Some vaccinated people are getting COVID. What does this mean?

Some vaccinated people are getting COVID. What does this mean?
By Kristen Panthagani, PhD

Delta is here and headlines are reporting the rise in new cases and hospitalizations, including some who have been fully vaccinated. What does this mean? Does the fact that some vaccinated people are getting sick mean the vaccines aren’t working?

Some breakthrough infections are expected

Thankfully, no. Even if a vaccine prevents 99% of infections, it is not 100% perfect. This statement should be obvious, but let it sink in for a moment. There are no vaccines that perfectly prevent every single infection, and this is to be expected. If a vaccine prevents 99% of infections, then there is 1% that it doesn’t prevent.  Sometimes there are clear reasons why a vaccine might not protect everybody — for example if someone is immunocompromised, their body might not form as strong as an immune response to the vaccine, which means they may not have enough immunity to protect them from getting COVID.

So how many breakthrough infections should we expect? Well, it depends on the population and how many people are vaccinated. This is where things can start to get counterintuitive, but I promise you it’s simple math and fractions. Here are a few examples.

Let’s say there is a population of 1,000,000 people, and today, 5% of them were exposed to SARS-CoV-2 (1,000,000 x 5% exposed = 50k people exposed). Now let’s say only 10% of them are vaccinated, so we have 50k exposed x 10% vaccinated = 5k vaccinated people exposed. In this hypothetical example, let’s say that the vaccine prevents 99% of infections, and without vaccination, everybody who gets exposed becomes infected.  How many breakthrough infections (infections in vaccinated people) would we expect? If there are 5k vaccinated people exposed and the vaccine prevents 99% of infections, then we would expect 5k x 0.01 = 50 breakthrough infections. When you’re looking at large groups of people, even when vaccines work very, very well, we do expect some breakthrough cases. These cases do not mean the vaccines aren’t working, they just mean the vaccines aren’t absolutely perfect (which we already knew).

If cases go up, breakthrough infections go up

Now let’s mess around with these numbers a little bit. Let’s say a more contagious variant has come through and instead of having 50k people exposed today, 100k people are exposed. What do we expect to happen to breakthrough infections? Let’s assume that the percent of the population that’s vaccinated remains the same at 10%. So we have 100k exposed x 10% vaccinated = 10k vaccinated people exposed. The vaccine is still preventing 99% of infections, so we would expect 10k x 0.01 = 100 breakthrough infections. So even though vaccine efficacy is unchanged, because more people were exposed, we got more breakthrough infections. Increasing numbers of breakthrough infections does not necessarily mean the vaccines are working less well, it might just mean that more people are getting exposed to the virus. 

If vaccinations go up, breakthrough infections can go up

Alright, now for the most counterintuitive piece. What happens if instead of 10% of the population being vaccinated, 90% is vaccinated? And let’s use all the same assumptions from the last example: 100k are exposed and the vaccine prevents 99% of infections. We would get 100k exposed x 90% vaccinated = 90k vaccinated exposed. The vaccine is still preventing 99% of infections, so our breakthrough infections would be 90k x 0.01 = 900 breakthrough infections.  That’s way higher than the last example! Why is it higher? Not because the vaccines are working less well, not because the virus is more contagious, but simply because there are more vaccinated people. So counterintuitively, if the virus is still circulating in a population, a higher vaccination rate will lead to higher numbers of breakthrough infections, because the overall number of vaccinated people has gone up. 

Once we reach herd immunity, that will no longer be the case, as the high level of vaccinations will slow the spread of the virus, leading to fewer cases overall (and thus fewer breakthrough cases). But until we reach that point and the spread of the virus is increasing, breakthrough cases will also increase.

In summary, the number of breakthrough infections is not only dependent on vaccine efficacy, but is also determined by other variables, including:

  • The number of people exposed to the virus
  • The percent of a population that is vaccinated (counterintuitively)

Percent of hospitalizations who are vaccinated is an unreliable metric

Alright, you made it this far. Ready for some more math? Here we go.

A lot of headlines are reporting COVID hospitalizations and listing the percent of the hospitalized patients that were fully vaccinated. Hearing something like ‘15% of hospitalized COVID patients were fully vaccinated’ is alarming, right? It makes it sound like maybe the vaccines aren’t working that well? While I get why this is a tempting statistic to report, it is actually a very unreliable metric to use to figure out how well the vaccines are working. That statistic is influenced by a whole lot of variables, only one of which is vaccine efficacy. Here’s another hypothetical example to show why this is true.

Let’s say there were 100,000 people all exposed to SARS-CoV-2 on the same day. And let’s say that for those who are unvaccinated, the risk of hospitalization is 10% (10% of unvaccinated who are exposed will end up in the hospital), while the risk of hospitalization for vaccinated people is only 0.5%. 

First, let’s figure out what the vaccine efficacy would be in this example. Vaccine efficacy for hospitalization is calculated as:

Said in words, it measures how much a vaccine reduces the risk of a bad outcome relative to that risk in an unvaccinated individual. If this is a little hard to wrap your head around, don’t worry, just trust the math. For our hypothetical example, the vaccine efficacy is (10% – 0.5%)/10% = 95% efficacy. For someone who has been vaccinated, the relative risk of hospitalization has been reduced by 95% compared to an unvaccinated individual.

