-airborne dichotomies, evolving terminology and science communication for respiratory pathogens should move toward reflecting the nuance of transmission and effective interventions [ 48 ]. Broadening the “airborne” definition to inhalable aerosol/droplet exposure or respiratory transmission allows new avenues to be explored and reconciles seemingly contradictory data and disciplines. Furthermore, discussing enhanced respiratory precautions and differences between long- and short-range, as well as risk in terms of types of exposure and activities can effectively inform subsequent public health interventions. As long-range aerosol transmission is situational, these circumstances can be explained through an increase in risk factors as dimmers rather than on/off switches. Both the WHO and the CDC have utilized this approach with communicating risk, with an emphasis on proximity, activity, environment, ventilation, NPIs, and vaccination status [ 32 , 50 , 52 , 55 , 424 ].

Bridging the interdisciplinary communication barriers and disagreements between the medical and engineering fields has proven complicated. Although academic disagreements may be valid and should not be met with hostility, narratives of misinformation and false dichotomies cause harm or do little to address the global needs for COVID-19 mitigation. There have been large-scale, continued attacks on those working in public health, which undermines public trust and is counterproductive to the pandemic response. Different disciplines should work together [ 425 ], instead of taking an adversarial position against public health agencies like the WHO and the CDC [ 426 , 427 ], which is decidedly not constructive.

In the end, the unresolved semantic dilemma warrants interdisciplinary efforts from the full range of experts, including medicine, epidemiology, occupational hygiene, engineering, and fluid physics, seeking a classification framework that recognizes both technical knowledge and practical implications in the context of public health and reconciles with real-life evidence without drawing inaccurate or unduly alarmist conclusions from available studies. Nuanced and transparent communication efforts, coming from those actively working to advance health and research amid the pandemic and facing the challenges of media representation of terminology, are valuable endeavors.

False dichotomy 5: Masks for all vs. no masking

Culture war and the false dichotomy of community mask wearing.

Preponderantly framed as a medical intervention in the past, face masks have become embedded as a social practice informed by expectations and norms amid the COVID-19 pandemic [ 56 , 428 ]. Masks have provoked a culture war and vigorous debates in many regions, with a volte-face in attitudes from mocking mask wearers earlier in the pandemic to shaming mask abstainers later [ 19 , 54 , 429 – 431 ].

On one side of the politically charged false dilemma about community masking, some “pro-mask” academics and armchair epidemiologists have hyped masks with overconfident slogans (e.g., “Just wear a mask, it’s common sense,” “The science behind masks is simple and clear,” “Masks increase rate of asymptomatic cases”) [ 18 , 57 , 190 ], stigmatizing terminology to refer to people not wearing masks (e.g., “deviants”) [ 428 ], and inaccurate analogies with parachutes and other accessories [ 432 – 434 ]. Also, some modeling/simulation studies, quasi-experimental studies, and ecological studies [ 280 , 435 – 446 ] were overinterpreted in social and mass media without due acknowledgment of their limitations, including confounding. With well-meaning but incendiary rhetoric [ 431 ], some mask proponents overstated the benefit of masks in preventing SARS-CoV-2 transmission and downplayed many considerations needed for community masking uptake and public trust. Likewise, existing evidence was misinterpreted to advocate further benefits of mask wearing related to reduced COVID-19 severity (or increased ASI rates), and protective immunity via reducing the viral inoculum (one of these papers was a preprint withdrawn by the authors) [ 190 – 192 , 447 – 449 ].

On the other hand, there have been two “ anti-mask” groups or counterpublics shaped by their hostile stance toward masking. One seems to ignore the need for and utility of complex systems methodologies, plausibility designs, and diverse evidence approaches [ 450 – 452 ] to study population-level interventions while staunchly upholding evidence-based medicine tenets (extended from biomedicine traditions and philosophies) and awaiting “definitive” randomized controlled trials (RCTs). The other has vociferously disparaged the use of “muzzles” or “face nappies” based on unwarranted or negligible physiological concerns (e.g., increased risk of hypercapnia, clinical worsening of infected individuals, increased risk of skin infections) [ 453 – 455 ], infringement on libertarian values [ 19 , 456 , 457 ], toxic masculinity [ 458 , 459 ], or plain mask denialism [ 460 , 461 ]. Unsurprisingly, deep-seated conspiracy theories, scientific illiteracy, strong political views, and counter-visualizations Footnote 20 have stoked the anti-mask sentiment of the latter group, aiming to overturn mask recommendations and mandates [ 19 , 462 ].

Setting up a binary choice between “masks for all” and no masking is overly simplistic. Further, reinforcing a view of “altruistic” vs. “selfish” people fosters a damaging binary [ 56 ]. Claims from eminent individuals polarized at either side of this false dichotomy (i.e., either “mask absolutists” or “mask abstainers”) have promoted a culture war. The public should be treated as stakeholders with legitimate input into mask debates, not just as adopters, resisters, or “deviants” that need to be persuaded or forced to wear masks [ 56 ].

The science of masks is not straightforward or simple

Masks—with their benefits and caveats [ 57 ]—are not a panacea or a hoax, nor are they mere symbols and commonsense interventions of the pandemic response [ 54 ]. There is merit in appraising different types of evidence on respiratory viruses and masks, particularly as this is the case of a complex public health intervention. Evidence on masks varies across study designs, settings, and populations; mask types and designs; mask-wearing purposes; and clinical and microbiological outcomes assessed. Medical masks and FFRs have been shown to prevent respiratory viral infections in healthcare settings [ 262 , 293 , 297 , 463 – 466 ]. In general, clinical studies comparing medical masks (also known as surgical or procedure masks) with FFRs have reported no statistically significant difference in preventing respiratory viral infections in HCWs [ 292 – 297 ]. As for community scenarios, before COVID-19, there had been evidence with mixed results for medical masks used by healthy and sick people in households, university residences, schools, and mass gatherings (the Hajj pilgrimage) but much less research on cloth face coverings (also known as cloth or fabric masks) to prevent onward transmission (source control from an infected person) and contracting infection (personal protection of an uninfected wearer) [ 463 , 467 – 469 ]. Researchers of the only existing RCT on cloth face coverings, carried out in 14 hospitals in Hanoi, Vietnam, initially cautioned against the use of cloth face coverings to protect against clinical respiratory illness, influenza-like illness, and laboratory-confirmed respiratory virus infection, compared with medical masks [ 470 ]. A post hoc analysis found that the risk of infection was doubled if cloth face coverings were self-washed by hand by the wearers rather than laundered in the hospital [ 471 ]. Face coverings laundered in the hospital were as protective as medical masks. The majority of existing healthcare and community studies have focused on medical masks and FFRs, and have examined clinical endpoints and influenza-related outcomes. Direct evidence of mask use related to infections caused by coronaviruses (not SARS-CoV-2) is relatively sparse [ 472 ]. Of note, the COVID-19 pandemic has prompted abundant research on SARS-CoV-2 and masks, which is discussed below.

Mask filters collect particles through a combination of mechanisms including inertial impaction, interception, diffusion, and electrostatic attraction [ 473 ]. Medical masks have higher and more variable particle penetration rates (~10%–70%) than N95 FFRs (or equivalent), which present low particle penetration rates (<5%) [ 474 – 478 ]. Several filtration studies of cloth face coverings have reported widely variable filtration efficiency and breathing resistance (breathability) estimates depending on the mask design and textile features (i.e., fabric microstructure, permeability, electrostatic properties, number of layers) [ 467 , 479 – 491 ]. Among cloth face coverings, multilayer non-valved masks made of hybrid, closely-woven fabrics show the best filtration efficiency and overall acceptable wearing comfort [ 55 , 58 , 492 – 494 ]. Facial fit, an aspect critical to minimize both outward and inward leakage around the facepiece edges and to improve filtration performance, has been increasingly studied. Several techniques have been suggested (e.g., use of mask fitters, nose wires, nylon hosier sleeves, rubber bands, or hair clips; knotting and tucking the ear loops; cloth mask placed over another mask) [ 477 , 478 , 486 , 495 ]. However, gaps in consistent communication with the public remain. Mechanistic evidence has demonstrated source control efficacy of medical masks in reducing influenza virus and human seasonal/endemic coronaviruses respiratory emissions from symptomatic individuals [ 291 , 496 , 497 ], as well as some protection against influenza virus afforded to the wearer [ 498 ]. Likewise, fluid dynamics simulation and experimental studies support the role of masks in limiting the spread of respiratory emissions [ 348 , 499 – 502 ].

As for direct evidence on SARS-CoV-2, Ueki et al. conducted SARS-CoV-2 experiments with different facepieces and two mannequin heads facing each other to simulate source control and personal protection [ 503 ]. Medical masks and cloth face coverings were 57%–58% effective in protecting others and 37%–50% in protecting the wearer. N95 FFRs performed better with 86%–90% source control efficacy and 96%–99% personal protection efficacy. However, since variations in mask efficacy can be largely explained by the context of SARS-CoV-2 transmission (level of infection probability and virus abundance), medical masks and well-designed face coverings should be effective under virus-limited situations [ 267 ].

