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Masks - FAQ for Skeptics

A bit of skepticism is healthy, and it’s especially reasonable given how much the official guidance on masks has varied over time and across regions. But of course, a good skeptic reads the evidence, and makes an informed decision based on that. So here’s some frequently asked questions I’ve been seeing from curious skeptics, and answers (with citations).


Why should most people wear masks?

Wearing a mask decreases the number of people infected by an infectious mask wearer (“source control”), because it reduces by around 99% the number of droplets that are ejected during speech. It also probably somewhat decreases the likelihood of an uninfected wearer getting infected, although it’s less effective for this, since many of the droplets quickly evaporate into small droplet nuclei that are hard to block. Reducing the number of people infected has an exponential impact, because it decreases the effective reproduction rate, R.

About half of infections are from people that aren’t showing symptoms – so people that don’t know they’re sick are infecting others. Because masks are far more effective at blocking infection at the source, that means we need everyone to wear a mask in public, since otherwise unmasked people put those around them at risk.

Shouldn’t only sick people wear masks?

Patients without symptoms pose a risk of infecting others, so it’s not enough to wait until you have symptoms to wear a mask. Four recent studies show that nearly half of patients are infected by people who do not themselves have symptoms—thus they aren’t even coughing or sneezing yet, but they can spread the disease just by talking in close proximity to someone else.

Shouldn’t we just follow WHO’s guidelines?

WHO says “if you are healthy, you only need to wear a mask if you are taking care of a person with COVID-19”. WHO also says that “Studies of influenza, influenza-like illness, and human coronaviruses provide evidence that the use of a medical mask can prevent the spread of infectious droplets from an infected person to someone else and potential contamination of the environment by these droplets.” Remember, you don’t know if you’re healthy, and you don’t know if the people that you’re with are healthy either. So to follow WHO’s guidelines, you really need to be wearing a mask when around others.

Many countries have been clear about this. The U.S. CDC (Center for Disease Control) recommends wearing cloth face coverings in public settings” because “a significant portion of individuals with coronavirus lack symptoms” and they can be contagious spreaders of the virus. Other countries that are officially recommending mask use include China, Japan, France, India, South Korea, Canada, Germany, Brazil, Spain, Indonesia, Israel, the Czech Republic, Singapore, South Africa, Slovenia, Bulgaria, Slovakia, Austria, Bosnia, Mongolia, Taiwan, Colombia, Philippines, Ukraine, Uzbekistan, Vietnam, Cuba, Turkey, Chile, Zambia, Rwanda, Luxembourg, Panama, Malaysia, Poland, Ecuador, Singapore, Morocco, Kenya, Venezuela, Rwanda, Nigeria, Ethiopia, Guinea, Honduras, Hong Kong, Bulgaria, Benin, Cyprus.

Many countries have gone further, and mandated mask use in most public settings, including Indonesia, Israel, the Czech Republic, Slovenia, Bulgaria, Slovakia, Austria, Bosnia, Mongolia, Taiwan, Singapore, Colombia, Poland, Panama, Philippines, Uzbekistan, Ukraine, Vietnam, Cuba, Morocco, Turkey, Kenya, Zambia, Luxembourg, Ecuador, Chile, Venezuela, Honduras, Ethiopia, Rwanda, Benin, Guinea, Parts of China, and Parts of USA (including New York, New Jersey, Maryland, Pennsylvania, Connecticut, Puerto Rico, Los Angeles, Miami, Washington DC, San Antonio, Most of Hawaii, and San Francisco).

Hopefully, WHO will update their guidelines to be clearer in the future. Their most recent guidelines say that “WHO is collaborating with research and development partners to better understand the effectiveness and efficiency of nonmedical masks. WHO is also strongly encouraging countries that issue recommendations for the use of masks in healthy people in the community to conduct research on this critical topic. WHO will update its guidance when new evidence becomes available.”

Is there a randomized controlled trial (RCT) for the impact of masks on community transmission of respiratory infections in a pandemic?

A randomized controlled trial (RCT) is sometimes considered the “gold standard” for assessing evidence to see whether a medical intervention actually works. It’s mainly used for assessing new drugs. In an RCT, a representative sample is selected, and randomly split into two groups, one of which receives the medical intervention (e.g. the drug), and one which doesn’t (normally that one gets a placebo). This can, when things go well, show clearly whether the drug made a difference. Generally, a “p value” is calculated, which is the probability that effect seen in the data would be observed by chance. If that p value is less than some number (often 0.05) the RCT is considered to be “statistically significant”. Without an RCT, it can be harder to distinguish whether two groups differ because of the intervention, or because of some other difference between the groups.

There has never been, and will never be, an RCT for the impact of masks, or hand-washing, or social distancing on community transmission of respiratory infections in a pandemic. The reason is that the following steps would be needed:

  1. Select 100 or so communities that are representative, and do not have significant population interaction (i.e people don’t move from one region to another)
  2. Select at random 50 communities where everyone must all wear masks in public; the populations of the other 50 must never wear masks in public
  3. Wait a few months
  4. See how many people died in each set of communities

Because we have such a strong prior expectation that masks are likely to be effective, there are probably no jurisdictions where it would be considered ethical to run such a study. It would also be very challenging to ensure compliance. A smaller and simpler trial that does not look at whole communities, but only individuals, would face similar ethical problems, and would also not be able to actually answer the question of whether community transmission is impacted.

The American Statistical Association (ASA) has released a “Statement on Statistical Significance and P-Values” with six principles underlying the proper use and interpretation of the p-value. In particular, note the following principles:

  • P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.
  • Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
  • A p-value, or statistical significance, does not measure the size of an effect or the importance of a result.

So what should policy decisions be based on? They should be based on an assessment of the potential upsides and downside of an intervention along with their probabilities, versus the potential cost, in order to come up with an approximate expected value (ideally, a probability distribution) if the intervention is used, versus not used.

Shouldn’t we wait for an RCT before doing something?

No. Even if we ignore the impossibility of running such an RCT, a British Medical Journal paper points out that “there is a moral argument that the public should be given the opportunity to change their behavior in line with the precautionary principle, even when direct experimental evidence for benefit is not clear cut”. The precautionary principle is (from Wikipedia) “a strategy for approaching issues of potential harm when extensive scientific knowledge on the matter is lacking.” Most nations have agreed, via UNICEF, to act in compliance with this principle.

No jurisdiction has waited for an RCT before recommending hand washing or physical distancing. Many jurisdictions have enforced extreme physical distancing through mandated lockdowns or shelter-in-place orders, despite the lack of an RCT showing their effectiveness at reducing community transmission of COVID-19, and despite this being a far more expensive intervention.

Aren’t there RCTs that show no effect of mask usage?

For an RCT to show no effect, we would need to observe two groups that are very similar, with enough data to make us confident that the effect is small enough to be not practically useful. There are no RCTs that have found this for any use of masks for any type of coronavirus.

The closest thing we have, perhaps, to a relevant RCT is the paper The First Randomized, Controlled Clinical Trial of Mask Use in Households to Prevent Respiratory Virus Transmission: This was an Australian study for influenza control in the community, but not during a pandemic, and without any enforcement of compliance (such as would be provided by a mask mandate). It stated that “observational epidemiologic data suggest that transmission of viral respiratory infection was significantly reduced during the SARS epidemic with the use of face masks as well as other infection control measures” and “in an adjusted analysis of compliant subjects, masks as a group had protective efficacy in excess of 80% against clinical influenza-like illness.” However, the authors noted that “we found compliance to be low, but compliance is affected by perception of risk. In a pandemic, we would expect compliance to improve. In compliant users, masks were highly efficacious.”

There is some evidence that basic masks are more effective for coronavirus than influenza. There is a lot of evidence that compliance with mask wearing can be high during the COVID-19 pandemic, since many communities are already at well over 80% compliance (including many at close to 100%, due to mask mandates).

Some other RCTs that are frequently discussed with regards to public mask use, none of which study impact on community transmission, include:

Doesn’t a mask need to be 100% effective to be useful?

No. No mask is 100% effective. However, fewer virus particles mean a better chance of avoiding infection, and research shows that, if you are infected, the lower your viral exposure load, the better your chance of only a mild illness.

Do we really know if the virus is transmitted through the air?

I claimed earlier that a mask works “because it reduces by around 99% the amount of droplets that are ejected during speech”. That’s only useful if we know that the virus is actually transmitted through the air. We have to be careful here, because many people (including the WHO) use a definition of “airborne” that does not include transmission caused by droplets traveling through the air, but only tiny particles that float freely in the air for hours. We don’t know for sure whether the virus is “airborne” under this definition. However, it does seem clear that a key transmission route of COVID-19 is via droplets that fly out of our mouths. It has been known since 1934 (and studied in hundreds of papers since) that respiratory infections are transmitted through these droplets, and that the smaller ones quickly evaporate. We’ve known since 1946 (and studied in hundreds of papers since) that this creates tiny particles that are extremely hard to stop. This mode of respiratory infection is well understood, and consistent with the transmission of SARS.