Now let’s look at the number that headlines like to report (but is an unreliable way to assess how well the vaccines are working): the fraction of hospitalizations that were fully vaccinated. 

This is calculated as:

While this fraction may seem to intuitively capture how well the vaccines are working, it very much does not. 

To see why, let’s break it down into two parts: the numerator (the top part of the fraction) and the denominator (the bottom part of the fraction). I know you remember this from elementary school, but what are two ways to make a fraction go up? Either the numerator goes up, or the denominator goes down. For this statistic, both of these can happen without the vaccine efficacy changing one bit, which is why it’s an unreliable metric to use. 

The numerator

The numerator is defined by the total number of vaccinated people who have been hospitalized. This is different from the numerator in the vaccine efficacy calculation as it is not the risk of hospitalization, it is just the raw number of hospitalizations. Unlike vaccine efficacy, it does not take into account how many people were exposed leading to those hospitalizations. This leads to problems. 

For example, as we saw before, if a higher percentage of the population is vaccinated (and all other variables are held constant, including the number of people exposed), this will lead to more breakthrough infections, simply because there are more vaccinated people. Because of this, a higher percentage of vaccinated people in the population can counterintuitively lead to higher numbers of vaccinated hospitalized patients. I know this is very hard to wrap one’s head around, so here’s a visualization:

breakthrough vaccination covid hospitalization

This is our hypothetical example where 100,000 people are exposed to COVID all at once, and the risk of hospitalization for the vaccinated is 0.5% and for the unvaccinated is 10%. The only thing that is changing in this graph is the overall percent of the population who is vaccinated (listed at the top of the graph); the number of COVID exposures and the vaccine efficacy are not changing. The height of the bars represent the raw number of hospitalized people in each group, and the labels (for the left two bars) show those numbers as a percent of total hospitalizations. The label on the left above the light blue bar shows the percent of hospitalizations who are vaccinated: this is the unreliable metric we’ve been talking about.  (Please note so no one gets confused, this is an overly simplified model that is meant to illustrate concepts, not predict specifics of real world data.)

So what happens to that light blue bar as vaccinations go up? As the percent of the population who is vaccinated rises, the number of vaccinated people who are hospitalized goes up (height of light blue bar), simply because there are more vaccinated people overall. The degree of protection the vaccine provides does not change in this example. Only the percent of the community who are vaccinated is changing. The height of that light blue bar is the numerator in the unreliable fraction we’ve been discussing, and if the numerator increases, the fraction (the label above the light blue bar) will increase. This means that as more people in a population get vaccinated, all else being equal, the percent of hospitalizations who are vaccinated will go up, even though total hospitalizations are going down.

The denominator

Now let’s look at the denominator of the unreliable fraction: total COVID hospitalizations (the red bar). Remembering fractions, if the denominator goes down, the fraction goes up. What can make total hospitalizations go down? You guessed it: more vaccinated people. And that is what we see in this example: as the percent of the population that is vaccinated goes up, total hospitalizations go down, counterintuitively driving the % vaccinated hospitalizations even higher! 

But at the very end of the graphic, it finally becomes intuitive. What happens when 100% of the population becomes vaccinated? 100% of hospitalizations must be vaccinated people, because there aren’t any unvaccinated people. “100% of hospitalizations were vaccinated” may sound scary, but it is misleading and not the right thing to look at. When the graphic gets to the point where 100% of people (and hospitalizations) are vaccinated, total hospitalizations is at its lowest point. And that is what we really care about: total hospitalizations.

This is why looking at the percent of hospitalizations who are vaccinated is not a good way to assess vaccine efficacy. It is impacted by multiple variables including the vaccination rate in the community as well as any other variable that impacts the total number of hospitalizations. For example, what if elderly people are vaccinated first, then younger people are vaccinated later? In this scenario, as more people are vaccinated, the risk of unvaccinated people being hospitalized would drop, not due to anything inherent about the vaccine, but due to the dynamics of vaccine rollout (i.e. as vaccination progresses, the remaining unvaccinated are younger, and thus at lower risk for severe COVID). If the risk of hospitalization for the unvaccinated decreases, then: the total number of hospitalizations decreases, the denominator decreases, and the percent of hospitalizations who are vaccinated increases (even though the raw number of vaccinated hospitalized people is unchanged). I know that’s a lot, so here’s another graphic.

In this graphic (again a hypothetical scenario), to make things simple, the risk of hospitalization after vaccination stays constant at 0.5%, just like it was in the last example. However I made it so that as the rate of vaccination increases in the community, the risk of hospitalization for the unvaccinated decreases by 1% every time (it starts at 10% when nobody is vaccinated, then drops to 9% when 10% are vaccinated, then 8% when 20% are vaccinated, etc.). Please note this is an overly simplified simulation that is only meant to help you understand numbers and isn’t meant to model any specific real world situation.

If you compare it to the last graph, you will see that the height of the light blue bar (representing raw number of vaccinated hospitalizations) is identical. This makes sense, because the risk of hospitalization after vaccination is unchanged from the last example (it’s still 0.5% the whole time). But if you look at the label above the light blue bar, that is different compared to the last time: the fraction increases faster. Why? Look at the red bar: total hospitalizations drop faster compared to the last example. And because total hospitalizations is the denominator of the unreliable fraction, if they decrease faster, the fraction increases faster, even though the total number of vaccinated people who were hospitalized is unchanged.