Several COVID-19 observational studies across diverse community scenarios [ 300 – 309 ] have suggested a benefit from masks in mitigating the transmission of SARS-CoV-2. On the other hand, there are the RCTs, which are presumed to provide the highest quality data. However, RCTs can hardly capture the complexities related to viral transmission and public health interventions [ 452 ]. Furthermore, large-scale mask RCTs related to SARS-CoV-2 are difficult to conduct given practical and ethical issues (e.g., involving no-mask controls raises an ethical dilemma regarding the principle of equipoise) and the existence of alternative types of evidence. Yet, two community-based RCTs have been conducted during the COVID-19 pandemic. One is the RCT DANMASK-19 that recruited 6,024 Danish citizens to evaluate the effect of medical masks recommendation in protecting against SARS-CoV-2 infection. This study found a non-statistically significant reduction in infection in the mask group vs. the non-mask group [ 504 ] (odds ratio 0.82, 95% confidence interval [0.54–1.23]). While medical use in this study led to a ~20% personal protection from incident SARS-CoV-2 infection, the study sample size was not enough to determine statistical significance. Because of methodological limitations of this study in addition to being underpowered (e.g., individual-level randomization, low mask adherence, serological diagnosis) [ 505 , 506 ], the findings do not disprove the effectiveness of community masking. The results of this study, however, may reflect the personal protective effect (not source control) of a mask recommendation in Denmark at the time (when the community incidence of infection was modest). The other mask RCT is a yet unpublished study conducted in Guinea-Bissau [ 507 ]. This cluster-RCT (which thus allows the assessment of source control) will complete enrollment of around 40,000 participants by August 2021. Of note, this community-based study aims to assess the effect of wearing locally-sewed cloth face coverings on COVID-19 severity and mortality. This study’s outcome is clinical and not based on tests (personal communication). Although it may be able to provide some clarity on the science of cloth face coverings, this study raises ethical concerns. The choice to conduct an RCT with a control group not provided with masks more than a year into a pandemic where other types of evidence suggest their effectiveness deserves scrutiny. Furthermore, while the study protocol was designed with Danish and Bissau-Guinean researchers, conducting this trial in Africa rather than Europe or North America raises potential issues of medical racism and colonialism.

Whereas observational epidemiological studies are likely to overestimate masks' effects due to residual confounding, experimental epidemiological studies are likely to underestimate effect sizes due to both suboptimal adherence in the intervention group and contamination (mask wearing) in the control group [ 508 ]. Therefore, the real effect size is likely between the estimates seen in those two types of study, with the maximum benefit of masking potentially resulting from the combination of source control and personal protection. Also, laboratory experiments in animal models—with their inherent limitations—have provided evidence on the efficacy of masks in preventing SARS-CoV-2 transmission [ 340 ].

While efficacy (performance in controlled or ideal conditions) and effectiveness (performance in usual or real-world conditions) are not synonymous [ 450 , 509 ], a large consensus and a growing body of literature have moved forward the uptake of community masking as part of comprehensive NPI bundles or “policy packages” aimed at preventing infections caused by respiratory viruses including SARS-CoV-2 [ 55 , 58 , 261 , 262 , 295 , 464 , 508 , 510 – 516 ]. Importantly, a fact undergirding community mask wearing during the pandemic is the risk of transmission, not only from symptomatic individuals, but also from presymptomatic and asymptomatic individuals (discussed in section 3). All in all, the intricate evidence base on the efficacy and effectiveness of masks explains the confusing messaging by public health officials about masks throughout 2020 and why mask policies flipped as scientists gathered data [ 23 , 31 , 55 , 58 , 512 ].

Alternatives to medical masks and cloth face coverings have been sought. In the face of limited data, face shields or visors have been suggested to provide some advantages over face masks in terms of eye protection, frontward airflow protection, no hand-to-face contact, breathability, full-face visibility, reuse, and disinfection [ 261 , 517 – 520 ]. However, variable design (shape, materials) of face shields and upward, downward, and sideways leakage jets from the edges, seams, and joints are major issues [ 482 , 500 , 521 – 523 ]. Face shields are therefore considered to provide a level of eye protection only [ 55 , 424 ]. The performance of clear masks and modified face shields remains largely untested. Also, although masks and FFRs with exhalation valves may ease breathing, they are discouraged for source control since the valves bypass the filtration function for exhaled air by the wearer [ 55 , 424 ].

Policymaking about masks and issues with compliance and mandates in the community

Many countries and regions with community-based transmission of SARS-CoV-2 have recommended or mandated the use of commercial or homemade cloth face coverings or medical masks to slow down the impact of viral spread [ 57 ]. This was particularly reasonable when population exposure increased as lockdowns ended. From a public health standpoint, mask advocacy has led to reflections over the way policies have been developed and communicated [ 54 , 524 ], and over the ideological distinction between applied and academic epidemiology [ 23 ]. Some scientists and academics have invoked the precautionary principle Footnote 21 , not only to advocate changes in TBPs and to guide public health policies in general [ 263 – 265 , 419 , 529 , 530 ], but also to argue the case for universal masking [ 432 , 439 , 531 – 533 ]. If the benefits of masks are to be considered (i.e., reduction of respiratory infectious disease transmission, mutual protection, positive prosocial signaling), potential downsides should not be utterly disregarded [ 55 , 59 ]. The latter include shortage of medical masks and FFRs for HCWs [ 534 , 535 ], cross-contamination due to inappropriate mask wearing [ 536 , 537 ], risk compensation or complacency toward other preventive measures (evidence in favor [ 538 – 540 ], evidence against [ 541 – 547 ]), psychosocial effects (e.g., threats to autonomy, psychological relatedness, competence) [ 455 , 548 , 549 ], communication and learning difficulties [ 518 , 550 – 555 ], physiological effects (e.g., subjective breathing discomfort or difficulties Footnote 22 , skin problems, headache, ocular dryness and irritation; these effects are more likely if there is a related predisposing condition) [ 454 , 455 , 556 , 562 – 565 ], and environmental pollution from mask waste [ 566 – 569 ]. Of note, these lingering concerns are not reasons to refrain from community masking (using medical masks or face cloth coverings) but are opportunities to maximize the benefits of masking, improve mask designs, and sharpen public health policies and messaging. The benefits of wearing masks outweigh the potential harms¸ especially when there exists widespread community transmission of SARS-CoV-2.

In addition to three essential mask parameters (filtration, fit, breathability), proper and consistent wearing of masks influences their effectiveness [ 55 , 57 ]. Training and guidance on correctness and consistency of mask usage are therefore crucial. Improper donning and doffing, usage of ill-fitting masks, and inconsistent mask usage point out challenges in scientific communication, health education, policy implementation, community outreach, and surveillance [ 57 ]. Mask adherence is contingent on aspects beyond mere “discipline”: knowledge about the virus, risk perception, social acceptability of masks, perceived efficacy, trust in government and health agencies, public engagement with science, health literacy, messaging from various sectors, past experiences with masking (e.g., for other respiratory virus epidemics or PM air pollution), mask comfort, consumer appeal, degree of enforcement by public authorities, accessibility (no supply issues), and affordability (no resource constraints) [ 4 , 57 , 59 , 469 , 570 – 572 ]. The psychological effects of masks are culturally framed and shape acceptance and adherence [ 54 , 510 ]. Mask policies aimed at fostering uptake should reflect the complex and contested sociocultural meanings and implications of mask wearing [ 56 , 428 ]. Studies examining sociocultural and psychological factors underlying public masking amid the COVID-19 pandemic are therefore vital to identifying motivators, barriers, and disparities, and formulating behavior change strategies that encourage and sustain appropriate mask wearing [ 469 , 550 , 572 – 581 ]. A study found that inducing empathy for people most vulnerable to SARS-CoV-2 promoted the motivation to adhere to physical distancing and mask wearing, whereas simply providing information about the importance of these measures did not [ 580 ]. A study exploring perceptions of face coverings via focus groups found that the most prevalent motivation was to protect or respect others, while barriers included discomfort, misinformation, and autonomy perceptions [ 574 ].

Another concern of masking is that of being compulsory and generalized. Haphazard compliance with mask recommendations amid a pandemic may justify mask laws. Behavioral experimental evidence showed that mask wearing signals prosocial concerns and may reflect a social contract where voluntary policies can trigger insufficient compliance, intensify stigmatization, and be perceived as less fair as opposed to mandatory policies [ 547 ]. However, issuance of blanket laws and punitive enforcement involves a trade-off with personal freedom. This might be counterproductive by further politicizing mask wearing, deepening structural inequalities, triggering active resistance and violence, and eroding public trust, particularly in regions with zero or little SARS-CoV-2 transmission [ 54 , 582 ]. For the same reasons, mandating masks in circumstances that provide marginal benefit such as outdoor spaces is inconvenient. Therefore, mask mandates—targeting specific settings and situations—should only be issued upon careful analysis of the legal challenges and local implications. Governments enforcing population-level masking should ensure the availability of masks and develop plans for free provision of masks to populations that might experience barriers to access [ 31 ]. For instance, public service providers could be mandated to have a stock of masks and educational aids for users, and private businesses could offer masks to customers out of enlightened self-interest.