Unfortunately, WHO makes things rather confusing, through their publication Modes of transmission of virus causing COVID-19. This document claims that “According to current evidence, COVID-19 virus is primarily transmitted between people through respiratory droplets and contact routes.” Five references provided to support this assertion (the WHO page currently lists 6, but it appears that the reference numbers are off by one, since the following reference does not relate to the correct document). However, a review of the references shows that none of them provide evidence supporting any particular transmission route. These are the references provided:

  • Community Transmission of Severe Acute Respiratory Syndrome Coronavirus 2, Shenzhen, China, 2020: “We suspect that community transmission and intrafamily transmission have potentially become the new transmission modes in the city. Also, nosocomial infection and transmission might pose a potential risk for COVID-19 control.” No evidence of whether transmission was through droplets, Aerosol, or fomites is presented.
  • A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: The focus of this study was to identify whether the key issue to analyze and control is super-spreader events, or whether other types of transmission could be a problem. “Our study showed that person-to-person transmission in family homes or hospitals, and intercity spread of this novel coronavirus are possible”. No analysis of transmission methods was done, however there is some evidence here that wearing a mask is effective, because the only person that was not infected in the family in this case was the only one to wear a mask.
  • Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia: This study only looked at “infected persons, relatives, close contacts, and health care workers”. It did not attempt to identify the method of transmission or find out whether transmission to more remote people occurred.
  • Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China: this study looks at “the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes” of patients. It does not look at or comment on transmission method in any way.
  • Active Monitoring of Persons Exposed to Patients with Confirmed COVID-19: “despite intensive follow-up, no sustained person-to-person transmission of symptomatic SARS-CoV-2 was observed in the United States among the close contacts of the first 10 persons with diagnosed travel-related COVID-19. Analyses of timing of exposure during each patient’s illness as well as the type and duration of exposures will provide information on potential risk factors for transmission.” This analysis was not done in this paper, so therefore it does not provide any information about transmission methods.

However, there are cases reported in the literature which support the conclusion that the virus is transmitted through the air. In particular, the paper COVID-19 Outbreak Associated with Air Conditioning in Restaurant, Guangzhou, China, 2020, notes that “Virus transmission in this outbreak cannot be explained by droplet transmission alone. Larger respiratory droplets (>5 μm) remain in the air for only a short time and travel only short distances, generally <1 m”. Indirect Virus Transmission in Cluster of COVID-19 Cases, Wenzhou, China, 2020 notes that “the rapid spread of SARS-CoV-2 in our study could have resulted from spread via fomites (e.g., elevator buttons or restroom taps) or virus aerosolization in a confined public space (e.g., restrooms or elevators). All case-patients other than those on floor 7 were female, including a restroom cleaner, so common restroom use could have been the infection source. For case-patients who were customers in the shopping mall but did not report using the restroom, the source of infection could have been the elevators. The Guangzhou Center for Disease Control and Prevention detected the nucleic acid of SARS-CoV-2 on a doorknob at a patient’s house (5), but Wenzhou Center for Disease Control and Prevention test results for an environmental sample from the surface of a mall elevator wall and button were negative”. Other cases not yet reported in the scientific literature include a Seattle choir rehearsal where 45 have been diagnosed with COVID-19 or ill with the symptoms, despite all attendees required to use hand sanitizer, and a conference in Boston where 77 people became infected. Locations that had sporting events and festivals, where there tends to be singing and shouting, which eject larger and more droplets further distances, have had large COVID-19 outbreaks.

Perhaps the most interesting case for understanding transmission routes is a call center building in Guro-gu, Seoul, There are a total of 163 confirmed cases since 8 March. Of the 163 confirmed cases, 97 are persons who worked in the building (11th floor = 94; 10th floor = 2; 9th floor = 1), and 66 are their contacts.” So nearly all the cases in the building were on a single floor. This strongly suggests that transmission must be mainly through the air, otherwise, if transmission was primarily through touching surfaces, elevator buttons and front doors etc would have caused substantial transmission to other floors.

I have only been able to find a single case that seems likely to be caused by infection through surface contact. In this case, a person in Singapore who sat in a church pew that was earlier used by an infected patient became infected themselves. It’s possible that the virus was transmitted through the pew, although it’s also possible that since the people went to the same church, they came into contact before or after the service.

If it’s spread through the air, can a cloth mask really stop it? Isn’t the virus too small?

Coronavirus particles are so small that they can fit through the weave of most household cloth materials. Medical masks, such as N95 respirators, use special materials that create difficult to navigate pathways in the fabric that make it very hard for these tiny particles to get through the material. They also are specially fitted to the face of each healthcare worker to minimize gaps that these particles can get through.

Many commentators have been distracted by this, not realizing that the droplets that are ejected from an infected mask wearer are far bigger than the virus particles, and are easily blocked with around 99% efficacy, as shown in this recent NEJM paper that used laser light scattering to explore the effect. (The paper includes videos that make it easy to see for yourself what’s going on.)

We don’t know for sure yet whether droplets ejected during breathing are also an important transmission path. These droplets are much smaller, and have a lower total viral load, compared to droplets from speech, but I haven’t found any studies that directly study the impact of this on COVID-19 transmission.

The good news, however, is that we do have a study that shows the impact of wearing an unfitted mask on seasonal coronavirus transmission, based on the amount of virus particles found in droplets ejected during breathing. In this study, the unfitted mask was 100% effective in blocking these for seasonal coronavirus.

There’s even a study that tested the efficacy of a cloth mask at blocking COVID-19. Unfortunately, there are some problems with the study:

  • Symptomatic people should stay home, so testing speech would have been more helpful than testing coughs
  • The test was done at 8 inches, which is much closer than people should be if following physical distancing guidelines
  • Only 4 patients were tested
  • The data analysis was not done correctly.

We can’t fix the first three problems, but we can fix the fourth. When we do, we find that over 95% of the viral load was blocked by the cloth masks.

To improve protection for the wearer, a coffee filter or paper towel can be easily inserted into the mask to improve filtering. For instance, the Hong Kong Consumer Council recommended design includes a paper towel, after scientists “scanned kitchen paper towel under electronic microscope and revealed that the fabric size, gaps and layout of kitchen paper towel are similar to that of the middle layer of surgical mask”.

I heard a doctor say that masks don’t help. Is that true?

No. In fact, many of the top doctors around the world are speaking up and telling the public to use masks, including an open letter from 100 doctors in Britain, Canada’s chief public health officer, and the US Surgeon General. The official position of the peak medical bodies in 11 of the top 12 (by GDP) countries is that masks can be used to reduce transmission (the outlier being the UK, which is expected to change its advice in the coming days).

Some doctors are taking longer to change their ways. Unfortunately, Western doctors don’t have a great track record of accepting the science of public health hygiene. The scientist who discovered the importance of hand-washing, Ignaz Semmelweis, was mocked and ignored by doctors at the time, and for decades afterwards. Before introducing hand washing, “puerpual fever” was killing hundreds of mothers a year in his unit. Afterwards, says Carl Zimmer, he “brought the death rate pretty much to zero. I mean, he couldn’t completely eliminate it, but he got pretty close. There were some months where like no women died at all. None.”

In the same way, Western doctors didn’t believe the young Malaysian-Chinese doctor, Wu Lien-teh, who realized that the 1910 Manchurian Plague was transmitted through the air, and that a simple cotton mask could reduce transmission. The podcast 99% Invisible explains: “Wu believed that all of his medical staff, as well as the general public, should wear masks, but other doctors wouldn’t listen to him possibly because of his young age and race. One French doctor named Gérald Mesny openly antagonized him and refused to wear his mask. Soon after, Mesny caught the plague and died vindicating Wu’s theory. After Mesny’s death, everyone began wearing Wu’s mask. People began photographing it and it became a symbol of medical success and the plague ended after 7 months. The government implemented a lot of epidemic practices that we still see today—wearing masks, quarantining patients, and cutting off travel to limit exposure.”

Won’t wearing masks make people just be less careful about physical distancing?

There is no evidence that mask use reduces compliance with other recommended strategies, such as physical distancing. Anecdotal evidence suggests that wearing masks is a useful reminder of the gravity of the situation, and may remind others to keep their distance. Historically, public health initiatives such as seat belts, condoms, and motorbike helmets are generally associated with concerns about negative outcomes due to increasing risky behavior, but overall population results have not borne out these concerns in practice.

As our paper explains (section B.2; see paper for citation details):

“One concern around public health messaging promoting the use of face-covering has been that members of the public may use risk compensation behavior and neglect physical distancing based on overvaluing the protection a surgical mask may offer due to an exaggerated or false sense of security (49). Similar arguments have previously been made for HIV prevention strategies (50) (51) and other safety devices and mandates such as motorcycle helmet laws (52) and seat-belts (53). However, research on these topics finds no such increase in adverse outcomes at the population level but rather improvements in safety and well-being, suggesting that even if risk compensation occurs in some individuals, that effect is dwarfed by the increased safety at the population level (53, 54). Further, even for deliberately high-risk recreational activities such as alpine skiing and snowboarding, wearing a helmet was generally associated with risk reduction oriented behavior (55), suggesting safety devices are both compatible with and perhaps encourage safety-oriented behavior. Even for high-risk recreational activities like alpine skiing and snowboarding, helmet use has greatly reduced injury rates (56).

