Data Based COVID-19 Discussion Thread

There’s something strange about this coronavirus pandemic. Even after months of extensive research by the global scientific community, many questions remain open.

Why, for instance, was there such an enormous death toll in northern Italy, but not the rest of the country? Just three contiguous regions in northern Italy have 25,000 of the country’s nearly 36,000 total deaths; just one region, Lombardy, has about 17,000 deaths. Almost all of these were concentrated in the first few months of the outbreak. What happened in Guayaquil, Ecuador, in April, when so many died so quickly that bodies were abandoned in the sidewalks and streets?* Why, in the spring of 2020, did so few cities account for a substantial portion of global deaths, while many others with similar density, weather, age distribution, and travel patterns were spared? What can we really learn from Sweden, hailed as a great success by some because of its low case counts and deaths as the rest of Europe experiences a second wave, and as a big failure by others because it did not lock down and suffered excessive death rates earlier in the pandemic? Why did widespread predictions of catastrophe in Japan not bear out? The baffling examples go on.

I’ve heard many explanations for these widely differing trajectories over the past nine months—weather, elderly populations, vitamin D, prior immunity, herd immunity—but none of them explains the timing or the scale of these drastic variations. But there is a potential, overlooked way of understanding this pandemic that would help answer these questions, reshuffle many of the current heated arguments, and, crucially, help us get the spread of COVID-19 under control.

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By now many people have heard about R0—the basic reproductive number of a pathogen, a measure of its contagiousness on average. But unless you’ve been reading scientific journals, you’re less likely to have encountered k, the measure of its dispersion. The definition of k is a mouthful, but it’s simply a way of asking whether a virus spreads in a steady manner or in big bursts, whereby one person infects many, all at once. After nine months of collecting epidemiological data, we know that this is an overdispersed pathogen, meaning that it tends to spread in clusters, but this knowledge has not yet fully entered our way of thinking about the pandemic—or our preventive practices.

Read: Herd immunity is not a strategy

The now-famed R0 (pronounced as “r-naught”) is an average measure of a pathogen’s contagiousness, or the mean number of susceptible people expected to become infected after being exposed to a person with the disease. If one ill person infects three others on average, the R0 is three. This parameter has been widely touted as a key factor in understanding how the pandemic operates. News media have produced multiple explainers and visualizations for it. Movies praised for their scientific accuracy on pandemics are lauded for having characters explain the “all-important” R0. Dashboards track its real-time evolution, often referred to as R or Rt, in response to our interventions. (If people are masking and isolating or immunity is rising, a disease can’t spread the same way anymore, hence the difference between R0 and R.)

Unfortunately, averages aren’t always useful for understanding the distribution of a phenomenon, especially if it has widely varying behavior. If Amazon’s CEO, Jeff Bezos, walks into a bar with 100 regular people in it, the average wealth in that bar suddenly exceeds $1 billion. If I also walk into that bar, not much will change. Clearly, the average is not that useful a number to understand the distribution of wealth in that bar, or how to change it. Sometimes, the mean is not the message. Meanwhile, if the bar has a person infected with COVID-19, and if it is also poorly ventilated and loud, causing people to speak loudly at close range, almost everyone in the room could potentially be infected—a pattern that’s been observed many times since the pandemic begin, and that is similarly not captured by R. That’s where the dispersion comes in.

There are COVID-19 incidents in which a single person likely infected 80 percent or more of the people in the room in just a few hours. But, at other times, COVID-19 can be surprisingly much less contagious. Overdispersion and super-spreading of this virus are found in research across the globe. A growing number of studies estimate that a majority of infected people may not infect a single other person. A recent paper found that in Hong Kong, which had extensive testing and contact tracing, about 19 percent of cases were responsible for 80 percent of transmission, while 69 percent of cases did not infect another person. This finding is not rare: Multiple studies from the beginning have suggested that as few as 10 to 20 percent of infected people may be responsible for as much as 80 to 90 percent of transmission, and that many people barely transmit it.

This highly skewed, imbalanced distribution means that an early run of bad luck with a few super-spreading events, or clusters, can produce dramatically different outcomes even for otherwise similar countries. Scientists looked globally at known early-introduction events, in which an infected person comes into a country, and found that in some places, such imported cases led to no deaths or known infections, while in others, they sparked sizable outbreaks. Using genomic analysis, researchers in New Zealand looked at more than half the confirmed cases in the country and found a staggering 277 separate introductions in the early months, but also that only 19 percent of introductions led to more than one additional case. A recent review shows that this may even be true in congregate living spaces, such as nursing homes, and that multiple introductions may be necessary before an outbreak takes off. Meanwhile, in Daegu, South Korea, just one woman, dubbed Patient 31, generated more than 5,000 known cases in a megachurch cluster.

Read: The pastors already planning to rebel against future shutdowns

Unsurprisingly, SARS-CoV, the previous incarnation of SARS-CoV-2 that caused the 2003 SARS outbreak, was also overdispersed in this way: The majority of infected people did not transmit it, but a few super-spreading events caused most of the outbreaks. MERS, another coronavirus cousin of SARS, also appears overdispersed, but luckily, it does not—yet—transmit well among humans.

This kind of behavior, alternating between being super infectious and fairly noninfectious, is exactly what k captures, and what focusing solely on R hides. Samuel Scarpino, an assistant professor of epidemiology and complex systems at Northeastern, told me that this has been a huge challenge, especially for health authorities in Western societies, where the pandemic playbook was geared toward the flu—and not without reason, because pandemic flu is a genuine threat. However, influenza does not have the same level of clustering behavior.