Duckworth and colleagues outlined three sensible steps during the transition toward acceptance of community mask wearing [ 583 ]: 1) “from effortful to easy,” 2) “from unclear to understood,” and 3) “from unconventional to expected.” Such an approach relies on education and effective public health communication. Permanent education campaigns and harm reduction-based approaches from both the government and the public are preferred over purely coercive approaches and patronizing exchanges (e.g., shaming, excessive fines, imprisonment, violence, criminalization) to attain the desired results regarding mask wearing and avert social divides [ 54 , 57 , 583 , 584 ]. Lamentably, there are countless examples of the latter approaches, which are unlikely to foster masking and end the mask wars [ 54 ]. In public settings, penalties for not wearing a mask—if not limited to restricting access to a service—should not be excessive or unfair.

Any mask policy (and policy in general) must engage with the potential for inequality and social exclusion [ 56 ]. There is a need to address the impact of mask policies on vulnerable and marginalized groups, including D/deaf, hard-of-hearing, or visually-impaired people who substantially rely on lip reading, facial expressions, or unmuffled speech for communication [ 550 , 551 , 554 , 555 ]; racial groups being asked to tip their masks, harassed for concealing their face, or disproportionately penalized for not wearing masks [ 548 , 549 ]; and rural and poor populations without access to government information channels and online health warnings [ 74 ]. The absence of tailored policies risks these individuals becoming isolated, neglected, or stigmatized.

Smart masking, not universal masking, in the community

Publications that advocated universal masking for the public [ 432 , 439 , 442 , 446 , 531 – 533 , 585 , 586 ] omitted nuances regarding viral transmission dynamics, risk communication, and sustainability. Also, there was limited consideration of social sciences aspects, including how mask policies might play out in practice [ 54 , 56 ].

Masks have become normalized during the COVID-19 pandemic, and therefore the quandary of yes/no has been replaced with a debate about who, where, when, how, and what type of mask should be worn [ 55 ]. Aligned with the WHO risk-based guidance on masks (first issued on 5 June 2020) [ 55 ], a smart masking approach seems more appropriate than universal masking in community settings. The term “universal” entails all persons, places, and times, but some exemptions for masking are legitimate and reasonable because of particular benefit-risk assessments [ 54 , 587 ]. Mask exceptions should not be seen as symbolic rejections of the pandemic [ 54 ].

For instance, some individuals are unable or contraindicated to wear a mask (e.g., people with some breathing difficulties, intellectual disability, psychological distress, hearing loss) [ 518 , 551 , 555 , 588 ], and masking of children may prove challenging [ 589 – 591 ]. Clear masks and face shields have been discussed in the literature as potential alternatives for these individuals. If face shields are worn in the context of mask non-availability or difficulties, the wearer should ensure proper design to cover the sides to reduce leakage [ 55 , 424 ]. The benefits of wearing masks in children to prevent SARS-CoV-2 transmission should be weighed against potential harms associated with wearing masks, including social, communication, and developmental concerns, feasibility, and discomfort [ 591 ].

Furthermore, not all settings and activities allow mask wearing or confer the same risk of SARS-CoV-2 infection (discussed in section 2). The case for mask wearing is strongest in higher-risk scenarios such as crowded spaces, indoor venues, and unventilated places [ 55 ]. The case for mask wearing is weakest in marginal-risk scenarios such as outdoor and uncrowded environments where physical distancing and ventilation may be ensured (e.g., people engaging in outdoor activities, people driving alone). Additional exemptions from mask wearing include those scenarios where the mask would interfere with a particular activity or occupation (e.g., people eating, performers who require clear enunciation or being recorded, high-intensity or professional athletes). Since households may represent scenarios where routine appropriate masking is impractical for members, the case for mask wearing in households is strongest when non-household members are visiting or when a household member (who lives with other people) is infected or has been potentially exposed to SARS-CoV-2 because of a recent potential exposure (e.g., occupational exposure, crowded settings, travel) [ 31 , 55 ]. Mask policies directed toward high-risk settings, and not toward low-risk activities, are expected to foster mask adherence and acceptance and decrease mask-related discomfort and fatigue [ 54 , 59 , 570 ].

Acknowledging uncertainty and countering misstatements

Some uncertainties still exist regarding the wearing of face masks and coverings as a measure to prevent or mitigate SARS-CoV-2 transmission. There are COVID-19 research opportunities to obtain direct and actionable evidence on the effectiveness of specific cloth face covering designs in community scenarios, extended use and reuse of cloth face coverings, the impact of diverse approaches to mask adoption, alternatives that are more comfortable and more environmentally friendly, downsides of masking, additive effectiveness of cloth face coverings and face shields in the community, and attitudes, beliefs, and behaviors toward masking in the long term [ 57 , 59 , 510 , 517 , 524 ].

The evidence around the relationship between mask wearing, SARS-CoV-2 inoculum, COVID-19 severity, and immunity has been poorly addressed and misrepresented in several viewpoint articles and scientific opinions [ 191 , 192 , 448 , 449 ]. The hypothesis that mask wearing reduces COVID-19 severity, increases ASIs, and promotes immunity (“variolation”) has been challenged [ 60 , 205 , 592 , 593 ]. As of writing, two comprehensive reviews by our group on the topic are undergoing peer-review and will be soon published (personal communication). Overstating the effectiveness of masks or the existence of benefits additional to curbing viral transmission may lead to false expectations and increased exposure to high-risk places, social gatherings, and leisure activities (in the absence of full vaccination), which in turn may end up undermining trust in pandemic response efforts when people, exhausted from the pandemic and the response, realize masks are not infallible, severe cases still occur, and the pandemic has not fizzled out.

A key aspect of mask advocacy is accurate messaging, which includes acknowledging the limited utility of mask wearing as a single intervention and cautioning against it as a sufficient alternative to a multilayered use of other NPIs, including physical distancing, ventilation, and limiting time in crowded spaces [ 55 , 57 , 424 ]. The main arguments should be based on scientific evidence rather than on moralistic stances and virtue signaling [ 54 ]. It is monumentally frustrating that academics both supporting masks and calling for well-crafted messages, nuanced (not universal) guidance, and further evidence have been misrepresented as anti-mask and accused of flagrant disregard for human lives by some universal masking advocates. The palpable sense of urgency in the COVID-19 pandemic requires a dispassionate discussion and weighing of benefits, risks, and uncertainties along with swift data-driven decision-making that accounts for the cases for and against public health interventions [ 8 , 17 , 524 , 594 , 595 ].

False dichotomy 6: SARS-CoV-2 reinfection vs. no reinfection

In April 2020, the Korea Disease Control and Prevention Agency (KDCA) investigated 116 patients previously infected with SARS-CoV-2 who tested positive by qRT-PCR upon having met discharge criteria, including negative diagnostic tests [ 596 , 597 ]. This sparked intense public concern in regard to whether these observations indicated scant or absent protection from SARS-CoV-2 reinfection—defined as the subsequent infection of a host with the same microorganism. Fears of continual cycles of reinfection owing to weak stimulation of adaptive immune responses followed this publication. It was also highly plausible that the positive samples were due to the performance limitations of qRT-PCR diagnostics. The debate around SARS-CoV-2 reinfection has two opposing views: 1) infection and recovery do not confer immunity, which results in the potential for cyclical reinfection; and 2) infection results in protective immunity that removes any possibility for reinfection. However, the situation is inherently more complicated. To address the SARS-CoV-2 reinfection conundrum, it is necessary to revisit how long and how well the immune responses are protective against SARS-CoV-2 [ 598 ], and differentiate reinfection, re-detection, and recrudescence.

Reinfection and natural immunity

The question of potential fading immunity and reinfection has been present since the beginning of the pandemic. Much of the concern arouse because infections by coronaviruses such as HCoV-NL63, HCoV-229E, HCoV-OC43, and HCoV-HKU1 generally confer short-lasting protective immunity (6 to 12 months) [ 599 ]. In addition, past investigations of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) epidemics have suggested that IgG antibodies are detectable up to nearly two years and one year, respectively [ 600 ]. Early analysis of antibody responses from convalescent COVID-19 individuals suggested that neutralizing antibody levels were detected following symptom onset and remained elevated during the two-week follow-up period [ 601 ]. Also, a population study in Iceland showed that antibodies against SARS-CoV-2 did not decline within four months after diagnosis [ 602 ]. Reassuring data have accrued since, with several works supporting humoral immune responses to SARS-CoV-2 for at least 5–7 months [ 603 , 604 ] and immunological memory (especially T-cell mediated) for at least 6–8 months [ 605 ]. Epidemiological analyses have reported natural immunity protection from reinfection for at least 6–12 months [ 61 , 64 , 604 , 606 , 607 ]. Protection could go beyond these estimates because of the complexity and robustness of immune responses, though it is also acknowledged that the induction and durability of immune responses—both humoral and cellular—are heterogenous across individuals and may be shorter in some [ 608 ].