This is an oversimplification because in the real world, if the demographics of the vaccinated group are changing as more people become vaccinated, you might expect the average risk of hospitalization for the vaccinated group to change as well (whereas in this example, it is held constant at 0.5%). Despite this simplification, the graphic still illustrates the overall point: if there are underlying demographic differences between the vaccinated and unvaccinated groups that impact their risk of hospitalization, then these differences in demographics can influence the fraction of hospitalized patients who are vaccinated, independently of any true change in how well the vaccine is working.

So, are the vaccines working?

So all that to say, figuring out how well the vaccines protect against the delta variant is complicated, and it takes more than looking at the percent of infections, hospitalizations, and deaths who have been vaccinated. To really figure it out, you have to take into account the overall number of people who have been vaccinated, how many people have been exposed (i.e. how fast delta is spreading through a community), the differences in age and underlying risk factors between vaccinated and unvaccinated groups, etc. That’s more than can fit in a headline, but if these variables aren’t considered, then it can lead to misleading conclusions.

So are the vaccines still working, even with delta circulating?

Yes. The figure below is a nice, big-picture way to visualize it: compare total cases to total deaths. In the past (pre-vaccination) era, every time cases went up (blue), deaths went up (red). But now in countries where a sizable portion of the country is vaccinated (UK and Portugal in this example), in recent weeks as cases have gone up, deaths have not gone up nearly to the same degree. The same is not seen for countries with low vaccination rates. 

If you’re looking more for specific numbers on vaccine effectiveness against delta, here’s a nice summary of some of the studies so far. A study published today in the New England Journal of Medicine assessing the effectiveness of the Pfizer vaccine against the delta variant found that vaccine effectiveness was 88% against symptomatic disease, while effectiveness against the alpha variant was 94%. A not yet peer-reviewed study assessing the delta variant found that the Pfizer vaccine was 96% effective against hospitalization. Check out this article for more details on other vaccines and variants. As delta continues to surge, I’m sure we will continue to have more analyses looking at vaccine effectiveness in the coming weeks. 

Delta is serious, and there will be some breakthrough infections. But this does not mean the vaccines aren’t working. The vaccines don’t completely eliminate the risk (as no vaccine ever does), but they do greatly reduce it. To figure out precisely how much they reduce the risk takes more than simple headlines, but requires nuanced, complex analyses that take into account many different variables, some of which may seem counterintuitive. And thankfully, so far the data has shown that the vaccines significantly reduce the risk of severe disease, even against delta. If you haven’t already, please look into getting vaccinated. The benefits of vaccination far outweigh the risks.

(Want more information on the COVID vaccines? Check out this article with a list of answers to frequently asked questions I’ve gotten about COVID vaccine side effects and efficacy.)

Pandemic contradictions: a sign of false information

Pandemic contradictions: a sign of false information
By Kristen Panthagani, PhD

One of the reasons false information and pandemic rumors can be so confusing and exhausting is the high degree of self-contradiction. Granted, not everyone believes every rumor simultaneously, but overall self-contradiction is often a hallmark of inaccurate information, and exposure to many different self-contradicting narratives (often with lots of emotion attached to them) can be highly disorienting and confusing. Here are a few examples I’ve run into over the last year:

“Spike protein shedding from vaccination makes it dangerous to be around vaccinated people” and “spike protein shedding from COVID infection is no big deal and there’s no need to social distance or wear a mask.”

“SARS-CoV-2 is not that dangerous” and “SARS-COV-2 is so good at making humans sick that it was clearly engineered.”

“A ~1% death rate from COVID is not a big deal” and “an extremely rare (<0.001%) nonfatal side effect from a vaccine is a very big deal.”

“If a 75-year-old with pre-existing conditions gets COVID and dies from pneumonia 4 days later, COVID was not the true cause” and “if a 75-year-old with pre-existing conditions gets the vaccine and dies from pneumonia 4 days later, the vaccine was definitely the true cause.”

“Most COVID cases are false positives” and “the case fatality rate is overestimated.” (If there were, in fact, fewer true COVID cases due to false positives, that would make the case fatality rate much higher.)

“Saying that ‘COVID is dangerous’ is fear-mongering” and “saying that ‘there is a massive network of elites who are secretly conspiring to take over the world by introducing a genetically engineered virus and forced vaccination with microchips’ is not fear-mongering.”

“The SARS-CoV-2 spike protein is dangerous” and “SARS-CoV-2 is not dangerous.”

“Foreign, non-replicating RNA from the vaccine inside human cells is dangerous” and “foreign, self-replicating RNA from the virus inside human cells is not a big deal.”

“Immune systems are naturally strong; they can handle the virus without help from the vaccine” and “immune systems can’t handle masks.”

“An observational study of ~30 people is enough to justify giving hydroxychloroquine for COVID” and “A randomized, blinded placebo-controlled trial of ~30,000 people is not large enough to justify giving the vaccine.”

“COVID isn’t real” and “COVID is a bioweapon.”