We can think of disease patterns as leaning deterministic or stochastic: In the former, an outbreak’s distribution is more linear and predictable; in the latter, randomness plays a much larger role and predictions are hard, if not impossible, to make. In deterministic trajectories, we expect what happened yesterday to give us a good sense of what to expect tomorrow. Stochastic phenomena, however, don’t operate like that—the same inputs don’t always produce the same outputs, and things can tip over quickly from one state to the other. As Scarpino told me, “Diseases like the flu are pretty nearly deterministic and R0 (while flawed) paints about the right picture (nearly impossible to stop until there’s a vaccine).” That’s not necessarily the case with super-spreading diseases.

Nature and society are replete with such imbalanced phenomena, some of which are said to work according to the Pareto principle, named after the sociologist Vilfredo Pareto. Pareto’s insight is sometimes called the 80/20 principle—80 percent of outcomes of interest are caused by 20 percent of inputs—though the numbers don’t have to be that strict. Rather, the Pareto principle means that a small number of events or people are responsible for the majority of consequences. This will come as no surprise to anyone who has worked in the service sector, for example, where a small group of problem customers can create almost all the extra work. In cases like those, booting just those customers from the business or giving them a hefty discount may solve the problem, but if the complaints are evenly distributed, different strategies will be necessary. Similarly, focusing on the R alone, or using a flu-pandemic playbook, won’t necessarily work well for an overdispersed pandemic.

Hitoshi Oshitani, a member of the National COVID-19 Cluster Taskforce at Japan’s Ministry of Health, Labour and Welfare and a professor at Tohoku University who told me that Japan focused on the overdispersion impact from early on, likens his country’s approach to looking at a forest and trying to find the clusters, not the trees. Meanwhile, he believes, the Western world was getting distracted by the trees, and got lost among them. To fight a super-spreading disease effectively, policy makers need to figure out why super-spreading happens, and they need to understand how it affects everything, including our contact-tracing methods and our testing regimes.

There may be many different reasons a pathogen super-spreads. Yellow fever spreads mainly via the mosquito Aedes aegypti, but until the insect’s role was discovered, its transmission pattern bedeviled many scientists. Tuberculosis was thought to be spread by close-range droplets until an ingenious set of experiments proved that it was airborne. Much is still unknown about the super-spreading of SARS-CoV-2. It might be that some people are super-emitters of the virus, in that they spread it a lot more than other people. Like other diseases, contact patterns surely play a part: A politician on the campaign trail or a student in a college dorm is very different in how many people they could potentially expose compared with, say, an elderly person living in a small household. However, looking at nine months of epidemiological data, we have important clues to some of the factors.

In study after study, we see that super-spreading clusters of COVID-19 almost overwhelmingly occur in poorly ventilated, indoor environments where many people congregate over time—weddings, churches, choirs, gyms, funerals, restaurants, and such—especially when there is loud talking or singing without masks. For super-spreading events to occur, multiple things have to be happening at the same time, and the risk is not equal in every setting and activity, Muge Cevik, a clinical lecturer in infectious diseases and medical virology at the University of St. Andrews and a co-author of a recent extensive review of transmission conditions for COVID-19, told me.

Read: I have seen the future—and it’s not the life we knew

Cevik identifies “prolonged contact, poor ventilation, [a] highly infectious person, [and] crowding” as the key elements for a super-spreader event. Super-spreading can also occur indoors beyond the six-feet guideline, because SARS-CoV-2, the pathogen causing COVID-19, can travel through the air and accumulate, especially if ventilation is poor. Given that some people infect others before they show symptoms, or when they have very mild or even no symptoms, it’s not always possible to know if we are highly infectious ourselves. We don’t even know if there are more factors yet to be discovered that influence super-spreading. But we don’t need to know all the sufficient factors that go into a super-spreading event to avoid what seems to be a necessary condition most of the time: many people, especially in a poorly ventilated indoor setting, and especially not wearing masks. As Natalie Dean, a biostatistician at the University of Florida, told me, given the huge numbers associated with these clusters, targeting them would be very effective in getting our transmission numbers down.

Overdispersion should also inform our contact-tracing efforts. In fact, we may need to turn them upside down. Right now, many states and nations engage in what is called forward or prospective contact tracing. Once an infected person is identified, we try to find out with whom they interacted afterward so that we can warn, test, isolate, and quarantine these potential exposures. But that’s not the only way to trace contacts. And, because of overdispersion, it’s not necessarily where the most bang for the buck lies. Instead, in many cases, we should try to work backwards to see who first infected the subject.

Because of overdispersion, most people will have been infected by someone who also infected other people, because only a small percentage of people infect many at a time, whereas most infect zero or maybe one person. As Adam Kucharski, an epidemiologist and the author of the book The Rules of Contagion, explained to me, if we can use retrospective contact tracing to find the person who infected our patient, and then trace the forward contacts of the infecting person, we are generally going to find a lot more cases compared with forward-tracing contacts of the infected patient, which will merely identify potential exposures, many of which will not happen anyway, because most transmission chains die out on their own.

The reason for backward tracing’s importance is similar to what the sociologist Scott L. Feld called the friendship paradox: Your friends are, on average, going to have more friends than you. (Sorry!) It’s straightforward once you take the network-level view. Friendships are not distributed equally; some people have a lot of friends, and your friend circle is more likely to include those social butterflies, because how could it not? They friended you and others. And those social butterflies will drive up the average number of friends that your friends have compared with you, a regular person. (Of course, this will not hold for the social butterflies themselves, but overdispersion means that there are much fewer of them.) Similarly, the infectious person who is transmitting the disease is like the pandemic social butterfly: The average number of people they infect will be much higher than most of the population, who will transmit the disease much less frequently. Indeed, as Kucharski and his co-authors show mathematically, overdispersion means that “forward tracing alone can, on average, identify at most the mean number of secondary infections (i.e. R)”; in contrast, “backward tracing increases this maximum number of traceable individuals by a factor of 2-3, as index cases are more likely to come from clusters than a case is to generate a cluster.”