In general, various forms of risk compensation theories have been proposed for many different safety innovations, but have not been found to have empirical support (57) at the population level. These findings strongly suggest that, instead of withholding a preventative tool, accompanying it with accurate messaging that combines different preventative measures would display trust in the general public’s ability to act responsibly and empower citizens, and risk compensation is unlikely to undo the positive benefits at the population level (58).”

Mightn’t people handle their masks wrong and make things worse?

As discussed, it appears that transmission of COVID-19 through surfaces is very rare. There are no reported cases that I’ve found that show transmission through an infected mask. Since there are now hundreds of millions of people around the world required to wear masks in public, we would expect to have seen examples of this by now.

The idea that wearing masks could increase risk due to touching it doesn’t stand up to scrutiny. If your mask has virus particles in it, there’s two possibilities for how they got there:

  • You’re already infected, in which case this isn’t really an issue, or
  • You’re not already infected, and the virus particles came from someone else, which means that your mask stopped them from going into your mouth.

COVID-19 is transmitted through the inside of the mouth, nose, or the eyes. If a mask stops virus particles from entering your mouth, then it’s done its job. People should be told to wash their mask when they get home, to minimize the chance that they get infected through an infected surface.

What if people touch their face more and infect themselves in the process?

As discussed, COVID-19 is transmitted through the inside of the mouth, nose, or the eyes. A mask covers the mouth and nose, making it much harder to accidentally touch them. People should be encouraged to avoid touching their mouth and nose regardless of whether or not they are wearing a mask.

Where am I going to get a mask anyway?

Masks can be made by cutting the ends off a sock, stapling rubber bands to a piece of kitchen towel, cutting the arms of a t-shirt, folding a handkerchief over hair-ties or rubber bands, by using a scarf or bandana, and so forth. There is no evidence to suggest that any of these masks are not effective at blocking droplets from an infected person.

Won’t this make people take masks away from healthcare workers?

Simple homemade cloth masks made of cut up cotton t shirts, paper towels, a handkerchief, etc are very effective for source control, so there’s no need to take medical masks away from healthcare workers. In regions that have mandated mask usage, most people are wearing DIY masks, not medical masks.

What about the article “Masks-for-all for COVID-19 not based on sound data”?

On April 1st, a retired professor, Lisa Brosseau, and Margaret Sietsema, an Assistant Professor of Environmental and Occupational Health Sciences, wrote an online commentary titled Masks-for-all for COVID-19 not based on sound data. The article is full of uncited claims, falsehoods, and misunderstandings, and wouldn’t normally be something that would be taken seriously and need to be discussed in any detail. However, it has been pushed heavily though social media and medical mailing lists. Therefore I’ll look at its claims here in detail.

The first section “Data lacking to recommend broad mask use” claims that “Sweeping mask recommendations… will not reduce SARS-CoV-2 transmission, as evidenced by the widespread practice of wearing such masks in Hubei province”. No references or data are provided to back up this claim. Looking at the actual data, however, shows that it supports the opposite conclusion - that masks may have been critical in controlling the Hubei outbreak. A report from Guo Yi in HK01 pointed out that up until Jan 22 most people in Wuhan were not wearing a mask. The next day, the government started requiring masks in public. Wuhan had their peak number of cases on Feb 4, and since then case numbers have been decreasing. Clearly, the evidence here does not support the conclusion that masks were ineffective. Such a broad claim as “mask recommendations will not reduce transmission”, made without any caveats, on the basis of a single location, made without data or references, where the actual data shows the opposite of the claimed result, suggests that this piece of writing may not have been carefully researched or reviewed.

The next claim made is that “Our review of relevant studies indicates that cloth masks will be ineffective at preventing SARS-CoV-2 transmission, whether worn as source control or as PPE”. This statement is made without any citations. The article actually presents no studies that provide evidence that cloth masks will be ineffective at preventing SARS-CoV-2 transmission.

The next claim made is “respirators, though, are the only option that can ensure protection for frontline workers dealing with COVID-19 cases”. This is incorrect. Respirators can not ensure protection. In fact, nothing can ensure protection. However, it would be accurate to say that respirators provide the best practical protection for frontline workers. However this bears no relation to the topic of their article, which purports to be about “masks for all”, not protection for frontline workers. Unfortunately, there is a shortage of respirators at present, which is why people are looking at what other options might be useful, rather than making idealistic gestures about would is best. (This point is mentioned in the article.) Furthermore, many respirators have a valve which makes them useless for source control, and therefore greatly reduces their effectiveness at reducing transmission.

The next section is titled “Filter efficiency and fit are key for masks, respirators”. However, this is only accurate when a mask is used for protecting the wearer (PPE) rather than those around the wearer (source control). This section makes many uncited claims, which I won’t bother discussing since no data or research is provided to support them. Focusing instead on those claims with references, various studies are presented which look at how much salt and aerosol particles flow through various fabrics at various pressures and particle sizes. There is no evidence that these simulations have any relationship to actual COVID-19 transmission. The same is true of the studies of fit that are presented. The claims are entirely unrelated to efficacy for source control, since droplets do not evaporate into droplet nuclei before hitting the cloth in the mask, due to the humid environment created by the mask. In practice, a cloth mask has around 99% efficacy at blocking droplets.

The most important section is next: “We found no well-designed studies of cloth masks as source control in household or healthcare settings”. Even if the failure of the authors was due to a lack of studies, rather than a research failure on their part, this does not support their contention that “cloth masks will be ineffective at preventing SARS-CoV-2 transmission”. Indeed, their own research here shows that they do not know whether cloth masks will be effective. In the next paragraph they make a similar error, claiming “may also have very limited utility as source control or PPE in households”, despite not having any data or research to support this claim. In fact, as we’ve seen, the research actually suggests that both cloth masks and surgical masks might be highly effective.

The next section “Surgical masks as source control” claims “Household studies find very limited effectiveness of surgical masks at reducing respiratory illness in other household members”, and cites 4 references. However, none of the references make this claim or show data to support this contention. Their reference 22, for instance, is a meta analysis in which section 3.4 lists the results of each analysis they looked at, and concludes “If the randomized control trial and cohort study were pooled with the case–control studies, heterogeneity decreased and a significant protective effect was found”. However, most of the studies were underpowered and were unable to distinguish between large, small, or negative protection. There was no study that found limited effectiveness (note that “not significant” is a statistical measure related to the amount of data in the study–it doesn’t imply that effectiveness was limited). Reference 23 finds “There is some evidence to support the wearing of masks or respirators during illness to protect others”. Reference 24 again finds many under-powered studies, but finds “Eight of nine retrospective observational studies found that mask and/or respirator use was independently associated with a reduced risk of severe acute respiratory syndrome”. Reference 25 does not relate to household use, despite their use of it as a reference here. The authors concludes “In sum, wearing surgical masks in households appears to have very little impact on transmission of respiratory disease”, however, none of the references provided support this claim.

In the section “Cloth masks as PPE”, the authors claim “A randomized trial comparing the effect of medical and cloth masks on healthcare worker illness found that those wearing cloth masks were 13 times more likely to experience influenza-like illness than those wearing medical masks.” However, this assertion is incorrect. The setting was actually 90% rhinovirus, which it has been found is ineffective for filtering with cloth masks. However, COVID-19 is not rhinovirus, and unlike rhinovirus is actually filtered effectively with cloth. In addition, the study referenced did not just compare surgical masks with cloth masks, but compared a regular supply of 2 new medical masks per day, with just 5 masks for a 4 week period. This is clearly inappropriate in the hot, busy, healthcare setting that was studied here.

Isn’t wearing a mask a personal choice?

The Republican governor of Maryland, Larry Hogan, said “Some people have said that covering their faces infringes on their rights, but this isn’t just about your rights or protecting yourself; it’s about protecting your neighbors. And the best science that we have shows that people might not know that they’re carriers of the virus, through no fault of their own, and they could infect other people. Spreading this disease infringes on your neighbors’ rights.”

Making a “personal choice” to not wear a mask can put those around you at risk.

(Some people genuinely can’t safely wear a mask, of course, but that is a separate issue.)

Mightn’t wearing a mask cause people of color to get harassed?

Mandating universal mask wearing, rather than just recommending mask use, may have additional benefits such as reducing stigma. From our paper (section B.2; see paper for citation details):

For many infectious diseases, including, for example, tuberculosis, health authorities recommend masks only for those infected or people who are taking care of someone infected. However, research shows that many sick people are reluctant to wear a mask if it identifies them as sick, and thus end up not wearing them at all in an effort to avoid the stigma of illness (60, 61). Stigma is a powerful force in human societies, and many illnesses come with stigma for the sick as well as fear of them, and managing the stigma is an important part of the process of controlling epidemics as stigma also leads to people avoiding treatment as well as preventive measures that would “out” their illness (62). Many health authorities have recommended wearing masks for COVID-19 only if people are sick; however, reports of people wearing masks being attacked, shunned and stigmatized have already been observed (63). Having masks worn only by the suspected/confirmed infected also has led to employers in high-risk environments like grocery stores and prisons, and even hospitals, banning employees from wearing one sometimes with the idea that it would scare the customer or the patients (64, 65). Further, in many countries, minorities suffer additional stigma and assumptions of criminality (66). In that vein, black people in the United States have reported that they were reluctant to wear masks in public during this pandemic for fear of being mistaken as criminals (67, 68).