“The COVID vaccines contain unnatural ingredients that shouldn’t be put in a human body” and “livestock preparations of ivermectin are a good treatment for COVID.”

If you’ve felt like your brain has been in a fog trying to sort through all the COVID information out there, this may be part of the reason why. Self-contradicting narratives can create a swirl of confusion that makes it hard to know what is what. Hopefully seeing some of these contradictions clearly laid out makes it easier to see how these rumors don’t hold up.

If you’d like more information on some of the statements above, here are some explanations that go into the detail of the science behind them:

What do we do when experts disagree?

What do we do when experts disagree?
Kristen Panthagani, PhD

A friend of mine recently asked me how I respond to people who have opposing views about scientific issues like vaccines, and as evidence they cite people with credentials (i.e. PhDs, MDs) who agree with their position. This is a great question, and something I encounter all the time. Here are my thoughts.

Do credentials matter? On one hand, yes they do, because somebody who has gone through medical school has much more training in medicine than someone who has never taken care of a patient. Likewise, someone who has a PhD in the biological sciences has much more training in interpreting biological research studies than someone who has never done research before. So training matters, and credentials are a mark of training.

On the other hand, people can hold advanced degrees and still be very wrong. And if we blindly believe someone because they have an MD or PhD, what happens when another MD or PhD disagrees with them? We are at an impasse. The idea that the person with higher scientific credentials must be right is a logical fallacy called the appeal to authority fallacy. It is not always true that the person with higher credentials is correct; reality does not bend to the will or whims of experts.

So, if one expert says one thing and another says the opposite, what do you do? Right now I think this one is especially confusing, because the scientific community says “trust the experts!” but then when an expert says something a bit weird, they say “ignore them!” The rules of science are this: the best data and the best analysis win. It does not matter who is saying it; the data and analysis are all that really matter. Often, scientists with more credentials are better at recognizing good data and analysis, so that is why it usually makes sense to “trust the experts!” But sometimes, an expert or two might latch onto bad data for a variety of reasons (they got overly attached to their hypothesis and want to be right, they have a financial conflict of interest, the science conflicts with their ideological views, they need an audience, they are speaking outside their area of expertise). In these cases, it is best to ignore them.

So how do you handle this? The ideal way is to go look at the data for yourself. Unfortunately, effectively interpreting scientific literature requires a lot of training, and it is not a task I would put on someone who does not have a science or medical background. When people without the prerequisite scientific and medical training go and research a topic, usually what ends up happening is they read other people’s simplified interpretations and summaries of the data, rather than diving in the data for themselves. So they are not doing research in the scientific sense; rather they are gathering simplified summaries provided by other people (which are sometimes mixed with a large serving of opinion and speculation). This is through no fault of their own, as interpreting scientific data takes a lot of background knowledge and practice, and is not easily learned without formal training.

Does that mean people without a science or medical background should just give up? Not at all. I do recommend people “do their research” and read other people’s simplified summaries and interpretations. But as they do so, it’s important to recognize that they are not interpreting the primary data for themselves and rather are choosing which experts to trust and which experts to ignore.  Sometimes when a person decides which expert to trust, that decision may be influenced by factors other than the expert’s credentials or the validity of their argument. For example, maybe someone trusts a scientist because what they’re saying also happens to align with their political views, and their summary of the science is more appealing. This can happen on both sides of the political aisle.

Second, when trying to figure out what’s true, I would recommend taking into account what the majority of scientists are saying — often this will lead you to the right answer. If there are 10 doctors saying one thing and there are 10,000 doctors saying a different thing, chances are the 10,000 doctors are right. There are of course some cases in history where the 10,000 doctors were wrong, but those are the exceptions, and ultimately the lone crazy ones won them over with the data.

So to get back to my friend’s question, how would I respond to someone who is arguing a scientific point that is false but cites credentialed scientists who support that view? First, I try to get more information on what that scientist is saying. Sometimes there are legitimate points, and then we have some common ground to agree upon. But sometimes the statements of the scientist are completely baseless. In those cases, I try to focus more on the data and the rationale behind the arguments, rather than a battle of which credentialed person carries more weight.

But it’s also important to remember that the major issue behind these arguments often isn’t about data or credentials; it’s about trust. Many people simply don’t trust the CDC, the WHO, and other institutions, and are looking for other experts whom they feel are more trustworthy. Someone can bring all the data and reasoned arguments in the world, but if there isn’t any trust there, it’s probably not going to do much.

Fact-Check: Dr. Mercola’s “How COVID-19 ‘Vaccines’ May Destroy the Lives of Millions”

Fact-Check: Dr. Mercola’s “How COVID-19 ‘Vaccines’ May Destroy the Lives of Millions”
Kristen Panthagani, PhD

There is an article circulating entitled “How COVID-19 ‘Vaccines’ May Destroy the Lives of Millions” (on Mercola, it’s behind a subscription wall) that is arguing that the COVID vaccines are dangerous. I have seen this article reposted multiple times on his site, so I thought I’d go ahead and debunk it. Here are the summary points from the article, and why they aren’t true.

Claim 1: “The COVID-19 vaccine really isn’t a vaccine in the medical definition of a vaccine. It’s more accurately an experimental gene therapy that could prematurely kill large amounts of the population and disable exponentially more.”