Even in an overdispersed pandemic, it’s not pointless to do forward tracing to be able to warn and test people, if there are extra resources and testing capacity. But it doesn’t make sense to do forward tracing while not devoting enough resources to backward tracing and finding clusters, which cause so much damage.

Another significant consequence of overdispersion is that it highlights the importance of certain kinds of rapid, cheap tests. Consider the current dominant model of test and trace. In many places, health authorities try to trace and find forward contacts of an infected person: everyone they were in touch with since getting infected. They then try to test all of them with expensive, slow, but highly accurate PCR (polymerase chain reaction) tests. But that’s not necessarily the best way when clusters are so important in spreading the disease.

PCR tests identify RNA segments of the coronavirus in samples from nasal swabs—like looking for its signature. Such diagnostic tests are measured on two different dimensions: Are they good at identifying people who are not infected (specificity), and are they good at identifying people who are infected (sensitivity)? PCR tests are highly accurate for both dimensions. However, PCR tests are also slow and expensive, and they require a long, uncomfortable swab up the nose at a medical facility. The slow processing times means that people don’t get timely information when they need it. Worse, PCR tests are so responsive that they can find tiny remnants of coronavirus signatures long after someone has stopped being contagious, which can cause unnecessary quarantines.

Meanwhile, researchers have shown that rapid tests that are very accurate for identifying people who do not have the disease, but not as good at identifying infected individuals, can help us contain this pandemic. As Dylan Morris, a doctoral candidate in ecology and evolutionary biology at Princeton, told me, cheap, low-sensitivity tests can help mitigate a pandemic even if it is not overdispersed, but they are particularly valuable for cluster identification during an overdispersed one. This is especially helpful because some of these tests can be administered via saliva and other less-invasive methods, and be distributed outside medical facilities.

In an overdispersed regime, identifying transmission events (someone infected someone else) is more important than identifying infected individuals. Consider an infected person and their 20 forward contacts—people they met since they got infected. Let’s say we test 10 of them with a cheap, rapid test and get our results back in an hour or two. This isn’t a great way to determine exactly who is sick out of that 10, because our test will miss some positives, but that’s fine for our purposes. If everyone is negative, we can act as if nobody is infected, because the test is pretty good at finding negatives. However, the moment we find a few transmissions, we know we may have a super-spreader event, and we can tell all 20 people to assume they are positive and to self-isolate—if there are one or two transmissions, there are likely more, exactly because of the clustering behavior. Depending on age and other factors, we can test those people individually using PCR tests, which can pinpoint who is infected, or ask them all to wait it out.

Read: The plan that could give us our lives back

Scarpino told me that overdispersion also enhances the utility of other aggregate methods, such as wastewater testing, especially in congregate settings like dorms or nursing homes, allowing us to detect clusters without testing everyone. Wastewater testing also has low sensitivity; it may miss positives if too few people are infected, but that’s fine for population-screening purposes. If the wastewater testing is signaling that there are likely no infections, we do not need to test everyone to find every last potential case. However, the moment we see signs of a cluster, we can rapidly isolate everyone, again while awaiting further individualized testing via PCR tests, depending on the situation.

Unfortunately, until recently, many such cheap tests had been held up by regulatory agencies in the United States, partly because they were concerned with their relative lack of accuracy in identifying positive cases compared with PCR tests—a worry that missed their population-level usefulness for this particular overdispersed pathogen.

To return to the mysteries of this pandemic, what did happen early on to cause such drastically different trajectories in otherwise similar places? Why haven’t our usual analytic tools—case studies, multi-country comparisons—given us better answers? It’s not intellectually satisfying, but because of the overdispersion and its stochasticity, there may not be an explanation beyond that the worst-hit regions, at least initially, simply had a few unlucky early super-spreading events. It wasn’t just pure luck: Dense populations, older citizens, and congregate living, for example, made cities around the world more susceptible to outbreaks compared with rural, less dense places and those with younger populations, less mass transit, or healthier citizenry. But why Daegu in February and not Seoul, despite the two cities being in the same country, under the same government, people, weather, and more? As frustrating at it may be, sometimes, the answer is merely where Patient 31 and the megachurch she attended happened to be.

Overdispersion makes it harder for us to absorb lessons from the world, because it interferes with how we ordinarily think about cause and effect. For example, it means that events that result in spreading and non-spreading of the virus are asymmetric in their ability to inform us. Take the highly publicized case in Springfield, Missouri, in which two infected hairstylists, both of whom wore masks, continued to work with clients while symptomatic. It turns out that no apparent infections were found among the 139 exposed clients (67 were directly tested; the rest did not report getting sick). While there is a lot of evidence that masks are crucial in dampening transmission, that event alone wouldn’t tell us if masks work. In contrast, studying transmission, the rarer event, can be quite informative. Had those two hairstylists transmitted the virus to large numbers of people despite everyone wearing masks, it would be important evidence that, perhaps, masks aren’t useful in preventing super-spreading.

Comparisons, too, give us less information compared with phenomena for which input and output are more tightly coupled. When that’s the case, we can check for the presence of a factor (say, sunshine or Vitamin D) and see if it correlates with a consequence (infection rate). But that’s much harder when the consequence can vary widely depending on a few strokes of luck, the way that the wrong person was in the wrong place sometime in mid-February in South Korea. That’s one reason multi-country comparisons have struggled to identify dynamics that sufficiently explain the trajectories of different places.