As an uncommon feature of SARS-CoV-2, reinfections are expected when immunity wanes or pathogen’s antigenicity evolves leading to immune evasion. The first confirmed case of SARS-CoV-2 reinfection in the USA was a 25-year-old male patient who was reinfected nearly two months after his first positive test, with the two symptomatic infection episodes separated by two negative nucleic acid tests at different time points [ 609 ]. Although the patient tested positive for IgG and IgM antibodies upon reinfection, antibody testing was not conducted after the first infection. Next-generation viral genome sequencing showed that the sequence variability between the two virus isolates belonging to Nextstrain clade 20C was too great to be explained by evolution within the patient alone. Likewise, initial reports of SARS-CoV-2 reinfections confirmed by viral genome sequences were identified in other individuals from the USA [ 610 , 611 ], Hong Kong [ 612 ], the Netherlands [ 613 ], South Korea [ 614 ], Belgium [ 615 ], Ecuador [ 616 ], India [ 617 , 618 ], Qatar [ 619 ], Brazil [ 620 – 622 ], United Kingdom [ 623 , 624 ], South Africa [ 625 ], and Colombia [ 626 ]. Additional cases from Brazil [ 627 ], Peru [ 628 ], and Colombia [ 629 ] were presumably SARS-CoV-2 reinfections, but no sequencing and phylogenetic analysis were conducted due to limited resources. The age of all these individuals (with confirmed reinfection) ranged from 21 to 92 years and the intervening period between first infection and reinfection for these cases ranged from 48 days to 9 months. Most individuals had no pre-existing known immunodeficiencies (except for a Dutch woman suffering from Waldenström macroglobulinemia [ 613 ]). Most individuals were symptomatic during both the first episode and the reinfection. Two individuals that were asymptomatic during the first infection remained as such thereafter [ 617 ]. Several individuals presented increased severity upon reinfection compared to first infection [ 609 , 611 , 613 , 616 , 622 – 625 ]. Cases in which the second episode was less severe raise the possibility of partial immune protection [ 62 ].

The limited diagnostic data available from the first wave of infections as well as the supportive evidence required to publish descriptions of reinfections Footnote 23 has impacted our appreciation for the frequency of these events [ 62 , 63 ]. More recently, large observational studies on reinfection have been published. A large, multicenter cohort study among HCWs in England reported an 84% lower risk of infection with a median protective effect observed seven months following primary infection [ 606 ]. This period was deemed the minimum probable since seroconversions not associated with a positive PCR test were excluded at baseline. A study in Italy found a 94% lower risk of reinfection among patients that had recovered from COVID-19 compared with patients with primary infection [ 61 ]. Natural immunity appeared to confer protection for at least a year. A matched cohort study nested in a representative sample of the general population in Switzerland found a 94% lower hazard of reinfection among SARS-CoV-2-seropositive participants, compared with seronegative controls, for at least eight months after initial serology [ 607 ]. Further, in a population-level observational study encompassing PCR-tested individuals in Denmark, researchers estimated that past SARS-CoV-2 infection conferred ~80% protection against reinfection, decreasing to 47% in individuals aged 65 years and older [ 64 ]. The overall estimate did not vary significantly by sex or over follow-up time (3–6 months vs. ≥7 months). In contrast, another large study of laboratory-confirmed cases in Qatar determined that the risk of reinfection was only 0.02% [ 619 ]. However, unlike this study, the Danish study involved a far greater proportion of asymptomatic individuals, who are likely to elicit relatively marginal immune protection [ 630 ]. Finally, the Alpha variant (clade 20I/501Y.V1 or lineage B.1.1.7) was not associated with an increased risk of reinfection in a study conducted in the UK [ 631 ], which is consistent with further evidence indicating minimal or no immune evasion associated with this genetic variant.


Although SARS-CoV-2 reinfection is possible, it is not responsible for the high number of post-discharge positives found amongst patient cohorts, and thus argues for diagnostic limitations as a major culprit. Persistent or intermittent RNA viral shedding leading to re-positive cases Footnote 24 has been widely reported [ 632 , 633 ]. For instance, a study assessed a group of recurrent-positive patients that exhibited absent or mild symptoms with no disease progression [ 632 ]. Despite detectable viral RNA levels, viral cultures were negative, whole genome sequencing revealed only genomic fragments, and no transmission to contacts was documented by clinical follow-up, acid nucleic tests, and antibody tests. These findings suggested re-detection likely due to intermittent, low-level viral RNA persistence rather than reinfection.


It is important to differentiate between reinfection and recrudescence (i.e., reactivation of lingering virus infection from sanctuary sites). However, this distinction is not straightforward. For example, Gousseff and colleagues reported on a case series of 11 patients with probable reinfection or recrudescence [ 65 ]. These individuals were re-positive for SARS-CoV-2 by qRT-PCR, and infectious virus was found in culture swabs from one of only two individuals tested. Reinfection was the likely scenario in a subgroup of younger, healthy HCWs (median age 32 years) that experienced a relatively mild clinical relapse. A durable immune response may have not elicited in these patients because of mild infection. On the contrary, a subset of older patients (median age 73 years) without occupational exposure required hospitalization for both episodes, leading to death in almost half of them. This suggested recurrence, potentially due to suboptimal control of infection, thus allowing a second episode of viral replication.

Viral recrudescence can result in post-discharge positive tests. Recent findings have demonstrated that the Ebola virus can persist in immune-privileged compartments for extended periods following disease recovery [ 634 – 637 ]. Indeed, recrudescence of Ebola virus from the central nervous system has been noted and resulted in viremia and clinical symptoms of disease. Testicular persistence of this virus has also been noted, though viremia or viral detection outside of semen has not been identified in infected patients [ 638 ]. While there have been limited investigations of persistence and recrudescence of SARS-CoV-2 in humans, data have emerged, demonstrating that viral components can be found in immune-privileged sites. Yang et al. looked at testes from fatal COVID-19 patients and found significant damage to testicular tissue, including the seminiferous tubules [ 639 ]. SARS-CoV-2 RNA was found in <10% of testes sampled and no viral particles were detected by electron microscopy. Ma et al. demonstrated that SARS-CoV-2 can infect testicular cells by revealing in transmission electron microscopy coronavirus-like particles in the interstitial compartment of the testes, in addition to detecting viral RNA [ 640 ]. An earlier cohort study from China demonstrated that SARS-CoV-2 RNA could be found in the semen of recovering patients though there were no assessments of infectious virus or longer-term follow-up samples post-discharge [ 641 ]. Another investigation noted signs of autoimmune orchitis and impaired spermatogenesis in COVID-19 patients; however, all semen samples in this cohort were negative for SARS-CoV-2 by qRT-PCR [ 642 ]. Our investigations of SARS-CoV-2 infections in golden Syrian hamsters have identified viral RNA transiently present within the epididymis and testes of infected animals at two and four days post-infection; however, viral RNA was absent by day seven (Kindrachuk J. et al., unpublished data). Thus, there are inconclusive data at present regarding SARS-CoV-2 persistence within the male reproductive tract.

While brain inflammation and neurological impairment have been recognized in COVID-19 patients, the identification of SARS-CoV-2 in brain tissue remains uncertain [ 643 , 644 ]. Data from both tissue culture organoids and mouse models suggest that brain tissue can be permissive to infection under these conditions, but the extension of this to natural infection is unknown [ 645 , 646 ]. Imai and colleagues demonstrated that infectious virus can be recovered from brain samples of infected one-month-old golden Syrian hamsters on day 3 post-infection but absent on day 6 and day 10 [ 647 ]. This was found using both high-dose (10 5.6 plaque-forming units [PFU]) and low-dose (10 3 PFU) inocula through a combination of intranasal and ocular infection routes. The authors noted that while they demonstrated that SARS-CoV-2 could enter the brain and replicate, viral antigen was not identified in brain tissue.

Further research on reinfection and recrudescence is needed

Given the tens of millions of SARS-CoV-2 infections worldwide to date, confirmed reinfections remain an exceedingly rare occurrence. Although rare, publication of reinfections is biased toward the diagnosis of symptomatic cases, with asymptomatic cases likely underreported [ 598 ]. Reasons for this are the testing eligibility criteria and the lack of resources and rigorous surveillance in many places, except for routine community testing scenarios such as airports [ 612 ] and healthcare settings [ 617 ].

In summary, while reinfection and recrudescence appear to be infrequent events, they cannot be dismissed altogether as simple errors or sensitivity issues in current diagnostic technologies. Distinguishing between reinfection and alternative phenomena is not easy and relies on epidemiological analyses (including clinical case history assessment) and virological data (nucleic acid amplification testing and comparative genome analysis) to rule out persistent viral RNA shedding and possibly recrudescence. SARS-CoV-2 reinfection has yet largely unknown implications for immunity. Therefore, further research is warranted.


This comprehensive narrative review sits at the heart of the science-policy interface, allows an interdisciplinary approach to evidence synthesis, and facilitates step-by-step engagement by readers. We compile and discuss evidence that is useful for decision-makers to consider in the context of a complex policy landscape with many actors and competing priorities and risks. The unfolding pandemic has raised conundrums for which there are no straightforward yes/no answers or unequivocal solutions. False dichotomies are pervasive and attractive—they offer an escape from the unsettling complexity and enduring uncertainty. Besides, faulty reasoning and politicization of uncertainty and disagreement in science preclude debating the merits of various positions and refuting the spurious claims. Uncertainties and complexities are part and parcel of science, public health, and several aspects of pathogen transmission, infection, and disease. These aspects lie on a gradient of gray shades—they are hardly binary, simple, or uniform, and should not be framed as black or white.

Overstated and poor-quality science is harmful and misinforms public health response and policy. In light of the challenges surrounding the science-policy interface for COVID-19, we caution against black-or-white messaging, all-or-nothing guidance, and one-size-fits-all approaches. Subtleties and uncertainties should not be portrayed as enemies but as allies of transparent and accurate messaging, health literacy, critical thinking, and credibility and legitimacy of health authorities. Continued efforts in countering misinformation and disinformation and promoting accuracy in social and mass media are needed.