Isn’t wearing a mask something that only Asian cultures do?

Many Western regions have now mandated mask wearing in many public places, including many parts of the USA and many countries in Europe. There is no sign that Westerners are unable or unwilling to wear masks.

Masks for all? The science says yes.

*Update: Jeremy has now written an article about masks in The Conversation lengthy FAQ. Trisha and Jeremy are two of over hundred of the world’s top academics who released an open letter to all U.S. governors asking that “officials require cloth masks to be worn in all public places, such as stores, transportation systems, and public buildings.” *

Confused about mask wearing? Sure, it’s complicated. But not as complicated as some people imply. We’ve been looking at the science (see our papers Face Masks Against COVID-19: An Evidence Review — with 104 references! — and Masks for the public: laying straw men to rest). Here’s a summary of the different streams of evidence, and our take on what it all means.


We’d love your help translating this article! Please at-mention @jeremyphoward on Twitter with your translation, and I’ll retweet it and add it here.

The epidemiology of disease spread

You’ve probably seen the videos of closely-packed dominos and mousetraps, where a single item fires off a huge cascade. The closer the dominos (or mousetraps), the more chaos gets generated. Every infectious disease has a transmission rate (R0). A disease with an R0 of 1.0 means that every infected person, on average, infects one other person. A disease whose R0 is less than 1.0 will die out. The strain of flu that caused the 1918 pandemic had an R0 of 1.8. The R0 of the virus which causes COVID-19 was estimated at 2.4 by Imperial College researchers, although some research suggests it could be as high as 5.7. This means that without containment measures, COVID-19 will spread far and fast. Importantly, COVID-19 patients are most infectious in the early days of the disease (To et al. 2020; Zou et al. 2020; Bai et al. 2020; Zhang et al. 2020; Doremalen et al. 2020; Wei 2020), during which they generally have few or no symptoms.

The physics of droplets and aerosols

When you speak, tiny micro droplets are ejected from your mouth. If you’re infectious, these contain virus particles. Only the very largest droplets end up surviving more than 0.1 s before drying out and turning into droplet nuclei (Wells 1934; Duguid 1946; Morawska et al. 2009) that are 3-5 times smaller than the original droplet itself, but still contain some virus.

That means that it’s much easier to block droplets just as they come out of your mouth, when they’re much larger, compared to blocking them as they approach the face of a non-infected person who is on the receiving end of those droplets. But this isn’t what most researchers have been looking at…

The material science of masks

Debates about the effectiveness of masks often assume that the purpose of the mask is to protect the wearer, since this is what all doctors learn about in medical school. Cloth masks are relatively poor (though not entirely ineffective) at this. For 100% protection, the wearer needs a properly fitted medical respirator (such as an N95). But cloth masks, worn by an infected person are highly effective at protecting the people around them. This is known as “source control”. And it is source control that matters in the debate about whether the public should wear masks.

If you have COVID-19 and cough on someone from 8 inches away, wearing a cotton mask will reduce the amount of virus you transmit to that person by over 90%, and is even more effective than a surgical mask. The researchers who did this experiment described the reduction as “ineffective”, partially based on an inappropriate analysis in which the patients where the cotton masks were perfectly effective were deleted. We disagree with their conclusion. It means you’ll transmit less than 1/10th of the amount of virus you would otherwise have done, decreasing the viral load, which is likely to lead to a lower probability of infection, and fewer symptoms if infected.

The mathematics of transmission

Mathematical modeling by our team, supported by other research (Yan et al. 2019), suggests that if most people wear a mask in public, the transmission rate (“effective R”) can go beneath 1.0, entirely stopping the spread of the disease. The mask doesn’t have to block every single viral particle, but the more particles it blocks, the lower the effective R.

Modelled impact of mask use on reproduction rate
Modelled impact of mask use on reproduction rate

Just how effective mask-wearing is depends on three things illustrated in the diagram: how well the mask blocks the virus (‘efficacy’: horizontal axis), what proportion of the public wear masks (‘adherence’: vertical axis), and the transmission rate of the disease (R0: the black lines on the graph). The blue area of the graph indicates an R0 below 1.0, which is what we need to achieve to wipe out the disease. If the mask blocks 100% of particles (the far right of the graph), even low adherence rates will lead to containment of the disease. Even if masks block a much lower proportion of viral particles, the disease could still be contained – but only if most or all people wear masks.

The political science of mask-wearing

How do you get all or most people to wear masks? Well, you can educate them and try to persuade them, but a more effective approach is to require them to wear a mask, either in specific settings such as public transportation or grocery stores or even at all times outside the home. Research on vaccination (Bradford and Mandich 2015) shows that jurisdictions which set a higher bar for vaccine exemptions have higher vaccination rates. The same approach is now being used to increase mask wearing compliance, and early results (Leffler et al. 2020) suggest that these laws are effective at increasing compliance and slowing or stopping the spread of COVID-19.

Mask-wearing experiments: artificial and natural

An artificial experiment is when a researcher allocates people (usually at random – hence the term ‘randomized controlled trial’ or RCT) to either wearing a mask or not wearing a mask (the control group). There have been no RCTs of mask-wearing by members of the public in COVID-19. RCTs of mask-wearing to prevent other diseases (such as influenza or tuberculosis) have tended to show a small effect which in many studies was not statistically significant. In most such studies, people assigned to the mask-wearing group didn’t always wear their masks.

A natural experiment is when we study something that is really happening – for example when a country introduces a policy of wearing masks. South Korea, for example, had rapid community spread that tracked the trajectory in Italy in the initial weeks. Then, in late February 2020, the government provided a regular supply of masks to every citizen. From that point, everything changed. As Italy’s death count accelerated to horrific levels, South Korea’s actually started decreasing. Here’s South Korea’s number of reported cases (red), and Italy’s (blue); take a close look at what happened in early March, as the impact of the mask distribution kicked in (this South Korean analysis is thanks to Hyokon Zhiang and visualization by Reshama Shaikh:

Comparison of COVID-19 cases between Korea and Italy
Comparison of COVID-19 cases between Korea and Italy

Natural experiments are scientifically imperfect, because there is no direct control group so we can’t be sure that any change is due to the masks. In some countries that introduced mask-wearing, other measures such as strict social distancing, school closures, and cancellation of public events happened at around the same time. Even in these cases, we can find relevant comparisons. For instance, European neighbors Austria and Czechia introduced social distancing requirements on the same date, but Czechia also introduced mandatory mask wearing. The Austrian case rate continued its upward trajectory, whilst Czechia’s flattened out. It wasn’t until Austria also introduced mask laws weeks later that the two counties returned to similar trajectories.

Comparison of COVID-19 cases between Czechia and Austria
Comparison of COVID-19 cases between Czechia and Austria

Importantly, in every country and every time period where mask usage has been encouraged through laws, or where masks were provided to citizens, case and death rates have fallen.

The behavioral science of mask wearing

Some have claimed that making (or strongly encouraging) people to wear masks will encourage risky behavior (Brosseau et al. 2020) (for example, going out more, washing hands less), with a net negative result, and this effect was seen in some experimental trials of masks. Similar arguments have previously been made for HIV prevention strategies (Cassell et al. 2006; Rojas Castro, Delabre, and Molina 2019) and motorcycle helmet laws (Ouellet 2011). However, real-world research on these topics found that even though some individuals responded with risky behavior, at a population level there was an overall improvement in safety and well-being (Peng et al. 2017; Houston and Richardson 2007).

The economics of mask-wearing

Economic analyses consider how much it costs to provide masks with how much value (both financial and non-financial) might be created – and, potentially, lost – if they are provided. Such economic studies (Abaluck et al. 2020) indicate that each mask worn by one person (which costs almost nothing) could generate economic benefits of thousands of dollars and save many lives.

The anthropology of mask-wearing

Mask-wearing by the public has been normalized in many Asian countries, partly for individual reasons (to protect against pollution) and partly for collective ones (as a result of recent MERS and SARS epidemics). My mask protects you; yours protects me. However, in most of these countries the norm has been to only wear a mask if you have symptoms; it’s only in recent weeks, as awareness of asymptomatic spread has become better understood, that mask wearing regardless of symptoms has become common.


Whilst not every piece of scientific evidence supports mask-wearing, most of it points in the same direction. Our assessment of this evidence leads us to a clear conclusion: keep your droplets to yourself – wear a mask.

You can make one at home, from a t-shirt, handkerchief, or paper towel, or even just wrap a scarf or bandana around your face. Ideally, use tightly woven fabric that you can still breathe through. Researchers recommend including a layer of paper towel as a disposable filter; you can simply slide it between two layers of cloth. There is no evidence that your mask needs to be made with any particular expertise or care to be effective for source control. You can put a cloth mask in the laundry and reuse it, just like you re-use a t-shirt.

If it turns out that you’re incubating COVID-19, the people you care about will be glad you wore a mask.

Epilogue: Jeremy’s Illustration of Source Control

Here’s a little illustration of source control from Jeremy!