Once we recognize super-spreading as a key lever, countries that look as if they were too relaxed in some aspects appear very different, and our usual polarized debates about the pandemic are scrambled, too. Take Sweden, an alleged example of the great success or the terrible failure of herd immunity without lockdowns, depending on whom you ask. In reality, although Sweden joins many other countries in failing to protect elderly populations in congregate-living facilities, its measures that target super-spreading have been stricter than many other European countries. Although it did not have a complete lockdown, as Kucharski pointed out to me, Sweden imposed a 50-person limit on indoor gatherings in March, and did not remove the cap even as many other European countries eased such restrictions after beating back the first wave. (Many are once again restricting gathering sizes after seeing a resurgence.) Plus, the country has a small household size and fewer multigenerational households compared with most of Europe, which further limits transmission and cluster possibilities. It kept schools fully open without distancing or masks, but only for children under 16, who are unlikely to be super-spreaders of this disease. Both transmission and illness risks go up with age, and Sweden went all online for higher-risk high-school and university students—the opposite of what we did in the United States. It also encouraged social-distancing, and closed down indoor places that failed to observe the rules. From an overdispersion and super-spreading point of view, Sweden would not necessarily be classified as among the most lax countries, but nor is it the most strict. It simply doesn’t deserve this oversize place in our debates assessing different strategies.

Although overdispersion makes some usual methods of studying causal connections harder, we can study failures to understand which conditions turn bad luck into catastrophes. We can also study sustained success, because bad luck will eventually hit everyone, and the response matters.

The most informative case studies may well be those who had terrible luck initially, like South Korea, and yet managed to bring about significant suppression. In contrast, Europe was widely praised for its opening early on, but that was premature; many countries there are now experiencing widespread rises in cases and look similar to the United States in some measures. In fact, Europe’s achieving a measure of success this summer and relaxing, including opening up indoor events with larger numbers, is instructive in another important aspect of managing an overdispersed pathogen: Compared with a steadier regime, success in a stochastic scenario can be more fragile than it looks.

Once a country has too many outbreaks, it’s almost as if the pandemic switches into “flu mode,” as Scarpino put it, meaning high, sustained levels of community spread even though a majority of infected people may not be transmitting onward. Scarpino explained that barring truly drastic measures, once in that widespread and elevated mode, COVID-19 can keep spreading because of the sheer number of chains already out there. Plus, the overwhelming numbers may eventually spark more clusters, further worsening the situation.

As Kucharski put it, a relatively quiet period can hide how quickly things can tip over into large outbreaks and how a few chained amplification events can rapidly turn a seemingly under-control situation into a disaster. We’re often told that if Rt, the real-time measure of the average spread, is above one, the pandemic is growing, and that below one, it’s dying out. That may be true for an epidemic that is not overdispersed, and while an Rt below one is certainly good, it’s misleading to take too much comfort from a low Rt when just a few events can reignite massive numbers. No country should forget South Korea’s Patient 31.

That said, overdispersion is also a cause for hope, as South Korea’s aggressive and successful response to that outbreak—with a massive testing, tracing, and isolating regime—shows. Since then, South Korea has also been practicing sustained vigilance, and has demonstrated the importance of backward tracing. When a series of clusters linked to nightclubs broke out in Seoul recently, health authorities aggressively traced and tested tens of thousands of people linked to the venues, regardless of their interactions with the index case, six feet apart or not—a sensible response, given that we know the pathogen is airborne.

Perhaps one of the most interesting cases has been Japan, a country with middling luck that got hit early on and followed what appeared to be an unconventional model, not deploying mass testing and never fully shutting down. By the end of March, influential economists were publishing reports with dire warnings, predicting overloads in the hospital system and huge spikes in deaths. The predicted catastrophe never came to be, however, and although the country faced some future waves, there was never a large spike in deaths despite its aging population, uninterrupted use of mass transportation, dense cities, and lack of a formal lockdown.

It’s not that Japan was better situated than the United States in the beginning. Similar to the U.S. and Europe, Oshitani told me, Japan did not initially have the PCR capacity to do widespread testing. Nor could it impose a full lockdown or strict stay-at-home orders; even if that had been desirable, it would not have been legally possible in Japan.

Oshitani told me that in Japan, they had noticed the overdispersion characteristics of COVID-19 as early as February, and thus created a strategy focusing mostly on cluster-busting, which tries to prevent one cluster from igniting another. Oshitani said he believes that “the chain of transmission cannot be sustained without a chain of clusters or a megacluster.” Japan thus carried out a cluster-busting approach, including undertaking aggressive backward tracing to uncover clusters. Japan also focused on ventilation, counseling its population to avoid places where the three C’s come together—crowds in closed spaces in close contact, especially if there’s talking or singing—bringing together the science of overdispersion with the recognition of airborne aerosol transmission, as well as presymptomatic and asymptomatic transmission.

Oshitani contrasts the Japanese strategy, nailing almost every important feature of the pandemic early on, with the Western response, trying to eliminate the disease “one by one” when that’s not necessarily the main way it spreads. Indeed, Japan got its cases down, but kept up its vigilance: When the government started noticing an uptick in community cases, it initiated a state of emergency in April and tried hard to incentivize the kinds of businesses that could lead to super-spreading events, such as theaters, music venues, and sports stadiums, to close down temporarily. Now schools are back in session in person, and even stadiums are open—but without chanting.

It’s not always the restrictiveness of the rules, but whether they target the right dangers. As Morris put it, “Japan’s commitment to ‘cluster-busting’ allowed it to achieve impressive mitigation with judiciously chosen restrictions. Countries that have ignored super-spreading have risked getting the worst of both worlds: burdensome restrictions that fail to achieve substantial mitigation. The U.K.’s recent decision to limit outdoor gatherings to six people while allowing pubs and bars to remain open is just one of many such examples.”

Could we get back to a much more normal life by focusing on limiting the conditions for super-spreading events, aggressively engaging in cluster-busting, and deploying cheap, rapid mass tests—that is, once we get our case numbers down to low enough numbers to carry out such a strategy? (Many places with low community transmission could start immediately.) Once we look for and see the forest, it becomes easier to find our way out.

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SR-71 said:

100% de chance qu'on va tous mourir un jour, Covid ou autre

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CuddlyJen said:

Please provide reliable statistics.