This claim has several falsehoods in it, so let’s address them one at a time. First, the medical definition of a vaccine is a substance that induces an immune response to a pathogen, building immunity to it. Therefore, the COVID vaccines are vaccines, because they induce immune responses to the pathogen SARS-CoV-2, and after people have been vaccinated, they have immunity against COVID.

Are the mRNA vaccines "experimental?"

Now the “experimental gene therapy” claim. First, when we call a drug or vaccine experimental, what does that mean? It means that the drug or vaccine is currently in the testing stage (clinical trials). For example, if someone has cancer, their doctor might recommend they enroll in a clinical trial that is testing a new experimental treatment. It is unknown whether the treatment will work on not, so it is called “experimental.”

So, are the COVID mRNA vaccines still in the testing stage? No. They have already completed the testing stage, which was the Phase III clinical trials. The test to see if the vaccines work has been completed, and we know the results (they work very well), so these vaccines are no longer “experimental.” If you’d like to understand the results of the clinical trial yourself, here is a video that explains it.

Are the mRNA vaccines "gene therapy?"

Now, the gene therapy claim. Are the COVID vaccines gene therapy? Traditionally, the term “gene therapy” is used to describe a treatment that inserts a piece of DNA into the human genome to fix a broken human gene (DNA code), often through use of a viral vector. Is that what the COVID mRNA vaccines are doing? Not at all. The mRNA vaccines do not use a viral vector, they are not made of DNA, they do not edit the human genome, and their purpose is not to fix a broken human gene. Here is an explanation of the difference between DNA and mRNA, and why the COVID mRNA vaccines will not alter your genome.

Are the mRNA vaccines dangerous?

Now the final claim in this statement: that the vaccines “could prematurely kill large amounts of the population and disable exponentially more.” This is not true. The mRNA vaccines were tested in tens of thousands of people during the phase III clinical trials, and there were no serious adverse events related to the vaccines in these trials.

And just like we do for every other vaccine, we are still monitoring for vaccine safety even after the mRNA vaccines were authorized for general use. One way we do this is through the Vaccine Adverse Events Reporting System (VAERS). This is a website where anyone who experiences any negative health event after receiving the vaccine can report it. And doctors are required to report any deaths that occur after vaccination.

But, it’s very important to note that these VAERS reports are made regardless of whether or not the vaccine had anything to do with the negative health event. Someone could die of a drug overdose after getting the vaccine, and that could be reported to VAERS. That does not mean that the vaccine caused a drug overdose; that would be physically impossible. 

So, how do we tell if the events reported to VAERS are caused by the vaccine or not? By analyzing the reports, and comparing them to background levels of those same events. Let’s look at miscarriages as an example. Miscarriage is quite common: an estimated 1 in 8 pregnancies ends in miscarriage. So if you tracked 80 different pregnant women, about 10 of them would likely report miscarriages due to natural causes. Now let’s say all of those 80 woman got vaccinated. You would still expect about 10 of them to have miscarriages for reasons unrelated to the vaccine. But to each individual, the reason for miscarriage is often unknown, so those women might report their miscarriage to VAERS, uncertain if the vaccine caused it or not. Because millions of people are getting vaccinated right now, we expect that some negative health events will happen near the time of vaccination, just due to chance. The way we test if the vaccine actually had anything to do with the negative health event is to look at the VAERS reports and see if the rate of that health event reported to VAERS is higher than would be expected from other causes. If it is, then that tells us the negative health event might be connected to the vaccine. If it’s not, then that tells us that these VAERS reports are likely capturing background levels of that health event, unrelated to the vaccine. And so far, that is what we see for miscarriages: rates of miscarriage reported to VAERS are not above the expected background levels of miscarriage.

Claim 2: “Since mRNA normally rapidly degrades, it must be complexed with lipids or polymers. COVID-19 vaccines use PEGylated lipid nanoparticles, and PEG is known to cause anaphylaxis.”

It’s true that mRNA rapidly degrades, and it’s true that they are stabilized in part by putting them in a tiny drop of lipid (lipid is the medical word for fat). My friend Dr. Sana Zekri has written a little bit about this here. And it’s true that there have been super rare reports of anaphylaxis after some of the mRNA vaccines. So far, 5 people out of every 1,000,000 people who have gotten the Pfizer vaccine have had anaphylaxis, and 3 out of every 1,000,000 people for the Moderna vaccine. Anaphylaxis is dangerous but treatable, so that is why they are screening and keeping a close eye on people who have a history of anaphylaxis.

Claim 3: “Free mRNA can signal danger to your immune system and drive inflammatory diseases. As such, injecting synthetic thermostable mRNA (mRNA that is resistant to breaking down) is highly problematic as it can fuel chronic, long-term inflammation”

This statement seems to be implying that the mRNA vaccines will never degrade and will just circulate for a long time in the human body because they are ‘thermostable,’ in turn causing all sorts of problems. First, this is not true. Second, this is kind of odd given Claim #2 from this same article, which talks about how the mRNA is fragile. So what is the truth; is mRNA fragile or is it so stable it will stay around forever? 