Public health thrives by providing nuanced guidance that reflects trade-offs and uncertainty, while engaging the public in policy decisions. Culturally appropriate public health communication, science-informed tailored policies, and health journalism that reckon with shades of gray, uncertainties, local contexts, and social determinants are long overdue. As evidence continues to accrue at an unparalleled pace, our understanding of SARS-CoV-2 and COVID-19 evolves allowing policy amendments.

Availability of data and materials

All studies cited and discussed are peer-reviewed journal articles, preprints, and media articles (gray literature) available in the public domain; some require the relevant database or journal/media subscriptions for access. All data underlying the evidence synthesis of this study are included in the article.

Infodemic is the overabundance of information (some accurate and some not) making it hard for people to find reliable sources and guidance [ 2 ].

Broadly, misinformation can be defined as incorrect information, possibly by accident [ 13 ]. In contrast, disinformation is often used to denote misinformation that is deliberately false and disseminated.

Engagement echo chambers and filter bubbles relate to social media practices that exhibit highly segmented interaction with social media content [ 14 ]. Echo chambers may reinforce shared stances and preexisting views of like-minded people. Echo chambers are not the exclusive or necessarily the main mechanism of misinformation.

Works deemed essential include but are not limited to healthcare, law enforcement, government, fire department, first responders, delivery/pick-up services, and transportation.

A lockdown is understood as a complete shutting down of all economic activity except if deemed most essential, along with stay-at-home orders and usually with stringent travel restrictions [ 25 ].

Respiratory etiquette or hygiene refers to covering mouth and nose with a disposable tissue when coughing or sneezing and disposing of it after use, or coughing/sneezing into the inner elbows or sleeve, followed by hand hygiene.

The term “Emmentaler cheese” is more appropriate than "Swiss cheese" since not all Swiss cheeses have holes [ 104 ].

SSEs refer to activities and settings where an individual gives rise to a large number of secondary infections [ 35 , 112 ]. Closed environments, poorly ventilated environments, crowded places, and prolonged exposures all correlate with emergence of SSEs. These hotspots are important as both community infection sources and targets for effective contact tracing, targeted restrictions, and NPIs. SSEs could be influenced by biological features (e.g., an individual with increased infectiousness inferred by viral loads), behavioral/social features (e.g., an infectious individual with high number of susceptible contacts), environmental features (e.g., high-risk settings due to high human interaction or density), and opportunistic features (e.g., singing or activities leading to increased probability of transmission and infection) [ 35 ].

The dispersion parameter k quantifies the variability in the number of secondary cases and is used as a measure of the impact of superspreading. A lower k parameter indicates higher transmission heterogeneity (higher superspreading potential). k for SARS-CoV-2 has been variably estimated from 0.04 to 0.58 [ 112 – 115 ].

COVID-19 absolutism is the discouragement and prohibition of absolutely any behavior leading to some risk of SARS-CoV-2 infection [ 133 ].

This term has been used to refer to the notion of behavioral fatigue potentially associated with a lower adherence to public health interventions.

Outdoor gatherings of household members, sports like tennis, and hiking or jogging while distancing are examples of lower-risk outdoor activities.

From a public health perspective, a mass gathering is defined as an event where the number of people attending (usually thousands but this is variable) may strain the planning and response resources of the host community, state, or country [ 145 ].

Elimination is often defined as the local reduction of infection to zero cases or a prolonged period of zero local transmission in a geographic region, with interventions still required to maintain it [ 162 – 164 ].

Eradication is often defined as the global reduction of infection to zero cases as a result of deliberate efforts, with further interventions no longer needed [ 162 – 164 ]. A further and more difficult step is extinction, which is when a pathogen no longer exists in nature or in the laboratory.

The incubation period is the time elapsed between infection and symptom onset. It usually ranges from 2 to 14 days. The mean incubation period for SARS-CoV-2 infection is 5.5–6.5 days, and the median incubation period is 5.1 days [ 118 , 166 ].

Secondary attack rate is the percentage of secondary cases (infectees), among the total of contacts, resulting from one infected person (infector).

Medical AGPs are procedures that are likely to generate high concentrations of infectious respiratory aerosols (e.g., tracheal intubation, non-invasive positive pressure ventilation), placing HCWs at an increased risk for pathogen exposure and infection [ 251 , 252 ]. The term AGP is a misnomer inasmuch as it is not the AGP per se that generates aerosols and drives transmission but the circumstances surrounding the AGP, including the air forced over the respiratory mucosa, index patient’s respiratory symptoms, exposure distance, and duration [ 253 ].

The reproduction number (R) is a measure that reflects the infectiousness of an agent. The basic reproduction number (R 0 ) is the average number of secondary infections caused by one infected person in a completely susceptible population, in the absence of interventions. The reproduction number varies according to several circumstances and time (thus called effective reproduction number at time t , R t ) as more people are infected (developing immunity) and public health measures are implemented.

Counter-visualizations are counterpublic’s data visualization practices that use orthodox methods to make unorthodox arguments [ 19 ]. They may serve the purpose of challenging mainstream narratives and promoting misinformation and conspiracy.

The precautionary principle assists with decision-making under uncertainty [ 525 , 526 ]. Traditionally used to advice caution in the uptake of interventions or innovations with known benefits but uncertain risks [ 527 ], the precautionary principle has been substantially used in the opposite sense in the COVID-19 pandemic—to advocate the uptake of interventions because of the potential great benefits and minimal risks [ 432 ]. It has been argued that both the omission in the former case and the act in the latter case may prevent harm [ 528 ].

Masks may cause discomfort and subjective but minor breathing difficulties. However, claims pertaining to decreased oxygen saturation are unfounded. Recent studies assessing medical masks and cloth face coverings in the general adult population have not demonstrated major physiologic changes related to gas exchange [ 454 , 455 , 556 – 561 ].

Acceptable evidence in support of SARS-CoV-2 reinfection consists of testing positive by qRT-PCR at two different time points (usually separated by a period of test negativity if documented), plus distinct genome sequencing of viral isolates on each episode [ 62 , 63 ]. The lack of these data, which is expected outside research settings, hinders reporting of reinfection. In regions with robust genomic surveillance, sequencing a SARS-CoV-2 genetic variant from a sample of the second infection episode provides evidence of reinfection if the variant was not present in the region at the time of the first episode [ 623 ].

Re-positive or recurrent-positive cases are individuals who recovered from previously confirmed COVID-19 disease and had a positive test again, attributed to the same infection episode.


Aerosol-generating procedure

Asymptomatic SARS-CoV-2 infection

Biosafety level 3

US Centers for Disease Control and Prevention

Coronavirus disease 2019

Cycle threshold

Filtering facepiece respirator

Healthcare worker

Infection prevention and control

Korea Disease Control and Prevention Agency

Middle East respiratory syndrome

Multisystem inflammatory syndrome in adults

Multisystem inflammatory syndrome in children

Non-pharmaceutical intervention

Post-acute sequelae of COVID-19

Plaque-forming unit

Particulate matter

Personal protective equipment

Basic reproduction number

randomized controlled trial

quantitative reverse transcriptase-polymerase chain reaction

Severe acute respiratory syndrome

Severe acute respiratory syndrome coronavirus 2

Superspreading event

Transmission-based precaution

Median tissue culture infectious dose

World Health Organization

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We thank Angélica L. Cruz, BA for designing the Fig. 2 for this manuscript and Gianina Flocco, MD (Cleveland Clinic, USA) for her comments on an earlier draft. We also acknowledge Graham Martin, PhD (University of Cambridge, UK), Emma B. Hodcroft, PhD (University of Bern, Switzerland) Gregory J. Bix, MD, PhD (Tulane University School of Medicine, USA), Krutika Kuppalli, MD (Medical University of South Carolina, USA), and Muge Cevik, MD, MSc (University of St Andrews, UK) for helpful discussions on the topics of this manuscript. All of them gave permission to add their names. This article was first preprinted in August 2020 at and was continuously updated until publication date.

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KeE conceptualized the article, led the manuscript development, and was a major contributor in the writing of all manuscript sections. ALR, IIB, EJM, KaE, JK, and SVP contributed to different sections of the first draft and commented on all subsequent manuscript versions. Specifically, the contributions of the authors were focused on the economic, sociological, and anthropological aspects (EJM, KaE, KeE), preventive and public health interventions (KeE, EJM), clinical and asymptomatic presentation (KeE, IIB), transmission (KeE, ALR, SVP), and reinfection (KeE, JK). KaE designed Figs. 1 , 3 , and 4 for this manuscript. All authors critically revised and agreed upon the final version of this manuscript prior to submission and during peer-review.

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Dr. Kevin Escandón is a Senior Editorial Board Member for BMC Infectious Diseases and Dr. Jason Kindrachuk is an Editorial Board Member for the Viral Diseases section of BMC Infectious Diseases . These authors were not involved in any of the decisions regarding review of the manuscript or its acceptance. Three in-house Editors for the BMC Series and two anonymous expert reviewers assessed this manuscript. Dr. Isaac I. Bogoch has consulted for BlueDot, a social benefit corporation that tracks the emergence of infectious diseases, and for the National Hockey League Players’ Association. The other authors declare no conflicts of interest. The authors confirm that they have read BMC’s guidance on competing interests. Views expressed here are solely those of the authors and do not represent the position or policy of any institution or organization.