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SRT-What? said:

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When her Danish colleagues first suggested distributing protective cloth face masks to people in Guinea-Bissau to stem the spread of the coronavirus, Christine Benn wasn’t so sure.

“I said, ‘Yeah, that might be good, but there’s limited data on whether face masks are actually effective,’” says Benn, a global-health researcher at the University of Southern Denmark in Copenhagen, who for decades has co-led public-health campaigns in the West African country, one of the world’s poorest.

That was in March. But by July, Benn and her team had worked out how to possibly provide some needed data on masks, and hopefully help people in Guinea-Bissau. They distributed thousands of locally produced cloth face coverings to people as part of a randomized controlled trial that might be the world’s largest test of masks’ effectiveness against the spread of COVID-19.

Face masks are the ubiquitous symbol of a pandemic that has sickened 35 million people and killed more than 1 million. In hospitals and other health-care facilities, the use of medical-grade masks clearly cuts down transmission of the SARS-CoV-2 virus. But for the variety of masks in use by the public, the data are messy, disparate and often hastily assembled. Add to that a divisive political discourse that included a US president disparaging their use, just days before being diagnosed with COVID-19 himself. “People looking at the evidence are understanding it differently,” says Baruch Fischhoff, a psychologist at Carnegie Mellon University in Pittsburgh, Pennsylvania, who specializes in public policy. “It’s legitimately confusing.”

To be clear, the science supports using masks, with recent studies suggesting that they could save lives in different ways: research shows that they cut down the chances of both transmitting and catching the coronavirus, and some studies hint that masks might reduce the severity of infection if people do contract the disease.

But being more definitive about how well they work or when to use them gets complicated. There are many types of mask, worn in a variety of environments. There are questions about people’s willingness to wear them, or wear them properly. Even the question of what kinds of study would provide definitive proof that they work is hard to answer.

“How good does the evidence need to be?” asks Fischhoff. “It’s a vital question.”

Beyond gold standards
At the beginning of the pandemic, medical experts lacked good evidence on how SARS-CoV-2 spreads, and they didn’t know enough to make strong public-health recommendations about masks.

The standard mask for use in health-care settings is the N95 respirator, which is designed to protect the wearer by filtering out 95% of airborne particles that measure 0.3 micrometres (µm) and larger. As the pandemic ramped up, these respirators quickly fell into short supply. That raised the now contentious question: should members of the public bother wearing basic surgical masks or cloth masks? If so, under what conditions? “Those are the things we normally [sort out] in clinical trials,” says Kate Grabowski, an infectious-disease epidemiologist at Johns Hopkins School of Medicine in Baltimore, Maryland. “But we just didn’t have time for that.”

So, scientists have relied on observational and laboratory studies. There is also indirect evidence from other infectious diseases. “If you look at any one paper — it’s not a slam dunk. But, taken all together, I’m convinced that they are working,” says Grabowski.


Confidence in masks grew in June with news about two hair stylists in Missouri who tested positive for COVID-191. Both wore a double-layered cotton face covering or surgical mask while working. And although they passed on the infection to members of their households, their clients seem to have been spared (more than half reportedly declined free tests). Other hints of effectiveness emerged from mass gatherings. At Black Lives Matter protests in US cities, most attendees wore masks. The events did not seem to trigger spikes in infections2, yet the virus ran rampant in late June at a Georgia summer camp, where children who attended were not required to wear face coverings3. Caveats abound: the protests were outdoors, which poses a lower risk of COVID-19 spread, whereas the campers shared cabins at night, for example. And because many non-protesters stayed in their homes during the gatherings, that might have reduced virus transmission in the community. Nevertheless, the anecdotal evidence “builds up the picture”, says Theo Vos, a health-policy researcher at the University of Washington in Seattle.

More-rigorous analyses added direct evidence. A preprint study4 posted in early August (and not yet peer reviewed), found that weekly increases in per-capita mortality were four times lower in places where masks were the norm or recommended by the government, compared with other regions. Researchers looked at 200 countries, including Mongolia, which adopted mask use in January and, as of May, had recorded no deaths related to COVID-19. Another study5 looked at the effects of US state-government mandates for mask use in April and May. Researchers estimated that those reduced the growth of COVID-19 cases by up to 2 percentage points per day. They cautiously suggest that mandates might have averted as many as 450,000 cases, after controlling for other mitigation measures, such as physical distancing.

“You don’t have to do much math to say this is obviously a good idea,” says Jeremy Howard, a research scientist at the University of San Francisco in California, who is part of a team that reviewed the evidence for wearing face masks in a preprint article that has been widely circulated6.

But such studies do rely on assumptions that mask mandates are being enforced and that people are wearing them correctly. Furthermore, mask use often coincides with other changes, such as limits on gatherings. As restrictions lift, further observational studies might begin to separate the impact of masks from those of other interventions, suggests Grabowski. “It will become easier to see what is doing what,” she says.

Although scientists can’t control many confounding variables in human populations, they can in animal studies. Researchers led by microbiologist Kwok-Yung Yuen at the University of Hong Kong housed infected and healthy hamsters in adjoining cages, with surgical-mask partitions separating some of the animals. Without a barrier, about two-thirds of the uninfected animals caught SARS-CoV-2, according to the paper7 published in May. But only about 25% of the animals protected by mask material got infected, and those that did were less sick than their mask-free neighbours (as measured by clinical scores and tissue changes).

The findings provide justification for the emerging consensus that mask use protects the wearer as well as other people. The work also points to another potentially game-changing idea: “Masking may not only protect you from infection but also from severe illness,” says Monica Gandhi, an infectious-disease physician at the University of California, San Francisco.

Gandhi co-authored a paper8 published in late July suggesting that masking reduces the dose of virus a wearer might receive, resulting in infections that are milder or even asymptomatic. A larger viral dose results in a more aggressive inflammatory response, she suggests.