  • Abaluck, Jason, Judith A. Chevalier, Nicholas A. Christakis, Howard Paul Forman, Edward H. Kaplan, Albert Ko, and Sten H. Vermund. 2020. “The Case for Universal Cloth Mask Adoption and Policies to Increase Supply of Medical Masks for Health Workers.” SSRN Scholarly Paper ID 3567438. Rochester, NY: Social Science Research Network. https://papers.ssrn.com/abstract=3567438.
  • Bai, Yan, Lingsheng Yao, Tao Wei, Fei Tian, Dong-Yan Jin, Lijuan Chen, and Meiyun Wang. 2020. “Presumed Asymptomatic Carrier Transmission of Covid-19.” Jama.
  • Bradford, W David, and Anne Mandich. 2015. “Some State Vaccination Laws Contribute to Greater Exemption Rates and Disease Outbreaks in the United States.” Health Affairs 34 (8): 1383–90.
  • Brosseau, Lisa M., ScD, Margaret Sietsema, PhD Apr 01, and 2020. 2020. “COMMENTARY: Masks-for-All for COVID-19 Not Based on Sound Data.” CIDRAP. https://www.cidrap.umn.edu/news-perspective/2020/04/commentary-masks-all-covid-19-not-based-sound-data.
  • Cassell, Michael M, Daniel T Halperin, James D Shelton, and David Stanton. 2006. “Risk Compensation: The Achilles’ Heel of Innovations in Hiv Prevention?” Bmj 332 (7541): 605–7.
  • Doremalen, Neeltje van, Trenton Bushmaker, Dylan H. Morris, Myndi G. Holbrook, Amandine Gamble, Brandi N. Williamson, Azaibi Tamin, et al. 2020. “Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1.” New England Journal of Medicine 0 (0): null. https://doi.org/10.1056/NEJMc2004973.
  • Duguid, JP. 1946. “The Size and the Duration of Air-Carriage of Respiratory Droplets and Droplet-Nuclei.” Epidemiology & Infection 44 (6): 471–79.
  • Houston, David J, and Lilliard E Richardson. 2007. “Risk Compensation or Risk Reduction? Seatbelts, State Laws, and Traffic Fatalities.” Social Science Quarterly 88 (4): 913–36.
  • Leffler, Christopher, Edsel Ing, Craig A. McKeown, Dennis Pratt, and Andrzej Grzybowski. 2020. “Country-Wide Mortality from the Novel Coronavirus (COVID-19) Pandemic and Notes Regarding Mask Usage by the Public.”
  • Morawska, LJGR, GR Johnson, ZD Ristovski, Megan Hargreaves, K Mengersen, Steve Corbett, Christopher Yu Hang Chao, Yuguo Li, and David Katoshevski. 2009. “Size Distribution and Sites of Origin of Droplets Expelled from the Human Respiratory Tract During Expiratory Activities.” Journal of Aerosol Science 40 (3): 256–69.
  • Ouellet, James V. 2011. “Helmet Use and Risk Compensation in Motorcycle Accidents.” Traffic Injury Prevention 12 (1): 71–81.
  • Peng, Yinan, Namita Vaidya, Ramona Finnie, Jeffrey Reynolds, Cristian Dumitru, Gibril Njie, Randy Elder, et al. 2017. “Universal Motorcycle Helmet Laws to Reduce Injuries: A Community Guide Systematic Review.” American Journal of Preventive Medicine 52 (6): 820–32.
  • Rojas Castro, Daniela, Rosemary M Delabre, and Jean-Michel Molina. 2019. “Give Prep a Chance: Moving on from the ‘Risk Compensation’ Concept.” Journal of the International AIDS Society 22: e25351.
  • To, Kelvin Kai-Wang, Owen Tak-Yin Tsang, Wai-Shing Leung, Anthony Raymond Tam, Tak-Chiu Wu, David Christopher Lung, Cyril Chik-Yan Yip, et al. 2020. “Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study.” Lancet Infect. Dis. 0 (0). https://doi.org/10.1016/S1473-3099(20)30196-1.
  • Wei, Wycliffe E. 2020. “Presymptomatic Transmission of SARS-CoV-2 — Singapore, January 23–March 16, 2020.” MMWR. Morbidity and Mortality Weekly Report 69. https://doi.org/10.15585/mmwr.mm6914e1.
  • Wells, WF. 1934. “On Air-Borne Infection: Study Ii. Droplets and Droplet Nuclei.” American Journal of Epidemiology 20 (3): 611–18.
  • Yan, Jing, Suvajyoti Guha, Prasanna Hariharan, and Matthew Myers. 2019. “Modeling the Effectiveness of Respiratory Protective Devices in Reducing Influenza Outbreak.” Risk Analysis 39 (3): 647–61. https://doi.org/10.1111/risa.13181.
  • Zhang, Juanjuan, Maria Litvinova, Wei Wang, Yan Wang, Xiaowei Deng, Xinghui Chen, Mei Li, et al. 2020. “Evolving Epidemiology and Transmission Dynamics of Coronavirus Disease 2019 Outside Hubei Province, China: A Descriptive and Modelling Study.” The Lancet Infectious Diseases 0 (0). https://doi.org/10.1016/S1473-3099(20)30230-9.
  • Zou, Lirong, Feng Ruan, Mingxing Huang, Lijun Liang, Huitao Huang, Zhongsi Hong, Jianxiang Yu, et al. 2020. “SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients.” New England Journal of Medicine 382 (12): 1177–9. https://doi.org/10.1056/NEJMc2001737.

6 Important Videos about Tech, Ethics, Policy, and Government

The remaining 6 videos from the the University of San Francisco Center for Applied Data Ethics Tech Policy Workshop are now available. This workshop was held in November 2019, which seems like a lifetime ago, yet the themes of tech ethics and responsible government use of technology remain incredibly relevant, particularly as governments are considering controversial new uses of technology for tracking or addressing the pandemic.

You can go straight to the videos here, or read more below:

And be sure to check out the full playlist of workshop videos here!

Speakers (left-right, top-bottom): Kristian Lum, Tawana Petty, Irina Raicu; Heather Patterson, Brian Hofer, Linda Gerull; Rumman Chowdhury, Deven Desai
Speakers (left-right, top-bottom): Kristian Lum, Tawana Petty, Irina Raicu; Heather Patterson, Brian Hofer, Linda Gerull; Rumman Chowdhury, Deven Desai

Hypervisibilizing the unseen: Dominant narratives, smart cities and race-blind tech policies

If you teach the world to fear the other, individualization and hyper surveillance become inevitable. Detroit is an incredible example of how the power of propaganda became a toolkit for race-blind policies with racist consequences, data and tech misuse, digital surveillance and the dangerous conflation between safety and security. “Be Afraid, Be Very Afraid,” is no longer just the tagline from a classic movie, it’s become a mantra for being and an excuse not to see each other. Tawana Petty is Director of the Data Justice Program for the Detroit Community Technology Project and co-leads Our Data Bodies, a five-person team concerned about the ways our communities’ digital information is collected, stored, and shared by government and corporations. Watch her talk here:

Fairness, Accountability, and Transparency: Lessons from predictive models in criminal justice

The related topics of fairness, accountability, and transparency in predictive modeling have seen increased attention over the last several years. One application area where these topics are particularly important is criminal justice. In this talk, Dr. Lum gives an overview of her work in this area— spanning a critical look at predictive policing algorithms to the role of police discretion in pre-trial risk assessment models to a look behind the scenes at how risk assessment models are created in practice. Through these examples, she demonstrate the importance of each of these concepts in predictive modeling in general and in the criminal justice system in particular. Kristian Lum is an assistant research professor at Penn CIS and the lead statistician at the Human Rights Data Analysis Group (HRDAG), where she leads the HRDAG project on criminal justice in the United States. Watch her talk here:

Diverse Faces, Diverse Lenses: Applied ethics and facial recognition research

Ethical issues are like birds: they are pervasive, varied, and often go unnoticed (especially by those not trained to identify them). Ethical “lenses” (or approaches) can help us see them. This presentation will introduce the Markkula Center for Applied Ethics Framework for Ethical Decision-Making, which features five ethical lenses. Attendees will then work together to apply those lenses to a case study that reflects the complexity of ethical decisions faced by practitioners who work with data. Irina Raicu is Director of the Internet Ethics Program at the Markkula Center for Applied Ethics.

Watch her talk here:

Panel on Local Government

There are significant challenges to creating informed tech policy, including: the diverse range of stakeholders involved, the way silicon valley incentives are misaligned with reflective policy making, binary modes of thinking, that munipalities are often an afterthought to tech companies, the gap between intended and actual use, and more. Our panel on local government had a lively and informative discussion. Panelists included:

  • Linda Gerull, CIO of City and County of San Francisco
  • Brian Hofer, founder of the non-profit Secure Justice whose work drafting SF’s ban on facial recognition was covered in the New York Times
  • Lee Hepner, an attorney and legislative aide who worked on SF’s facial recognition ban
  • Heather Patterson, privacy researcher at Intel and member of the Oakland Privacy Advisory Commission

Watch the panel here:

Deconstructing the Surveillance State

The smart city has become co-opted by an exclusionary narrative that enables a surveillance state. In this talk, Dr. Chowdhury presents the global imperative to deconstruct the current surveillance state by illustrating already-existing harms. In it’s place, she shares a vision of Digital Urban Design, which presents a community-driven and collaborative smart city. A work in progress, the goal of digital urban design is to evolve the field of urban design to merge the digital and analog fabrics in a way that impacts and improves the lives of citizens. Rumman Chowdhury is the the Global Lead for Responsible AI at Accenture Applied Intelligence, where she works with C-suite clients to create cutting-edge technical solutions for ethical, explainable and transparent AI. Watch her talk here:

Law and Data Driven Innovation

Data-driven innovation fueled Silicon Valley and more after the first dotcom bubble. It still has potential to drive incredible outcomes. Yet the days of deference to companies because of promised innovation and creation of wealth seem to be over. This talk looks at where we came from and how changes in the law show dissatisfaction with innovation narratives. And yet, the talk offers that there is a way to use data and software to build trust and success going forward. Deven Desai is an associate professor in the faculty of the Law and Ethics Program at Georgia Tech’s Scheller College of Business. Watch his talk here:

More CADE Videos

You can check out the full playlist of videos from the CADE Tech Policy Workshop here!