The answer is mRNA is fragile, and is definitely not ‘thermostable’. The word ‘thermostable’ means stable at warm temperatures. In the lab when we work with mRNA, we have to constantly keep in cold (on ice) to keep it from degrading. There is a joke in research that if you look at your mRNA sample wrong, it will degrade. (This is why I personally hate working with RNA, and prefer DNA, which is much more stable). The mRNA vaccines also require cold temperatures to be stable, which is why they are stored at very cold temperatures. So no — these mRNA vaccines are not “thermostable”, and they do not circulate in your body indefinitely, but are degraded relatively soon after they are injected.

Claim 4: “Many commonly reported side effects from the COVID-19 gene therapy “vaccines” appear to be caused by brain inflammation.”

Uhhh… nope. Brain inflammation would cause things like altered mental status, memory loss, inability to walk, etc. I am guessing what they’re trying to blame on “brain inflammation” is actually reactogenicity, which are the signs of the immune system appropriately responding to the vaccine. These are the same symptoms of a mild cold like fatigue, fever/chills, etc. These are normal and expected, and not signs of “brain inflammation.”

Claim 5: “Anyone with an inflammatory disease such as rheumatoid arthritis, Parkinson’s disease or chronic Lyme and those with acquired immune deficiency/dysfunction from any microbial pathogen, brain trauma or environmental toxin are at high risk of dying from COVID-19 mRNA vaccines”

From a medical perspective, this is essentially a very random list of syndromes that honestly just doesn’t make any sense. It’s like they pulled a random list of diseases out of a hat. But to address their claims, there is no evidence that the COVID mRNA vaccines increase risk of death. In the clinical trials, there were no deaths caused by the vaccines, and in the ongoing monitoring of the vaccine safety data, the rate of death is proportionate to what is to be expected in the population at large. You have to remember that on average in the US, ~7500 people die every day due to all sorts of causes. And when millions of people are being vaccinated, that means that some people will happen to die (from other causes) close to the time of vaccination. This does not mean the vaccine caused it — to figure that out, you have to look and see if there is an increase in deaths relative to what’s expected. Here’s what the data shows:

Reports of death after COVID-19 vaccination are rare. More than 339 million doses of COVID-19 vaccines were administered in the United States from December 14, 2020, through July 19, 2021. During this time, VAERS received 6,207 reports of death (0.0018%) among people who received a COVID-19 vaccine. FDA requires healthcare providers to report any death after COVID-19 vaccination to VAERS, even if it’s unclear whether the vaccine was the cause. Reports of adverse events to VAERS following vaccination, including deaths, do not necessarily mean that a vaccine caused a health problem. A review of available clinical information, including death certificates, autopsy, and medical records, has not established a causal link to COVID-19 vaccines. However, recent reports indicate a plausible causal relationship between the J&J/Janssen COVID-19 Vaccine and TTS, a rare and serious adverse event—blood clots with low platelets—which has caused deaths.” Source: CDC

For more details and context on how to interpret the VAERS reports, check out this post.

Heard another rumor about COVID vaccines not addressed here? Check out this post tackling 10 common COVID myths circulating online, explaining why none of them are true.

When you can never be wrong: the unfalsifiable hypothesis

When you can never be wrong: the unfalsifiable hypothesis
By Kristen Panthagani, PhD

If there was one single scientific concept I could teach everyone in the country right now it would be this: what is an unfalsifiable hypothesis, and why do they confuse everyone.

This concept alone explains a lot of the confusion and conspiracy theories around the COVID pandemic… why many still insist that Bill Gates was involved in planning the pandemic or that there are microchips in vaccines. 

What is a hypothesis?

Before we get to unfalsifiable hypotheses, let’s start with what a hypothesis is. In very simple terms, a hypothesis is a tentative explanation that needs to be tested. It’s an idea formed on the available evidence that is maybe true, but still needs to be explored and verified. For example, at the beginning of the pandemic, many had the hypothesis that hydroxychloroquine is an effective treatment for COVID.  

Hypotheses are the jumping off points of scientific experiments. They define what question we want to test. And that brings us to one of the most important qualities of a valid scientific hypothesis: they must actually be testable. Or said another way, they must be falsifiable.

What is a falsifiable hypothesis?

What does it mean for a hypothesis to be falsifiable? It means that we can actually design an experiment to test if it’s wrong (false). For a hypothesis to be falsifiable, we must be able to design a test that provides us with one of three possible outcomes:

1. the results support the hypothesis,* or

2. the results are inconclusive, or 

3. the results reject the hypothesis. 

When the results reject our hypothesis, it tells us our hypothesis is wrong, and we move on.

*If we want to be nitpicky, instead of saying the results ‘support’ our hypothesis we should really say ‘the results fail to disprove our hypothesis.’ But, that’s beyond the scope of what you need to know for this post.

When the results reject our hypothesis, it tells us our hypothesis is wrong, and we move on.

That is the hallmark of a falsifiable hypothesis: you can find out when you’re wrong. So then, what is an unfalsifiable hypothesis? It is a hypothesis that is impossible to disprove. And it is not impossible to disprove because it’s correct, it’s impossible to disprove because there is no way to conclusively test it. For unfalsifiable hypotheses, every test you run will come up with not three, but two possible outcomes: 

1. the results support the hypothesis or

2. the results are inconclusive. 