She and her colleagues are currently analysing hospitalization rates for COVID-19 before and after mask mandates in 1,000 US counties, to determine whether the severity of disease decreased after public masking guidelines were brought in.

The idea that exposure to more virus results in a worse infection makes “absolute sense”, says Paul Digard, a virologist at the University of Edinburgh, UK, who was not involved in the research. “It’s another argument for masks.”

Gandhi suggests another possible benefit: if more people get mild cases, that might help to enhance immunity at the population level without increasing the burden of severe illness and death. “As we’re awaiting a vaccine, could driving up rates of asymptomatic infection do good for population-level immunity?” she asks.

Back to ballistics
The masks debate is closely linked to another divisive question: how does the virus travel through the air and spread infection?

The moment a person breathes or talks, sneezes or coughs, a fine spray of liquid particles takes flight. Some are large — visible, even — and referred to as droplets; others are microscopic, and categorized as aerosols. Viruses including SARS-CoV-2 hitch rides on these particles; their size dictates their behaviour.

Droplets can shoot through the air and land on a nearby person’s eyes, nose or mouth to cause infection. But gravity quickly pulls them down. Aerosols, by contrast, can float in the air for minutes to hours, spreading through an unventilated room like cigarette smoke.

Visualization of the droplet spread when an N95 mask equipped with an exhalation port is used to impede the emerging jet.
Time-lapse images show how cough droplets spread from a person wearing an N95 mask that has a valve to expel exhaled air.Credit: S. Verma et al./Phys. Fluids

What does this imply for the ability of masks to impede COVID-19 transmission? The virus itself is only about 0.1 µm in diameter. But because viruses don’t leave the body on their own, a mask doesn’t need to block particles that small to be effective. More relevant are the pathogen-transporting droplets and aerosols, which range from about 0.2 µm to hundreds of micrometres across. (An average human hair has a diameter of about 80 µm.) The majority are 1–10 µm in diameter and can linger in the air a long time, says Jose-Luis Jimenez, an environmental chemist at the University of Colorado Boulder. “That is where the action is.”

Scientists are still unsure which size of particle is most important in COVID-19 transmission. Some can’t even agree on the cut-off that should define aerosols. For the same reasons, scientists still don’t know the major form of transmission for influenza, which has been studied for much longer.

Many believe that asymptomatic transmission is driving much of the COVID-19 pandemic, which would suggest that viruses aren’t typically riding out on coughs or sneezes. By this reasoning, aerosols could prove to be the most important transmission vehicle. So, it is worth looking at which masks can stop aerosols.

All in the fabric
Even well-fitting N95 respirators fall slightly short of their 95% rating in real-world use, actually filtering out around 90% of incoming aerosols down to 0.3 µm. And, according to unpublished research, N95 masks that don’t have exhalation valves — which expel unfiltered exhaled air — block a similar proportion of outgoing aerosols. Much less is known about surgical and cloth masks, says Kevin Fennelly, a pulmonologist at the US National Heart, Lung, and Blood Institute in Bethesda, Maryland.

In a review9 of observational studies, an international research team estimates that surgical and comparable cloth masks are 67% effective in protecting the wearer.

In unpublished work, Linsey Marr, an environmental engineer at Virginia Tech in Blacksburg, and her colleagues found that even a cotton T-shirt can block half of inhaled aerosols and almost 80% of exhaled aerosols measuring 2 µm across. Once you get to aerosols of 4–5 µm, almost any fabric can block more than 80% in both directions, she says.

Multiple layers of fabric, she adds, are more effective, and the tighter the weave, the better. Another study10 found that masks with layers of different materials — such as cotton and silk — could catch aerosols more efficiently than those made from a single material.

Benn worked with Danish engineers at her university to test their two-layered cloth mask design using the same criteria as for medical-grade ventilators. They found that their mask blocked only 11–19% of aerosols down to the 0.3 µm mark, according to Benn. But because most transmission is probably occurring through particles of at least 1 µm, according to Marr and Jimenez, the actual difference in effectiveness between N95 and other masks might not be huge.

Eric Westman, a clinical researcher at Duke University School of Medicine in Durham, North Carolina, co-authored an August study11 that demonstrated a method for testing mask effectiveness. His team used lasers and smartphone cameras to compare how well 14 different cloth and surgical face coverings stopped droplets while a person spoke. “I was reassured that a lot of the masks we use did work,” he says, referring to the performance of cloth and surgical masks. But thin polyester-and-spandex neck gaiters — stretchable scarves that can be pulled up over the mouth and nose — seemed to actually reduce the size of droplets being released. “That could be worse than wearing nothing at all,” Westman says.

Some scientists advise not making too much of the finding, which was based on just one person talking. Marr and her team were among the scientists who responded with experiments of their own, finding that neck gaiters blocked most large droplets. Marr says she is writing up her results for publication.

“There’s a lot of information out there, but it’s confusing to put all the lines of evidence together,” says Angela Rasmussen, a virologist at Columbia University’s Mailman School of Public Health in New York City. “When it comes down to it, we still don’t know a lot.”

Minding human minds
Questions about masks go beyond biology, epidemiology and physics. Human behaviour is core to how well masks work in the real world. “I don’t want someone who is infected in a crowded area being confident while wearing one of these cloth coverings,” says Michael Osterholm, director of the Center for Infectious Disease Research and Policy at the University of Minnesota in Minneapolis.

Perhaps fortunately, some evidence12 suggests that donning a face mask might drive the wearer and those around them to adhere better to other measures, such as social distancing. The masks remind them of shared responsibility, perhaps. But that requires that people wear them.

Across the United States, mask use has held steady around 50% since late July. This is a substantial increase from the 20% usage seen in March and April, according to data from the Institute for Health Metrics and Evaluation at the University of Washington in Seattle (see go.nature.com/30n6kxv). The institute’s models also predicted that, as of 23 September, increasing US mask use to 95% — a level observed in Singapore and some other countries — could save nearly 100,000 lives in the period up to 1 January 2021.