Be sure to read these posts with other videos and material from CADE events:

Saving The Mask

A message from Jeremy: This is a very special guest post from Sarada Lee (李文華), a Visiting Scholar at the Data Institute (University of San Francisco) and Conjoint Fellow at the School of Medicine and Public Health at the University of Newcastle. She’s also one of our most inspirational and impactful fast.ai alumni. Here, Sarada brings to us some potentially life-saving expertise that has been developed over the last 20 years in places that have already tackled respitatory pandemics: how to create masks, scaling from home production all the way to to mass production. The ability of even simple masks to protect from infection is now well established, with even the CDC providing guidance that basic surgical masks are worth using. If you go out without a mask, and you’re infected without knowing it, you’re putting your community at risk. And if someone that’s infected coughs when you’re within a couple of meters from them, and you’re not wearing a mask (and googles or glasses) then you’re at a much higher risk of getting infected yourself (this is both established science from a virology and epidemiology standpoint). There’s a huge shortage of masks in the US and many other countries, so please get to work creating masks, because you can help save lives. Your local hospital is the most important place to help first. The method here requires special equipment and materials, so you’ll need to coordinate with your local community to get things going. Also, there are simpler methods to create simpler masks. For help with both, we’ve provided a wiki and discussion thread for this post.

Disclaimer: I am not a qualified medical professional. But, I lived through SARS in Hong Kong back in 2003. My ex-colleague was a confirmed case. I used to walk past her desk every day prior to her being admitted into a hospital.

“Virtually everybody here (Hong Kong) has been through the drill, they know the consequences.” (Keiji Fukuda, a U.S. expert on infectious diseases and former assistant director-general for health security at the World Health Organization)

One may argue about the protectiveness of wearing a mask. But, the bottom line is if I am an asymptomatic carrier of COVID-19, I won’t spread the virus to the people around me. That will help to “flatten the curve” (check out Jeremy and Rachel’s explanation).


This video 1 (in Cantonese), shows the background and the development process.

How to make the reusable mask (the base model)?

What you require

  1. Equipment/tools:
    • A 3D printer (I use MakerBot Replicator with a legacy printer support software. So, I can’t use most of the features and need to work around calibration (ie leveling the printing bed.))
    • A vacuum mold forming machine (I use Mayku FormBox.)
    • A pair of scissors
    • A hole puncher
    • A grinder
  2. Materials:
    • 3D printing material (I used a Mayku.)
    • Food grade (very important!) vacuum mold forming material (1 form sheet (white color) for inner layer and 1 cast sheet (transparent) for outer layer)
    • A surgical mask (1 piece can be cut into 10 smaller pieces or more ie 10x supply)
    • Elastic band (length to adjust to fit you best)
    • Soft material for nosepiece (I don’t know what will work best yet)
    • Glue (suitable for plastic and soft material)
    • Masking tape (preferably in sheet instead of roll for 3D printer’s print bed)


  1. Download the file for 3D printing from the links below (credit to TinkerCad user ID: alex.wh.leeJ5ZTZ 李偉康老師). 3 mold sizes are available. (Source files are also available so you can modify them for customization.)

Indicative print time and materials (g) required by size:

Quality (options available to me) Large Medium Small

5:46 hrs



3:08 hrs



4:40 hrs



2:34 hrs



6:10 hrs



2:45 hrs


  1. Print a mask mold (blue color in the video).
  2. Put a form sheet (white color in the video) over the mask mold to make the inner layer of the mask.
  3. Use a pair of scissors to cut out the inner layer (similar to the shape of the mold)
  4. Put a cast sheet (transparent in the video) over the mask mold to make the outer layer of the mask.
  5. Use a pair of scissors to cut out the outer layer (leave extra material for each side in order to put on elastic bands - see light blue line and point above)
  6. Use a hole puncher to put holds on the outer layer (better to punch it from insider)
  7. Use a grinder to polish all the sharp edges
  8. Put a small piece of surgical mask in the filter area
  9. Put elastic bands to the holes
  10. Glue soft material around the nose bridge
  11. Check out this video 2 on how to wear and remove it. It is also important NOT to touch the front surface after use. Always perform hand hygiene prior to wear and after removing it. For face-hair growers, please consider changing your facial hairstyle based on CDC’s advice.3

How can we do better?

For the mask design

Of course, the download file may not fit you perfectly which is the key for protection. You may want to modify the source files. But, you need to find out your face shape before you can modify it. This can be done by using multiple images from different angles to create a 3D model (look for structure from motion software).

Alternatively, using deep learning to create it using a single photo (paper: Photorealistic Facial Texture Inference Using Deep Neural Networks by Shunsuke Saito et al. https://arxiv.org/pdf/1612.00523v1.pdf). Any one is up for this challenge?

For engineers out there, to adhere a valve or more fancy design.

For the filter

One may doubt the protection from a surgical mask. I was able to source CKP-V28 filter sheets which can filter anything bigger than 0.3 micron which is a similar particle filtration rating as N95 (US) or P2 (Europe). (Caution: if this idea worked, this version of mask has NOT been tested or certified as a personal protective equipment.)

For food grade 3D printing materials

To save money on materials, it is possible to recycle plastic bottles into filament by using a special cutter 4 or you can make one 5.

For mass production

For those in the industry, the mold can be made in aluminum using CNC machines. Please use your skills and resources to support this.

Campaign Status:

Date Actions
14 Mar 20 Found out about “Saving the Mask” Campaign
15 Mar 20 Contacted local makers community who own the equipment and sourced materials
18 Mar 20 Equipment was delivered and being briefly trained
19 Mar 20 Printed a mold (small size)
20 Mar 20 Small size of mask was made (see Annex 1) with a try-out-list
By 27 Mar 20 Improve my skills and work on the try-out-list and order more casting and mold sheets

Remarks: I don’t have any financial interest in any products mentioned here.

Final note

Personal hygiene (more than washing hands regularly) is important 6.

Social distancing/self-isolating is also important.

If you don’t have hand sanitizer (with over 70% alcohol content). WHO recommends two formulas 7 for small volume production. While Ethanol (96%) may not be available, Alternatively, check with your local compounding pharmacy (not normal pharmacy), they may be able to make it for you.


(Click ↩ on a footnote to go back to where you were.)

  1. “可重用STEM口罩 料日產二萬口罩贈學界社福界” (Translation: Reusable STEM masks, expected 20K daily production to benefit educational sector and the society) by 香港大紀元新唐人聯合新聞頻道 (Hong Kong Epoch Times), 13 March 2020. Video. Press

  2. “N95 3M mask: How to Wear & Remove” by SingHealth. Video 

  3. “To Beard or not to Beard? That’s a good Question!” in Centres for Disease Control and Prevention by Jaclyn Krah Cichowicz et al. 2 November 2017 

  4. A water bottle cutter Video 

  5. DIY water bottle cutter video 

  6. The Hong Kong University’s recommendations on personal hygiene. Credit: Prof. Ivan Hung (HKU Faculty) 

  7. WHO Guidelines on Hand Hygiene in Health Care: First Global Patient Safety Challenge Clean Care Is Safer Care. 12 WHO-recommended hand-rub formulations Website 

Covid-19, your community, and you — a data science perspective

We are data scientists—that is, our job is to understand how to analyze and interpret data. When we analyze the data around covid-19, we are very concerned. The most vulnerable parts of society, the elderly and the poor, are most at risk, but controlling the spread and impact of the disease requires us all to change our behavior. Wash your hands thoroughly and regularly, avoid groups and crowds, cancel events, and don’t touch your face. In this post, we explain why we are concerned, and you should be too. For an excellent summary of the key information you need to know, read Corona in Brief by Ethan Alley (the president of a non-profit that develops technologies to reduce risks from pandemics).


Anyone is welcome to translate this article, to help their local communities understand these issues. Please link back to here with appropriate credit. Let us know on Twitter so we can add your translation to this list.


We need a working medical system

Just over 2 years ago one of us (Rachel) got a brain infection which kills around 1/4 of people who get it, and leaves 1/3 with permanent cognitive impairment. Many others end up with permanent vision and hearing damage. Rachel was delirious by the time she crawled across the hospital parking lot. She was lucky enough to receive prompt care, diagnosis, and treatment. Up until shortly before this event Rachel was in great health. Having prompt access to the emergency room almost certainly saved her life.