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Escandón, K., Rasmussen, A.L., Bogoch, I.I. et al. COVID-19 false dichotomies and a comprehensive review of the evidence regarding public health, COVID-19 symptomatology, SARS-CoV-2 transmission, mask wearing, and reinfection. BMC Infect Dis 21 , 710 (2021).

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Research Misconduct by the Numbers

The data on this page reflect the Research Integrity and Administrative Investigations Division’s allegations of research misconduct received, research misconduct cases opened and closed, and outcome of research misconduct cases closed by Fiscal Year (FY). This page will be updated yearly.

FY 2023 (October 1, 2022 – September 30, 2023)

The table below shows the number of research misconduct allegations received, cases opened, and cases closed in FY 2023. Investigations may span multiple FYs.

Research Misconduct Cases FY 2023

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When we receive an allegation of research misconduct, we initiate an inquiry to determine whether it has enough substance to warrant an investigation. For example, we may send the subject of the allegation a letter requesting an explanation and supporting evidence. If there is enough substance to proceed, we open a formal investigation.

Investigations involve collecting and reviewing facts, assessing the elements required for a research misconduct finding, and determining whether research misconduct occurred. We generally refer research misconduct investigations, along with any evidence we obtained during our inquiry, to the awardee institution. We also provide procedural guidance to the institution’s investigation committee. Once the institution completes its investigation, it sends us a report. We review the report for accuracy and completeness and decide whether to accept its conclusions. We may accept an institution’s report in whole or in part, request additional information, or initiate our own independent investigation.

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The table below shows the outcomes of the research misconduct cases closed during FY 2023.

Outcomes of Research Misconduct (RM) Cases FY 2023

** “Included Debarment” is a subset of NSF finding and actions.  

FY 2022 (October 1, 2021 – September 30, 2022)

The table below shows the number of research misconduct allegations received, cases opened, and cases closed in FY 2022. Investigations may span multiple FYs.

Research Misconduct Cases FY 2022  

The table below shows the outcomes of the research misconduct cases closed during FY 2022. 

Outcomes of Research Misconduct Cases FY 2022 

* “Included Debarment” is a subset of NSF Findings and Actions. 

FY 2021 (October 1, 2020 – September 30, 2021) 

The table below shows the Research Integrity and Administrative Investigations Division’s allegations received, cases opened, and cases closed during FY 2021. 

Research Integrity and Administrative Investigations FY 2021

* “Other” indicates violations of non-research misconduct regulations (e.g., violations of reviewer confidentiality, human subject regulation, or matters not appropriate for investigation).  * “Mixed” indicates cases that involved more than one type of allegation. 

The figure below shows the outcomes of the research misconduct cases closed during FY 2021. 

The outcomes of research misconduct cases closed during FY 2021. 13 cases referred to institution; 19 cases closed with warning letter; 9 cases with NSF findings and actions; 1 case with debarment; 39 cases closed in total; 51.3% plagiarism; 5.1% fabrication/falsification; 25.6% other; 10.3% whistleblower retaliation; and 7.7% mixed.

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Striking findings from 2022

examples of false research findings 2021

Pew Research Center’s surveys have shed light on public opinion around some of the biggest news events of 2022 – from Russia’s military invasion of Ukraine to the overturning of Roe v. Wade to Americans’ experiences with extreme weather events . Here’s a look back at the past year through 15 of our most striking research findings, which cover these topics and more. These findings represent just a sample of the Center’s research publications this year .

Today, roughly four-in-ten Americans (41%) say none of their purchases in a typical week are paid for using cash , a July survey found. This is up from 29% in 2018 and 24% in 2015.

A bar chart showing that Americans have become more likely to say they don’t use cash for purchases in a typical week. 41% say this, up from 29% in 2018 and 24% in 2015.

Meanwhile, the portion of Americans who say that all or almost all of their purchases are paid for with cash in a typical week has declined from 24% in 2015 to 18% in 2018 to 14% today.

While growing shares of Americans across income groups are relying less on cash than in the past, this is especially the case among the highest earners. Roughly six-in-ten adults whose annual household income is $100,000 or more (59%) say they make none of their typical weekly purchases using cash, up sharply from 43% in 2018 and 36% in 2015.

If recent trends continue, Christians could make up a minority of Americans by 2070. That’s according to a September report that models several hypothetical scenarios of how the U.S. religious landscape might change over the next 50 years, based on religious switching patterns.

Since the 1990s, large numbers of Americans have left Christianity to join the growing ranks of U.S. adults who describe their religious identity as atheist, agnostic or “nothing in particular.”

Depending on whether religious switching continues at recent rates, speeds up or stops entirely – the last of which is not plausible because it assumes all switching has already ended – the projections show Christians of all ages shrinking from 64% to somewhere between 54% and 35% of all Americans by 2070. Over that same period, “nones” would rise from their current 30% of the population to somewhere between 34% and 52%.

A line graph showing that U.S. Christians are projected to fall below 50% of the population if recent trends continue

Views of reparations for slavery vary widely by race and ethnicity , especially between Black and White Americans, a November analysis found. Overall, 30% of U.S. adults say descendants of people enslaved in the United States should be repaid in some way, such as given land or money. About seven-in-ten (68%) say these descendants should not be repaid.

A bar chart showing that 77% of Black Americans – compared with 18% of White Americans – support reparations for descendants of enslaved people

Around three-quarters of Black adults (77%) say the descendants of people enslaved in the U.S. should be repaid in some way. Just 18% of White adults hold this view.

There are also notable differences by party affiliation and age. Among Democrats and Democratic-leaning independents, views are split: 48% say descendants of enslaved people should be repaid in some way, while 49% say they should not. Only 8% of Republicans and GOP leaners say these descendants should be repaid in some way, and 91% say they should not.

And 45% of adults under 30 say these descendants should be repaid, compared with 18% of those 65 and older.

Notably, three-quarters of adults who say descendants of those enslaved in the U.S. should be repaid (including 82% of Black adults who say this) say it’s a little or not at all likely this will happen in their lifetime.

A growing share of adult TikTok users in the U.S. are getting news on the platform , bucking the trend on other social media sites, according to a survey fielded in July and August. A third of adults who use TikTok say they regularly get news there, up from 22% two years ago. The increase comes even as news consumption on many other social media sites has either decreased or stayed about the same in recent years. For example, the share of adult Facebook users who regularly get news there has declined from 54% in 2020 to 44% this year.

A line graph showing that a growing share of TikTok’s adult users say they regularly get news on the site. 33% say this, up from 29% in 2021 and 22% in 2020.

Most Americans who have experienced extreme weather in the past year – including majorities in both political parties – see climate change as a factor, according to a May survey .

A bar chart showing that in both parties, six-in-ten or more who faced certain weather events say climate change played a role

Overall, 71% of Americans said that, in the past 12 months, their community had experienced at least one of the five forms of extreme weather the Center’s survey asked about. Among those who had recently encountered extreme weather, more than eight-in-ten said climate change contributed at least a little to each type of event.

Among Democrats as well as Republicans, majorities of those who had experienced one of these forms of extreme weather said climate change contributed to the event. But Democrats were more likely than Republicans to say climate change contributed  a lot .

A line graph showing that since Russia’s invasion of Ukraine, Americans are much more likely to consider Russia an enemy. 70% of Americans say this, up from 41% in January

Following Russia’s military invasion of Ukraine, Americans became much more likely to see Russia as an enemy of the United States . In March, just after the invasion, 70% of Americans said that, on balance, Russia is an enemy of the U.S., up sharply from 41% who held this view in January. In the January survey, Americans were more likely to describe Russia as a competitor of the U.S. than as an enemy. In both surveys, very few Americans described Russia as a U.S. partner.

Democrats and Republicans largely agreed in the March survey that Russia is an enemy of the U.S., but partisan and ideological differences still existed. Liberal Democrats, for example, were the most likely to see Russia as an enemy (78%), while moderate and liberal Republicans were the least likely to do so (63%).

Relatively few Americans take an absolutist view on the legality of abortion – either supporting or opposing it at all times, according to a survey conducted in March, before the Supreme Court overturned Roe v. Wade. The vast majority of the public is somewhere in the middle when it comes to abortion : Most think it should be legal in at least some circumstances, but most are also open to limitations on its availability in others.

A pie chart showing that a 61% majority of adults say abortion should be legal in some cases and illegal in others

Overall, 19% of Americans say that abortion should be legal in all cases, with no exceptions. Fewer (8%) say abortion should be illegal in every case, without exception. But 71% either say it should be mostly legal or mostly illegal, or say there are exceptions to their blanket support for or opposition to legal abortion.

A separate survey conducted in June and July – after the Supreme Court struck down Roe – found that 57% of adults disapproved of the decision, including 43% who strongly disapproved. About four-in-ten (41%) approved, including 25% who strongly approved. 

A line graph showing that the partisan gap in views of the Supreme Court is now wider than at any point in more than three decades. 73% of Republicans have a favorable view; 28% of Democrats do.

Following the Supreme Court’s decision to overturn Roe v. Wade, the partisan gap in views of the court grew wider than at any point in more than three decades. While 73% of Republicans expressed a favorable view of the court in an August survey, only 28% of Democrats shared that view. That 45-point gap was wider than at any point in 35 years of polling on the court.

The current polarization follows a term that included the ruling on abortion and  several other high-profile cases  that often split the justices along ideological lines.