“There’s a lot more we would like to know,” says Vos, who contributed to the analysis. “But given that it is such a simple, low-cost intervention with potentially such a large impact, who would not want to use it?”

Further confusing the public are controversial studies and mixed messages. One study13 in April found masks to be ineffective, but was retracted in July. Another, published in June14, supported the use of masks before dozens of scientists wrote a letter attacking its methods (see go.nature.com/3jpvxpt). The authors are pushing back against calls for a retraction. Meanwhile, the World Health Organization (WHO) and the US Centers for Disease Control and Prevention (CDC) initially refrained from recommending widespread mask usage, in part because of some hesitancy about depleting supplies for health-care workers. In April, the CDC recommended that masks be worn when physical distancing isn’t an option; the WHO followed suit in June.

There’s been a lack of consistency among political leaders, too. US President Donald Trump voiced support for masks, but rarely wore one. He even ridiculed political rival Joe Biden for consistently using a mask — just days before Trump himself tested positive for the coronavirus, on 2 October. Other world leaders, including the president and prime minister of Slovakia, Zuzana Čaputová and Igor Matovič, sported masks early in the pandemic, reportedly to set an example for their country.

Denmark was one of the last nations to mandate face masks — requiring their use on public transport from 22 August. It has maintained generally good control of the virus through early stay-at-home orders, testing and contact tracing. It is also at the forefront of COVID-19 face-mask research, in the form of two large, randomly controlled trials. A research group in Denmark enrolled some 6,000 participants, asking half to use surgical face masks when going to a workplace. Although the study is completed, Thomas Benfield, a clinical researcher at the University of Copenhagen and one of the principal investigators on the trial, says that his team is not ready to share any results.


Benn’s team, working independently of Benfield’s group, is in the process of enrolling around 40,000 people in Guinea-Bissau, randomly selecting half of the households to receive bilayer cloth masks — two for each family member aged ten or over. The team will then follow everyone over several months to compare rates of mask use with rates of COVID-like illness. She notes that each household will receive advice on how to protect themselves from COVID-19 — except that those in the control group will not get information on the use of masks. The team expects to complete enrolment in November.

Several scientists say that they are excited to see the results. But others worry that such experiments are wasteful and potentially exploit a vulnerable population. “If this was a gentler pathogen, it would be great,” says Eric Topol, director of the Scripps Research Translational Institute in La Jolla, California. “You can’t do randomized trials for everything — and you shouldn’t.” As clinical researchers are sometimes fond of saying, parachutes have never been tested in a randomized controlled trial, either.

But Benn defends her work, explaining that people in the control group will still benefit from information about COVID-19, and they will get masks at the end of the study. Given the challenge of manufacturing and distributing the masks, “under no circumstances”, she says, could her team have handed out enough for everyone at the study’s outset. In fact, they had to scale back their original plans to enrol 70,000 people. She is hopeful that the trial will provide some benefits for everyone involved. “But no one in the community should be worse off than if we hadn’t done this trial,” she says. The resulting data, she adds, should inform the global scientific debate.

For now, Osterholm, in Minnesota, wears a mask. Yet he laments the “lack of scientific rigour” that has so far been brought to the topic. “We criticize people all the time in the science world for making statements without any data,” he says. “We’re doing a lot of the same thing here.”

Nevertheless, most scientists are confident that they can say something prescriptive about wearing masks. It’s not the only solution, says Gandhi, “but I think it is a profoundly important pillar of pandemic control”. As Digard puts it: “Masks work, but they are not infallible. And, therefore, keep your distance.”

Nature 586, 186-189 (2020)

doi: 10.1038/d41586-020-02801-8

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It is a basic instinct of infectious diseases clinicians and researchers to seek and prescribe prophylactics or treatments that are almost guaranteed to benefit the patients who entrust us with their care.

With Covid-19, this instinct can be counterproductive.

The new coronavirus travels through populations too quickly and unpredictably for us to wait to tackle it until we have devised nearly flawless solutions. The widespread implementation of imperfect prevention measures, therapies and vaccines may be the fastest way to get a handle on the crisis.

Even those communities in the United States that are faring relatively well against the virus right now are still dangerously close to a tipping point: Infection rates and deaths could shoot up again suddenly, as they did in several states this summer. But there is another possible tipping point, too, in the other direction.

With more comprehensive use of even moderately effective prevention and treatment strategies, cases of infection and deaths could decrease substantially within weeks. It would be safer then to reopen schools and relax physical distancing restrictions.

There are potential benefits to using inexpensive paper-strip tests to detect coronavirus infections, even though those are less accurate than the standard polymerase chain reaction (P.C.R.) tests, which can return a positive result for tiny amounts of the virus or long after a person has ceased to be infectious. The paper tests have a rapid turnaround time; if deployed widely and frequently, they could be an effective first-detection tool.

And here is a simple, imperfect measure that has already saved many lives: the face mask.

A mask, especially one made of cloth, is a primitive block against respiratory viruses, and in terms of efficacy it probably pales in comparison with the condom, the gold standard of barriers for preventing infectious diseases. Measuring a mask’s effectiveness at the individual level — particularly in the real world where use is intermittent and imperfect, and where people wear various types of masks — is devilishly challenging.


But based on mathematical modeling, my research group found, as it described in a recent preprint (a paper not yet peer-reviewed), that a mask worn by an infected person that filtered only 50 percent of the virus that person exhaled would lower the chances of their transmitting the virus to someone else by 10-60 percent (depending on how much of the virus the infected person carried at the time). When an infected person and another person are both masked, the chance of a transmission decreases by 40-80 percent.

According to our model, even when masking does not prevent people from getting infected, it decreases by roughly 10-fold the amount of virus to which they have been exposed, and that, in turn, may limit the likelihood that they will develop a severe form of Covid-19.