Now, let’s talk about covid-19, and what might happen to people in Rachel’s situation in the coming weeks and months. The number of people found to be infected with covid-19 doubles every 3 to 6 days. With a doubling rate of three days, that means the number of people found to be infected can increase 100 times in three weeks (it’s not actually quite this simple, but let’s not get distracted by technical details). One in 10 infected people requires hospitalization for many weeks, and most of these require oxygen. Although it is very early days for this virus, there are already regions where hospitals are entirely overrun, and people are no longer able to get the treatment that they require (not only for covid-19, but also for anything else, such as the life-saving care that Rachel needed). For instance, in Italy, where just a week ago officials were saying that everything was fine, now sixteen million people have been put on lock-down (update: 6 hours after posting this, Italy put the entire country on lock-down), and tents like this are being set up to help handle the influx of patients:

A medical tent used in Italy
A medical tent used in Italy

Dr. Antonio Pesenti, head of the regional crisis response unit in a hard-hit area of Italy, said, “We’re now being forced to set up intensive care treatment in corridors, in operating theaters, in recovery rooms… One of the best health systems in the world, in Lombardy is a step away from collapse.”

This is not like the flu

The flu has a death rate of around 0.1% of infections. Marc Lipsitch, the director of the Center for Communicable Disease Dynamics at Harvard, estimates that for covid-19 it is 1-2%. The latest epedemiological modeling found a 1.6% rate in China in February, sixteen times higher than the flu1 (this might be quite a conservative number however, because rates go up a lot when the medical system can’t cope). Current best estimates expect that covid-19 will kill 10 times more people this year than the flu (and modeling by Elena Grewal, former director of data science at Airbnb, shows it could be 100 times more, in the worst case). This is before taking into consideration the huge impact on the medical system, such as that described above. It is understandable that some people are trying to convince themselves that this is nothing new, an illness much like the flu, because it is very uncomfortable to accept the reality that this is not familiar at all.

Trying to understand intuitively an exponentially increasing growth in the number of infected people is not something that our brains are designed to handle. So we have to analyze this as scientists, not using our intuition.

Where will this be in 2 weeks? 2 months?
Where will this be in 2 weeks? 2 months?

For each person that has the flu, on average, they infect 1.3 other people. That’s called the “R0” for flu. If R0 is less than 1.0, then an infection stops spreading and dies out. If it’s over 1.0, it spreads. R0 currently is 2-3 for covid-19 outside China. The difference may sound small, but after 20 “generations” of infected people passing on their infection, an R0 of 1.3 would result in 146 infections, but an R0 of 2.5 would result in 36 million infections! (This is, of course, very hand-wavy and ignores many real-world impacts, but it’s a reasonable illustration of the relative difference between covid-19 and flu, all other things being equal).

Note that R0 is not some fundamental property of a disease. It depends greatly on the response, and it can change over time2. Most notably, in China R0 for covid-19 has come down greatly, and is now approaching 1.0! How, you ask? By putting in place measures at a scale that would be hard to imagine in a country such as the US—for instance, entirely locking down many giant cities, and developing a testing process that allows more than a million people a week to be tested.

One thing which comes up a lot on social media (including from highly-followed accounts such as Elon Musk) is a misunderstanding of the difference between logistic and exponential growth. “Logistic” growth refers to the “s-shaped” growth pattern of epidemic spread in practice. Obviously exponential growth can’t go on forever, since otherwise there would be more people infected than people in the world! Therefore, eventually, infection rates must always decreasing, resulting in an s-shaped (known as sigmoid) growth rate over time. However, the decreasing growth only occurs for a reason–it’s not magic. The main reasons are:

  • Massive and effective community response, or
  • Such a large percentage of people are infected that there’s fewer uninfected people to spread to.

Therefore, it makes no logical sense to rely on the logistic growth pattern as a way to “control” a pandemic.

Another thing which makes it hard to intuitively understand the impact of covid-19 in your local community is that there is a very significant delay between infection and hospitalization — generally around 11 days. This may not seem like a long time, but when you compare it to the number of people infected during that time, it means that by the time you notice that the hospital beds are full, community infection is already at a level that there will be 5-10 times more people to deal with.

Note that there are some early signs that the impact in your local area may be at least somewhat dependent on climate. The paper Temperature and latitude analysis to predict potential spread and seasonality for COVID-19 points out that the disease has so far been spreading in mild climates (unfortunately for us, the temperature range in San Francisco, where we live, is right in that range; it also covers the main population centers of Europe, including London.)

“Don’t panic. Keep calm.” is not helpful

One common response we’ve seen on social media to people that are pointing out the reasons to be concerned, is “don’t panic” or “keep calm”. This is, to say the least, not helpful. No-one is suggesting that panic is an appropriate response. For some reason, however, “keep calm” is a very popular reaction in certain circles (but not amongst any epidemiologists, whose job it is to track these things). Perhaps “keep calm” helps some people feel better about their own inaction, or makes them feel somehow superior to people who they imagine are running around like a headless chicken.

But “keep calm” can easily lead to a failure to prepare and respond. In China, tens of millions were put on lock-down and two new hospitals were built by the time they reached the statistics that the US has now. Italy waited too long, and just today (Sunday March 8) they reported 1492 new cases and 133 new deaths, despite locking down 16 million people. Based on the best information we’re able to ascertain at this stage, just 2-3 weeks ago Italy was in the same position that the US and UK are in today (in terms of infection statistics).

Note that nearly everything about covid-19 at this stage is up in the air. We don’t really know it’s infection speed or mortality, we don’t know how long it remains active on surfaces, we don’t know whether it survives and spreads in warm conditions. Everything we have is current best guesses based on the best information people are able to put together. And remember, the vast majority of this information is in China, in Chinese. Currently, the best way to understand the Chinese experience so far is to read the excellent Report of the WHO-China Joint Mission on Coronavirus Disease 2019, based on a joint mission of 25 national and international experts from China, Germany, Japan, Korea, Nigeria, Russia, Singapore, the United States of America and the World Health Organization (WHO).

When there’s some uncertainty, that perhaps this won’t be a global pandemic, and perhaps everything just might pass by without the hospital system collapsing, that doesn’t mean that the right response is to do nothing. That would be enormously speculative and not an optimal response under any threat modeling scenario. It also seems extremely unlikely that countries like Italy and China would effectively shut down large parts of their economy for no good reason. It’s also not consistent with the actual impacts we’re seeing on the ground in infected areas, where the medical system is unable to cope (for instance, Italy is using 462 tents for “pre-triage”, and still has to move ICU patients from infected areas).

Instead, the thoughtful, reasonable response is to follow the steps that are recommended by experts to avoid spreading infections:

  • Avoid large groups and crowds
  • Cancel events
  • Work from home, if at all possible
  • Wash hands when coming and going from home, and frequently when out
  • Avoid touching your face, especially when outside your home (not easy!)
  • Disinfect surfaces and packages (it’s possible the virus may remain active for 9 days on surfaces, although this still isn’t known for sure either way).

It’s not just about you

If you are under 50, and do not have risk factors such as a compromised immune system, cardiovascular disease, a history of previous smoking, or other chronic illnesses, then you can have some comfort that covid-19 is unlikely to kill you. But how you respond still matters very much. You still have just as much chance of getting infected, and if you do, just as much chance of infecting others. On average, each infected person is infecting over two more people, and they become infectious before they show symptoms. If you have parents that you care about, or grandparents, and plan to spend time with them, and later discover that you are responsible for infecting them with covid-19, that would be a heavy burden to live with.

Even if you are not in contact with people over 50, it is likely that you have more coworkers and acquaintances with chronic illnesses than you realize. Research shows that few people disclose their health conditions in the workplace if they can avoid it, for fear of discrimination. Both of us are in high risk categories, but many people who we interact with regularly may not have known this.

And of course, it is not just about the people immediately around you. This is a highly significant ethical issue. Each person who does their best to contribute to controlling the spread of the virus is helping their whole community to slow down the rate of infection. As Zeynep Tufekci wrote in Scientific Amercian: “Preparing for the almost inevitable global spread of this virus… is one of the most pro-social, altruistic things you can do”. She continues:

We should prepare, not because we may feel personally at risk, but so that we can help lessen the risk for everyone. We should prepare not because we are facing a doomsday scenario out of our control, but because we can alter every aspect of this risk we face as a society. That’s right, you should prepare because your neighbors need you to prepare—especially your elderly neighbors, your neighbors who work at hospitals, your neighbors with chronic illnesses, and your neighbors who may not have the means or the time to prepare because of lack of resources or time.

This has impacted us personally. The biggest and most important course we’ve ever created at fast.ai, which represents the culmination of years of work for us, was scheduled to start at the University of San Francisco in a week. Last Wednesday (March 4), we made the decision to move the whole thing online. We were one of the first large courses to move online. Why did we do it? Because we realized early last week that if we ran this course, we were implicitly encouraging hundreds of people to get together in an enclosed space, multiple times over a multi-week period. Bringing groups together in enclosed spaces is the single worst thing that can be done. We felt ethically obliged to ensure that, at least in this case, this didn’t happen. It was a heart-breaking decision. Our time spent working directly with our students has been one of the great pleasures and most productive periods every year. And we had students planning to fly in from all over the world, who we really didn’t want to let down3.