Results reject the hypothesis‘  is missing. No amount of testing will ever lead to data that conclusively rejects the hypothesis, even if the hypothesis is completely wrong.

For unfalsifiable hypotheses that happen to be true (i.e. love exists), this is not a huge issue, because it’s usually pretty obvious that they’re right, despite their unfalsifiability. The problem arises for unfalsifiable hypotheses that are more tenuous claims.

In these cases, people may deeply believe they’re right, in part, because it is impossible to find conclusive evidence that they’re wrong.  Every time they try to test if their claim is true, they only find inconclusive evidence. And again, this is not because the hypothesis is correct, it’s because the hypothesis is set up in a way where a definitive “no that’s wrong” is impossible to find. A great example is the hypothesis that there are microchips in the vaccines. You could say ‘well just look in one and see if it’s there!’ And somebody checks and finds no microchip. End of story? Well no.. someone could argue ‘well the microchips are just too small to detect!’ or ‘They will know to take it out of the vials before they are scanned!’ Excuses are made so that the negative results are no longer negative results, but instead are inconclusive. Thus every possible result from any test we do can be deemed inconclusive by those who believe the hypothesis is correct. This makes the hypothesis, for the sake of the people who believe in it, unfalsifiable. This is why conspiracy theories are so hard to debunk… many of them are unfalsifiable hypotheses.

Why do these trap people so effectively? Two reasons. First, for a believer of the hypothesis, all they see is inconclusive data (which they can usually make fit their narrative). They never see any data disproving it, so it makes it easy for them to believe they’re right. And second, because it’s impossible to conclusively disprove it, we can’t go and… conclusively disprove it. This makes it easy for people to stay trapped in an unfalsifiable hypothesis they want to believe in, even when it’s 100% wrong.

So how do you know if you’ve been trapped into believing an unfalsifiable hypothesis? Ask yourself… how would I know if this was false? What evidence would come forward that would convince me? If the answer is ‘well, I’m waiting for the results of this study to decide‘ or ‘I’m waiting for the outcome of this particular event to know,’ then that suggests you’re not trapped in an unfalsifiable hypothesis, as you are open to actual evidence showing you that you’re wrong. (But, only if you do actually change your mind if that evidence fails to support your hypothesis, rather than finding an excuse why that event or evidence doesn’t actually disprove it.)

But, if the answer relies not on specific events or outcomes but primarily on the opinion of other believers, then you may be trapped in an unfalsifiable hypothesis, because that isn’t evidence… it’s just group think.

A scientist looks at the COVID vaccine data

A scientist looks at the COVID vaccine data
By Kristen Panthagani, PhD

Trying to decide if you’re going to take the COVID vaccine? Me too. And I decided the best way to make that decision was to look through the data. I figured there are a lot of people like me who want to understand the data for themselves. So if that’s you, check out this hand-animated walk through of the Pfizer COVID vaccine safety and efficacy data.


A big thank you to Dr. Peter Hotez, MD, PhD for taking the time to answer some questions for this video!

Minor notes:

Side effects shown at @3:48 are common side effects for ages 16-55 after Dose 1. Side effects were generally lower for age 55+, and higher after Dose 2 for both age groups. I didn’t want to overwhelm people with graphs so didn’t draw out every single one; see all side effect data here. If you’re curious why the participant numbers @1:52 and @7:09 don’t perfectly match, see Figure 1 here


@9:13 Vaccine efficacy of 95% indicates that the relative risk of getting COVID is reduced by 95% with vaccination relative to placebo.

Are vaccines safe? A story about why I didn’t get vaccinated…

Are vaccines safe? A story about why I didn’t get vaccinated…
By Kristen Panthagani, PhD

How do we know that vaccines are safe?

To answer that question, I’m going to tell you a story… my senior year of college I had the chance to live in Ecuador for a couple of months. Ever since playing Amazon Trail as a kid, I had wanted to visit the jungle. So here was my chance… the Amazon was only an 8 hour bus ride away from where I was staying. So before leaving the US, I went to a travel clinic to get the necessary vaccines for the crazy tropical microbes I was about to confront. For most people, that includes the Yellow Fever vaccine, as the mosquitos in the jungle carry this disease. However, I soon learned that the medical community recommended I NOT RECEIVE the Yellow Fever vaccine. Why couldn’t I get this vaccine, when everybody else could?

Ecuadorian jungle with my buddy, 2010. Our guide said it would look cool if we put these flowers on our faces.

Well, it’s because of a weird medical thing that happened when I was a kid. When I was 12, a massive teratoma (a type of tumor) was discovered growing next to my heart. Teratomas are one of the weirdest types of tumors — they come from stem cells which can differentiate into any type of human cell, so they’re just a random mix of tissue. Mine had teeth and pancreases, among other things. The tumor was right next to my thymus (a small immune organ) and during the surgery, my thymus was so inflamed and my tumor was so crazy that my surgeon couldn’t tell the difference between the two, so he decided the best action was to take it all out, thymus and all. (I’m totally ok now – the thymus doesn’t contribute much to the immune system after early childhood.)