When these effects are extended to an entire population, the overall impact can be profound, and when deployed along with other measures, they could mean the difference between case numbers that suddenly skyrocket and the suppression of a local outbreak. Since superspreading events seem to be driving the pandemic, even slightly better masking practices among people who cannot avoid situations that favor clusters of outbreaks — like extended time spent in crowded and poorly ventilated settings — could bring outsize benefits.


Marginal improvements in the efficacy of masks themselves could also vastly reduce the number of new cases. Substantial investment should go to designing more protective and more comfortable masks, and marketing them with labeling that describes their level of protection as well as how best to use them.

Likewise, the widespread deployment of an even partially effective therapy could place the United States in a much safer position.

One of the true failures of our response to the pandemic has been the slow development or testing of antivirals, medicines designed to stop a virus from infecting our cells or limit dangerous levels of inflammation. Only a tiny minority of infected people worldwide have enrolled in clinical trials to date, and many of them had already suffered severe symptoms when they signed up.

In an important study of remdesivir, which cripples enzymes that viruses need to replicate themselves, the drug shortened the duration of Covid-19 symptoms in hospitalized patients by about four days. Another study of the effects of dexamethasone, a common steroid, showed a slight decrease in mortality. But with either drug, the studies suggested, only one in every 20 people who received treatment would be saved from dying.

This is not surprising. A serious case of Covid-19 is akin to an uncontrolled forest fire. Much of the lung and vascular tissue is inflamed; damage occurs in multiple organs. Treatment is more likely to succeed when it is started in the early stages of infection, when the fire is small and localized. This is the case with viruses such as influenza, Ebola, zoster and H.I.V.

Initiating treatment when the first symptoms of Covid-19 appear would not only help prevent deaths; it could also lower the hospitalization rate, relieving some of the burden on emergency departments and intensive care units.

Multiple trials are now underway to investigate early treatment for Covid-19 with antivirals — or repurposed drugs commonly used for other diseases, or antibodies to this coronavirus that have been engineered and mass produced. Yet it’s unclear whether these studies can be completed quickly enough to meaningfully lower rates of hospitalization or deaths before a vaccine is developed and widely distributed.

This is partly the case because trial drugs are usually evaluated based on whether they lower cases of hospitalization. But hospitalization rates may not be the only, nor the best, endpoint for clinical trials — not when time is pressing. Hospitalization rates for Covid-19 patients involved in early-treatment trials in the United States have tended to be below 5 percent, which means that a study that hopes to demonstrate a statistically significant difference between a drug and a placebo requires the participation of more than 1,000 people.

Endpoints other than lower hospitalization rates could be set for assessing trials; one of them could be determining whether certain drugs shorten the duration of patients’ symptoms. Such criteria would allow for still rigorous but much speedier testing involving, say, fewer than 100 participants. Smaller, nimbler studies would also promote comparison among a greater breadth of promising medicines, all with an eye toward getting effective drugs to the market as quickly as possible.

The most crucial area where the search for perfection could come at the expense of the greater good is the development, assessment and licensing of vaccines.

As with antiviral therapies, a vaccine should not be distributed to the public without its safety and efficacy having first been demonstrated in randomized double-blind, placebo-controlled clinical trials. But at issue, again, is how we choose to define efficacy.

The U.S. Food and Drug Administration typically approves vaccines that are at least 50 percent effective at preventing a disease. But even a vaccine less effective than that could substantially lower the number of cases of coronavirus infection and Covid-19-related deaths, if it were rolled out fast enough and given first to the people most likely to get infected or to infect other people. As others have argued, vaccines don’t just prevent a disease; they can stop the pathogen that causes it from being transmitted.

Mass immunization programs benefit not only the people who are vaccinated, but also everyone else, since they are less likely to come into contact with an infected person. For example, the widespread inoculation of children in the United States with a vaccine for the pneumococcus bacterium, a common cause of pneumonia, has been shown to curb deaths and hospitalizations from the disease among adults.

Similarly, some of the people most at risk of developing severe cases of Covid-19 — the elderly, the immunocompromised — may not adequately respond to a vaccine. But they could nevertheless be shielded by one if a sufficient proportion of the total population were inoculated with it.

And even a vaccine that does not protect against Covid-19 might be of enormous utility, if it causes recipients to carry less of the coronavirus and therefore, presumably, be less contagious.

The potentially huge benefits of rolling out low-sensitivity tests that can be administered quickly and frequently is increasingly being recognized as a viable path to, say, reopening college campuses and professional sports leagues. If over the next six months we can also make small, iterative gains with masking and modestly effective therapies and vaccines, then the worst of the pandemic might soon be behind us. For that to happen, though, we must avoid the temptation of seeking perfect solutions.

Dr. Joshua T. Schiffer is an associate professor in the Vaccine and Infectious Disease Division at the Fred Hutchinson Cancer Research Center and the Division of Allergy and Infectious Diseases at the University of Washington in Seattle.

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Abstract

Face masks are recommended to reduce community transmission of SARS CoV 2. One of the primary benefits of face masks and other coverings is as source control devices to reduce the expulsion of respiratory aerosols during coughing, breathing, and speaking. Face shields have been proposed as an alternative to face masks, but information about face shields as source control devices is limited. We used a cough aerosol simulator with a headform to propel small aerosol particles (0 to 7 μm) into different face coverings. An N95 respirator blocked 99% of the cough aerosol, a procedure mask blocked 59%, a 3-ply cloth face mask blocked 51%, and a polyester neck gaiter blocked 47% as a single layer and 60% when folded into a double layer. In contrast, the face shield blocked 2% of the cough aerosol. Our results suggest that face masks and neck gaiters are preferable to face shields as source control devices for cough aerosols.

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CuddlyJen said:

Grim 2.0

Manque juste des images excessivement espacées.

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