But we knew it was the right thing to do, because otherwise we’d be likely to be increasing the spread of the disease in our community4.

We need to flatten the curve

This is extremely important, because if we can slow down the rate of infection in a community, then we give hospitals in that community time to deal with both the infected patients, and with the regular patient load that they need to handle. This is described as “flattening the curve”, and is clearly shown in this illustrative chart:

Staying under that dotted line means everything
Staying under that dotted line means everything

Farzad Mostashari, the former National Coordinator for Health IT, explained: “New cases are being identified every day that do not have a travel history or connection to a known case, and we know that these are just the tip of the iceberg because of the delays in testing. That means that in the next two weeks the number of diagnosed cases will explode… Trying to do containment when there is exponential community spread is like focusing on putting out sparks when the house is on fire. When that happens, we need to switch strategies to mitigation–taking protective measures to slow spread & reduce peak impact on healthcare.” If we can keep the spread of disease low enough that our hospitals can handle the load, then people can access treatment. But if the cases come too quickly, then those that need hospitalization won’t get it.

Here’s what the math might look like, according to Liz Specht:

The US has about 2.8 hospital beds per 1000 people. With a population of 330M, this is ~1M beds. At any given time, 65% of those beds are already occupied. That leaves about 330k beds available nationwide (perhaps a bit fewer this time of year with regular flu season, etc). Let’s trust Italy’s numbers and assume that about 10% of cases are serious enough to require hospitalization. (Keep in mind that for many patients, hospitalization lasts for weeks — in other words, turnover will be very slow as beds fill with COVID19 patients). By this estimate, by about May 8th, all open hospital beds in the US will be filled. (This says nothing, of course, about whether these beds are suitable for isolation of patients with a highly infectious virus.) If we’re wrong by a factor of two regarding the fraction of severe cases, that only changes the timeline of bed saturation by 6 days in either direction. If 20% of cases require hospitalization, we run out of beds by ~May 2nd If only 5% of cases require it, we can make it until ~May 14th. 2.5% gets us to May 20th. This, of course, assumes that there is no uptick in demand for beds from other (non-COVID19) causes, which seems like a dubious assumption. As healthcare system becomes increasingly burdened, Rx shortages, etc, people w/ chronic conditions that are normally well-managed may find themselves slipping into severe states of medical distress requiring intensive care & hospitalization.

A community’s reaction makes all the difference

As we’ve discussed, this math isn’t a certainty—China has already shown that it’s possible to reduce the spread by taking extreme steps. Another great example of a successful response is Vietnam, where, amongst other things, a nationwide advertising campaign (including a catchy song!) quickly mobilized community response and ensured that people adjusted their behavior appropriately.

This is not just a hypothetical situation — it was clearly displayed in the 1918 flu pandemic. In the United States two cities displayed very different reactions to the pandemic: Philadelphia went ahead with a giant parade of 200,000 people to help raise money for the war. But St Louis put in place carefully designed processes to minimize social contacts so as to decrease the spread of the virus, along with cancelling all large events. Here is what the number of deaths looked like in each city, as shown in the Proceedings of the National Academy of Sciences:

Impact of differing responses to the 1918 Flu pandemic
Impact of differing responses to the 1918 Flu pandemic

The situation in Philadelphia became extremely dire, even getting to a point where there were not enough funeral caskets or morgues to handle the huge number of dead from the flu.

Richard Besser, who was acting director of the Centers for Disease Control and Prevention during the 2009 H1N1 pandemic, says that in the US “the risk of exposure and the ability to protect oneself and one’s family depends on income, access to health care, and immigration status, among other factors.” He points out that:

The elderly and disabled are at particular risk when their daily lives and support systems are disrupted. Those without easy access to health care, including rural and Native communities, might face daunting distances at times of need. People living in close quarters — whether in public housing, nursing homes, jails, shelters or even the homeless on the streets — might suffer in waves, as we have already seen in Washington state. And the vulnerabilities of the low-wage gig economy, with non-salaried workers and precarious work schedules, will be exposed for all to see during this crisis. Ask the 60 percent of the U.S. labor force that is paid hourly how easy it is to take time off in a moment of need.

The US Bureau of Labor Statistics shows that less than a third of those in the lowest income band have access to paid sick leave:

Most poor Americans do not have sick leave, so have to go to work.
Most poor Americans do not have sick leave, so have to go to work.

We don’t have good information in the US

One of the big issues in the US is that very little testing is being done, and testing results aren’t being properly shared, which means we don’t know what’s actually happening. Scott Gottlieb, the previous FDA commissioner, explained that in Seattle there has been better testing, and we are seeing infection there: “The reason why we knew early about Seattle outbreak of covid-19 was because of sentinel surveillance work by independent scientists. Such surveillance never got totally underway in other cities. So other U.S. hot spots may not be fully detected yet.” According to The Atlantic, Vice President Mike Pence promised that “roughly 1.5 million tests” would be available this week, but less than 2,000 people have been tested throughout the US at this point. Drawing on work from The COVID Tracking Project, Robinson Meyer and Alexis Madrigal of The Atlantic, said:

The figures we gathered suggest that the American response to the covid-19 and the disease it causes, COVID-19, has been shockingly sluggish, especially compared with that of other developed countries. The CDC confirmed eight days ago that the virus was in community transmission in the United States—that it was infecting Americans who had neither traveled abroad nor were in contact with others who had. In South Korea, more than 66,650 people were tested within a week of its first case of community transmission, and it quickly became able to test 10,000 people a day.

Part of the problem is that this has become a political issue. In particular, President Donald Trump has made it clear that he wants to see “the numbers” (that as, the number of people infected in the US) kept low. This is an example of where optimizing metrics interferes with getting good results in practice. (For more on this issue, see the Ethics of Data Science paper The Problem with Metrics is a Fundamental Problem for AI). Google’s Head of AI Jeff Dean, tweeted his concern about the problems of politicized disinformation:

When I worked at WHO, I was part of the Global Programme on AIDS (now UNAIDS), created to help the world tackle the HIV/AIDS pandemic. The staff there were dedicated doctors and scientists intensely focused on helping address that crisis. In times of crisis, clear and accurate information is vital to helping everyone make proper and informed decisions about how to respond (country, state, and local governments, companies, NGOs, schools, families, and individuals). With the right information and policies in place for listening to the best medical and scientific experts, we will all come through challenges like the ones presented by HIV/AIDS or by COVID-19. With disinformation driven by political interests, there’s a real risk of making things way, way worse by not acting quickly and decisively in the face of a growing pandemic, and by actively encouraging behaviors that will actually spread the disease more quickly. This whole situation is incredibly painful to watch unfold.

It doesn’t look like there is the political will to turn things around, when it comes to transparency. Health and Human Services Secretary Alex Azar, according to Wired, “started talking about the tests health care workers use to determine if someone is infected with the new coronavirus. The lack of those kits has meant a dangerous lack of epidemiological information about the spread and severity of the disease in the US, exacerbated by opacity on the part of the government. Azar tried to say that more tests were on the way, pending quality control.” But, they continued:

Then Trump cut Azar off. “But I think, importantly, anybody, right now and yesterday, that needs a test gets a test. They’re there, they have the tests, and the tests are beautiful. Anybody that needs a test gets a test,” Trump said. This is untrue. Vice President Pence told reporters Thursday that the US didn’t have enough test kits to meet demand.

Other countries are reacting much more quickly and significantly than the US. Many countries in SE Asia are showing great results, including Taiwan, where R0 is down to 0.3 now, and Singapore, which is being proposed as The Model for COVID-19 Response. It’s not just in Asia though; in France, for instance, any gathering of >1000 people is forbidden, and schools are now closed in three districts.

In conclusion

Covid-19 is a significant societal issue, and we can, and should, all work to decrease the spread of the disease. This means:

  • Avoiding large groups and crowds
  • Canceling events
  • Working from home, if at all possible
  • Washing hands when coming and going from home, and frequently when out
  • Avoiding touching your face, especially when outside your home.

Note: due to the urgency of getting this out, we haven’t been as careful as we normally like to be about citing and crediting the work we’re relying on. Please let us know if we’ve missed anything.

Thanks to Sylvain Gugger and Alexis Gallagher for feedback and comments.


(Click ↩ on a footnote to go back to where you were.)

  1. Epidemiologists are people who study the spread of disease. It turns out that estimating things like mortality and R0 are actually pretty challenging, so there is a whole field that specializes in doing this well. Be wary of people who use simple ratios and statistics to tell you how covid-19 is behaving. Instead, look at modeling done by epidemiologists. 

  2. Well, not technically true. “R0” strictly speaking refers to the infection rate in the absence of response. But since that’s not really ever the thing that we care about, we’ll let ourselves be a bit sloppy on our definitions here. 

  3. Since that decision, we’ve worked hard to find a way to run a virtual course which we hope will be even better than the in-person version would have been. We’ve been able to open it up to anyone in the world, and will be running virtual study and project groups every day. 

  4. We’ve made many other smaller changes to our lifestyle too, including exercising at home instead of going to the gym, moving all our meetings to video-conference, and skipping night events that we’d been looking forward to.