Growing shares of Democrats also say the Supreme Court has a conservative tilt: 67% said this in August, up from 57% in January. And about half of Democrats (51%) said in August that the justices on the court are doing a poor job of keeping their own political views out of their judgments on major cases, nearly double the share who said this in January (26%).

A bar chart showing that in the U.S., young adults are the most likely to be transgender or nonbinary; 5% say this

About 5% of Americans younger than 30 are transgender or nonbinary – that is, their gender is different from their sex assigned at birth, according to a survey conducted in May. By comparison, 1.6% of those ages 30 to 49 and 0.3% of those 50 and older say that their gender is different from their sex assigned at birth. Overall, 1.6% of U.S. adults are transgender or nonbinary – that is, someone who is neither a man nor a woman or isn’t strictly one or the other.

While a relatively small share of U.S. adults are transgender or nonbinary, many say they know someone who is. More than four-in-ten (44%) say they personally know someone who is trans and 20% know someone who is nonbinary. The share of adults who know someone who is transgender  has increased  from 42% in 2021 and from 37% in 2017.

In focus groups with trans and nonbinary adults, most participants said they knew from an early age – many as young as preschool or elementary school – that there was something different about them, even if they didn’t have the words to describe what it was.

Most Americans say journalists should always strive to give every side equal coverage , but journalists themselves are more likely to say every side does not always deserve equal coverage, according to two separate surveys conducted in late winter amid debate over “ bothsidesism ” in the media.

A bar chart showing that U.S. journalists are more likely than the public to say all sides don’t always deserve equal coverage. 76% of U.S. adults say this; 44% of journalists do.

Among Americans overall, 76% say journalists should always strive to give all sides equal coverage, while 22% say every side does not always deserve equal coverage. The balance of opinion is reversed among journalists themselves: A little more than half (55%) say every side does not always deserve equal coverage, while 44% say journalists should always strive to give every side equal coverage.

This issue gained  new intensity  during Donald Trump’s presidency and the  widespread disinformation and competing views  surrounding the 2020 election and the COVID-19 pandemic. Those who favor  equal coverage argue that it’s always necessary to allow the public to be equally informed about multiple sides of an argument, while those who disagree contend that people making false statements or unsupported conjectures do not warrant as much attention as those making factual statements with solid supporting evidence.

A recent surge in U.S. drug overdose deaths has hit Black men the hardest, a January analysis found. While overdose death rates have increased in every major demographic group in recent years, no group has seen a bigger increase than Black men. As a result, Black men have overtaken White men and are now on par with American Indian or Alaska Native men as the demographic groups most likely to die from overdoses.

A line graph showing that the drug overdose death rate among Black men in the U.S. more than tripled between 2015 and 2020 from 17.3 per 100,000 to 54.1

Nearly 92,000 Americans died of drug overdoses in 2020, up from around 70,000 in 2017. During the same period, the  rate  of fatal overdoses rose from 21.7 to 28.3 per 100,000 people.

Despite these increases, the share of Americans who say drug addiction is a major problem in their local community  declined by 7 percentage points  in subsequent surveys – from 42% in 2018 to 35% in 2021. And in a  separate survey  in early 2022, dealing with drug addiction ranked lowest out of 18 priorities for the president and Congress to address this year.

Nearly half of U.S. teens now say they use the internet “almost constantly,” according to a survey conducted in April and May. This percentage has roughly doubled since 2014-15, when 24% said they were almost constantly online.

A bar chart showing that nearly half of teens (46%) now say they use the internet ‘almost constantly’

Black and Hispanic teens stand out for being on the internet more frequently than White teens. Some 56% of Black teens and 55% of Hispanic teens say they are online almost constantly, compared with 37% of White teens. (There were not enough Asian American teens in the sample to analyze separately.)

Older teens are also more likely to be online almost constantly. About half of 15- to 17-year-olds (52%) say they use the internet almost constantly, while 36% of 13- to 14-year-olds say the same. And 53% of urban teens report doing this, compared with somewhat smaller shares of suburban and rural teens (44% and 43%, respectively).

Since 2014-15, there has been a 22-point rise in the share of teens who report having access to a smartphone (from 73% then to 95% now). While teens’ access to smartphones has increased, their access to other digital technologies, such as desktop or laptop computers or gaming consoles, has remained statistically unchanged.

The share of aggregate U.S. household income held by the middle class has fallen steadily since 1970, according to an analysis published in April.

A line graph showing that the share of aggregate income held by the U.S. middle class has plunged since 1970, from 62% to 42%

In 1970, adults in middle-income households accounted for 62% of aggregate income, a share that fell to 42% by 2020. Meanwhile, the share of aggregate income held by upper-income households has increased steadily, from 29% in 1970 to 50% in 2020. Part of this increase reflects the rising share of adults who are in the upper-income tier; another part reflects more rapid growth in earnings for these adults.

The share of U.S. aggregate income held by lower-income households edged down from 10% to 8% over these five decades, even though the proportion of adults living in lower-income households increased over this period.

Growing shares of both Republicans and Democrats say that members of the other party are more immoral, dishonest and closed-minded than other Americans, according to a survey conducted in June and July.

A line graph showing that growing shares of both Republicans and Democrats say members of the other party are more immoral, dishonest, and closed-minded than other Americans

The percentage of Americans who view the people in the opposing political party in a negative light has increased in recent years. In 2016, 47% of Republicans and 35% of Democrats said those in the other party were a lot or somewhat more immoral than other Americans. Today, 72% of Republicans regard Democrats as more immoral than other Americans, and 63% of Democrats say the same about Republicans. Similar patterns exist when it comes to seeing members of the other party as more dishonest, closed-minded and unintelligent than other Americans.

There is one negative trait that Republicans are far more likely than Democrats to link to their political opponents. A 62% majority of Republicans say Democrats are “more lazy” than other Americans, up from 46% in both  2019  and  2016 .

A bar chart showing that social media is generally seen as good thing for democracy – but not in the U.S. A median of 57% in 19 countries say this, while 34% of U.S. adults do.

Majorities in nations around the world generally see social media as a good thing for democracy – but not in the United States, a survey of people in 19 advanced economies found.

Americans are the most negative about the impact of social media on democracy: 64% say it has been bad. Republicans are much more likely than Democrats (74% vs. 57%) to see the ill effects of social media on the political system.

In addition to being the most negative about social media’s influence on democracy, Americans are consistently among the most negative in their assessments of specific ways that social media has affected politics and society. For example, 79% in the U.S. believe access to the internet and social media has made people more divided in their political opinions, the highest percentage among the countries polled.

Read the other posts in our striking findings series:

  • Striking findings from 2021
  • 20 striking findings from 2020
  • 19 striking findings from 2019
  • 18 striking findings from 2018
  • 17 striking findings from 2017
  • 16 striking findings from 2016
  • 15 striking findings from 2015
  • 14 striking findings from 2014
  • Other Topics
  • Politics & Policy

Katherine Schaeffer's photo

Katherine Schaeffer is a research analyst at Pew Research Center .

What the data says about crime in the U.S.

Most americans think u.s. k-12 stem education isn’t above average, but test results paint a mixed picture, about 1 in 4 u.s. teachers say their school went into a gun-related lockdown in the last school year, changing partisan coalitions in a politically divided nation, about half of americans say public k-12 education is going in the wrong direction, most popular.

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examples of false research findings 2021

Study: racial bias is no ‘false alarm’ in policing

April 30, 2024

ANN ARBOR—Black drivers are more frequently searched without finding contraband during traffic stops than white drivers, according to a University of Michigan study.

Institute for Social Research scientists Maggie Meyer and  Richard Gonzalez  analyzed data from 98 million traffic stops, and showed that innocent Black drivers were likely to be searched about 3.4 to 4.5 percent of the time while innocent white drivers were likely to be searched about 1.9 to 2.7 percent of the time. Their results are published in the Journal of Quantitative Criminology.

“We show that there’s this pervasive bias in multiple states and multiple counties across different stop and search reasons that we need to understand,” said Meyer, a doctoral candidate in psychology. “We’re not the first people to find racial bias in policing and we won’t be the last, but hopefully, this gives a clear place to intervene.”

Meyer and Gonzalez, director of the Research Center for Group Dynamics at ISR and professor of psychology, used data from the Stanford Open Policing Project, a database of traffic stops from law enforcement agencies across the country. They examined traffic stops in 14 state police departments and 11 local law enforcement departments between the years 1999 and 2017.

Meyer says that, for example, in Durham County, North Carolina, the false alarm rate for Black drivers ranges from 6 to 8% while the false alarm rate for white drivers is 3 to 4%. This equates to 11,000 Black drivers compared to about 2,500 white drivers who are searched while innocent.

“We know that there’s at least this 2% difference at most, where the two values are the closest. That’s where we can start to make these claims of bias,” Meyer said. “The really powerful part about these data is that these findings aren’t massive—they’re not 30, 40%, they’re 2, 3, 4, 5%. But at 98 million traffic stops across 14 states, that’s still a very large and meaningful number of innocent drivers who are searched.”

Officers’ decisions result in tradeoffs between the probability of contraband in the population, the probability of finding contraband among those the officer selects to search, and the costs of making errors such as failing to search someone with contraband and needlessly searching someone without contraband, Gonzalez says.

The researchers had three pieces of information about these traffic stops: the total number of traffic stops during the time period in a given county or state, whether the officer searched their car, and whether they found contraband. They don’t know whether drivers who weren’t searched held contraband.

To account for this unknown, the researchers developed what they call the Overlapping Condition Test. They base this test on a standard descriptive tool in statistics called a 2×2 table. This table allows researchers to jointly evaluate a decision and an outcome. To do this, researchers use hit rates and false alarm rates. 

In this context, hit rate is a measure of officer accuracy that depends on the contraband rate on any driver that was stopped, even if they were not searched. False alarm rates refer to the proportion of drivers officers search but do not find contraband, and it depends on the total number of innocent drivers that were stopped–even if they were not searched.

In this 2×2 table, the researchers filled in these known pieces of information: whether the officer searched and whether the officer found contraband when they searched. The researchers explored the possible values of the missing information—whether the drivers who were not searched had contraband or not.

“It’s analogous to presidential elections with the electoral college. An election can be called because one candidate already has enough electoral votes to win, even though all of us haven’t been counted,” Gonzalez said. “So even though there may still be missing information, the outcome of the election is set. Even if those uncounted votes went for the other candidate, one candidate has already got it in the bag.”

The researchers say their method can be used to inform policy to help mitigate the issue. 

“We can move forward. We can say, ‘Hey, there’s a problem. We have to think about policy,”’ Gonzalez said. “We have to figure out why officers are searching innocent Blacks more than innocent whites. We don’t need to wait until we know the actual values of the missing information, because it doesn’t matter what they are. The bias is there.”

Study:  Detecting bias in traffic searches: Examining false searches of innocent drivers

Contact: Morgan Sherburne, 734-647-1844,  [email protected]   

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April 30, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:


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Study: Racial bias is no 'false alarm' in policing

by University of Michigan

Study: Racial bias is no 'false alarm' in policing

Black drivers are more frequently searched during traffic stops without finding contraband than white drivers, according to a University of Michigan study.

Institute for Social Research scientists Maggie Meyer and Richard Gonzalez analyzed data from 98 million traffic stops, and showed that innocent Black drivers were likely to be searched about 3.4 to 4.5% of the time while innocent white drivers were likely to be searched about 1.9 to 2.7% of the time. The results are published in the Journal of Quantitative Criminology .

"We show that there's this pervasive bias in multiple states and multiple counties across different stop and search reasons that we need to understand," said Meyer, a doctoral candidate in psychology. "We're not the first people to find racial bias in policing and we won't be the last, but hopefully, this gives a clear place to intervene."

Meyer and Gonzalez, director of the Research Center for Group Dynamics at ISR and professor of psychology, used data from the Stanford Open Policing Project, a database of traffic stops from law enforcement agencies across the country. They examined traffic stops in 14 state police departments and 11 local law enforcement departments between the years 1999 and 2017.

Meyer says that for example, in Durham County, North Carolina, the false alarm rate for Black drivers ranges from 6 to 8% while the false alarm rate for white drivers is 3 to 4%. This equates to 11,000 Black drivers compared to about 2,500 white drivers who are searched while innocent.

"We know that there's at least this 2% difference at most, where the two values are the closest. That's where we can start to make these claims of bias," Meyer said. "The really powerful part about these data is that these findings aren't massive—they're not 30, 40%, they're 2, 3, 4, 5%. But at 98 million traffic stops across 14 states, that's still a very large and meaningful number of innocent drivers who are searched."

Officers' decisions result in tradeoffs between the probability of contraband in the population, the probability of finding contraband among those the officer selects to search, and the costs of making errors such as failing to search someone with contraband and needlessly searching someone without contraband, Gonzalez says.

The researchers had three pieces of information about these traffic stops: the total number of traffic stops during the time period in a given county or state, whether the officer searched their car, and whether they found contraband. They don't know whether drivers who weren't searched held contraband.

To account for this unknown, the researchers developed what they call the Overlapping Condition Test. They base this test on a standard descriptive tool in statistics called a 2x2 table. This table allows researchers to jointly evaluate a decision and an outcome. To do this, researchers use hit rates and false alarm rates.

In this context, hit rate is a measure of officer accuracy that depends on the contraband rate on any driver that was stopped, even if they were not searched. False alarm rates refer to the proportion of drivers officers search but do not find contraband, and it depends on the total number of innocent drivers that were stopped–even if they were not searched.

In this 2x2 table, the researchers filled in these known pieces of information: whether the officer searched and whether the officer found contraband when they searched. The researchers explored the possible values of the missing information—whether the drivers who were not searched had contraband or not.

"It's analogous to presidential elections with the electoral college. An election can be called because one candidate already has enough electoral votes to win, even though all of us haven't been counted," Gonzalez said. "So even though there may still be missing information, the outcome of the election is set. Even if those uncounted votes went for the other candidate, one candidate has already got it in the bag."

The researchers say their method can be used to inform policy to help mitigate the issue.

"We can move forward. We can say, 'Hey, there's a problem. We have to think about policy,''' Gonzalez said. "We have to figure out why officers are searching innocent Blacks more than innocent whites. We don't need to wait until we know the actual values of the missing information, because it doesn't matter what they are. The bias is there."

Provided by University of Michigan

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    More. There has been an increasing concern in both the scientific and lay communities that most published medical findings are false. But what does it mean to be false? Here we describe the range of definitions of false discoveries in the scientific literature. We summarize the philosophical, statistical, and experimental evidence for each type ...

  15. What proportion of published research findings are false?

    Different methods. The article 'Why most published research findings are false' by John Ioannidis attracted considerable attention when it was published in 2005 (1).The article was not based on data, but postulated a model for the proportion of false positive findings among published positive findings based on the following four quantities: the proportion of actually true hypotheses of all the ...

  16. List of scientific misconduct incidents

    In Denmark, scientific misconduct is defined as "intention [al] negligence leading to fabrication of the scientific message or a false credit or emphasis given to a scientist", and in Sweden as "intention [al] distortion of the research process by fabrication of data, text, hypothesis, or methods from another researcher's manuscript form or ...

  17. When Research Evidence is Misleading

    When Research Evidence is Misleading. Ioannidis JP. Why most published research findings are false. PLoS Med. 2005;2 (8):e124. Each year, millions of research hypotheses are tested. Datasets are analyzed in ad hoc and exploratory ways. Quasi-experimental, single-center, before and after studies are enthusiastically performed.

  18. Research Misconduct by the Numbers

    4. 1. 15. * "Mixed" indicates cases that involved more than one type of allegation. When we receive an allegation of research misconduct, we initiate an inquiry to determine whether it has enough substance to warrant an investigation. For example, we may send the subject of the allegation a letter requesting an explanation and supporting ...

  19. Examples of False Research Findings: What They Are and How to ...

    False research findings can occur due to various reasons, including biases, errors in methodology, or even intentional misconduct. Here are some examples and ways to avoid them: By being aware of…

  20. Striking findings from 2021

    As 2021 draws to a close, here are some of Pew Research Center's most striking research findings from the past year. These 15 findings cover subjects ranging from extreme weather to the COVID-19 pandemic and ongoing demographic shifts in the United States. And they represent just a small slice of the year's full list of research publications.

  21. Misleading Statistics

    The ASA also claimed that the scripts used for the survey informed the participants that the study was being performed by an independent research company, which was inherently false. Based on the misuse techniques we covered, it is safe to say that this sleight off-hand technique by Colgate is a clear example of misleading statistics in ...

  22. Misinformation and competing views of reality ...

    3. Misinformation and competing views of reality abounded throughout 2020. 4. Americans who mainly got news via social media knew less about politics and current events, heard more about some unproven stories. 5. Republicans' views on COVID-19 shifted over course of 2020; Democrats' hardly budged. Appendix: Measuring news sources used ...

  23. Evaluating witness testimony: Juror knowledge, false memory, and the

    Research also shows that false memories can arise more spontaneously. For example, a false memory can arise where a person remembers the 'gist' of something that they have seen (e.g. an offender) (creating a familiarity) but not specific 'verbatim' details (precise recollection) (Brainerd and Reyna, 2005). When this happens, a person ...

  24. PDF Written Testimony of Jamie Raskin, Ranking Member, House Committee on

    business model, the companies suppressed relevant scientific findings for decades and came to challenge and contradict urgent calls by scientists to take climate change seriously as a global threat. As the experts told us, this pattern of lying and evasion set the country back decades in our ability to seriously address and manage climate change.

  25. Striking findings from 2022

    Pew Research Center's surveys have shed light on public opinion around some of the biggest news events of 2022 - from Russia's military invasion of Ukraine to the overturning of Roe v. Wade to Americans' experiences with extreme weather events.Here's a look back at the past year through 15 of our most striking research findings, which cover these topics and more.

  26. Study: racial bias is no 'false alarm' in policing

    Institute for Social Research scientists Maggie Meyer and ... Meyer says that, for example, in Durham County, North Carolina, the false alarm rate for Black drivers ranges from 6 to 8% while the false alarm rate for white drivers is 3 to 4%. This equates to 11,000 Black drivers compared to about 2,500 white drivers who are searched while innocent.

  27. Study: Racial bias is no 'false alarm' in policing

    Meyer says that for example, in Durham County, North Carolina, the false alarm rate for Black drivers ranges from 6 to 8% while the false alarm rate for white drivers is 3 to 4%.