11-year-old me on a family vacation where I kept getting mysteriously sick. Turns out there was a teratoma growing next to my heart, making my other organs understandably unhappy.

Except, because my thymus was removed, I can’t get the Yellow Fever vaccine. Why?

If a new vaccine is approved or authorized based on clinical trials, it becomes available for use in the general public. Clinical trials test two main things: is this vaccine safe and does this vaccine work? To answer these questions, they study thousands of people. Analysis of the data from clinical trials will catch the vast majority of side effects from the vaccine, answering the question is this vaccine safe? And of course, by comparing the disease incidence of people who got the vaccine versus people who were in the control group, they can answer the question does this vaccine work? If the data shows that the vaccine is safe and it works to protect against disease, then it will be approved (or authorized) by the FDA for use in the general public.

However, the safety monitoring doesn’t stop there. Sometimes vaccines (and other drugs) can cause super, super rare side effects. So even after a vaccine is FDA approved, it is still monitored for safety. How is this done? By a team effort: anyone (doctors or patients) can report any weird symptoms that could be related to the vaccine to a centralized database. (Additional monitoring methods discussed here.) Researchers on the back end of these databases look for patterns — if multiple people report the same adverse health event after the vaccine is administered, and the number of reports is higher than would be expected from background rates of that event in the population, then the vaccine is re-evaluated. If after re-evaluation it is shown that the risk of the rare side effect is greater than the risk of the disease it’s preventing, then the vaccine is discontinued. Or, if they find that a certain group of individuals is at higher risk for the rare side effect, then the vaccine becomes contraindicated (vaccine should not be given) for that group. And this is why I can’t get the Yellow Fever vaccine.

As of 2004, a total of 23 people worldwide (a rate of 3 people per 1 million vaccine doses given) developed a severe yellow fever-like disease after getting the yellow fever vaccine. 14 of the people died. A careful review of the situation was performed, and it was found that 4 out of the 23 people had a history of thymectomy (surgical removal of thymus). That’s 17% of all the cases, which is way higher than can be explained by chance, as thymectomies are quite uncommon. So because of this review, it was determined that a thymectomy is a significant risk factor for this vaccine reaction, and anyone who has had a thymectomy (like me) shouldn’t get the vaccine.

So I slept under an ethereal blue mosquito net and wore bug spray when I went to the jungle. A lot of bug spray.

Ethereal blue mosquito net in Ecuadorian jungle. I did not get one mosquito bite during that trip, and am incredibly proud of this accomplishment.

You may be wondering why this vaccine is still given to people without thymectomies, since 19 out of 23 people who had the side effect didn’t have thymectomies. Well, you have to remember that Yellow Fever can be really, really bad. So even though there is a small risk of serious side effects with the vaccine, for those traveling to areas where Yellow Fever is circulating, the risk of not getting vaccinated and dying from yellow fever is higher than the risk of side effects from the vaccine. For those who aren’t traveling to the jungle, there’s not a benefit of getting the vaccine, so it’s not worth the small risk of side effects. This is always the way any vaccine or medication is evaluated: no drug/vaccine is 100% without side effects, so it’s always an evaluation of the risks versus the benefits. If the risk of the disease it’s preventing is higher than the risk of side effects, then it makes sense to give it. If not, then you don’t give it.

In summary, while no vaccine is 100% without side effects, there are systems in place to continually monitor vaccine safety, detect rare reactions, and take action as necessary. So when the medical community says “these vaccines are safe,” or more explicitly, “the benefits of this vaccine far outweighs the risks,” they are doing their homework to make sure that’s actually true.

Are COVID death counts exaggerated?

Are COVID death counts exaggerated?
By Kristen Panthagani, PhD

Since the beginning of the pandemic, there has been confusion over the numbers: are cases being undercounted or overcounted? Are deaths being correctly attributed to COVID, or is the COVID death count grossly overestimating the true death toll? People hear stories of men dying from heart attacks being listed as COVID deaths, fueling suspicions that the official COVID death counts are inaccurate, including people who died “with” COVID rather than “from” COVID. Are these suspicions true?

First, let’s acknowledge the obvious: when you are trying to tally things up in real time across an entire country, the precise number is bound to be somewhat inaccurate. This is not a sign of negligence or deception; it’s a reflection of the difficulty of counting things in real time and coordinating across thousands of different health care systems simultaneously. If you want to argue with me that as of today, there are not precisely 224,292 people who have died from COVID in the US, I would say you’re probably correct. It is likely at least a little bit off from that number. We rarely have an exact number when it comes to death tolls from disease — even the seasonal flu is based on estimates, not precise counting.

I think most people realize this, and what they really want to know is this: is the official death count in the right ballpark of the true total? They want to know if it’s way off. If the true death total is actually 50,000 or 500,000, that would be important to know. If it’s actually 223,189 not 224,292, that’s really not something to make a fuss over.

How are COVID deaths counted?

So how do we count COVID deaths? The current method used by the CDC is to look at the causes of death listed on death certificates. These are filled out by doctors who took care of the patients who died, or sometimes medical coroners or medical examiners. In order to get as full of a picture as possible as to what happened, the death certificate asks the doctor to list the “final” cause of death (i.e. myocardial rupture) as well as the diseases/events that contributed to that happening (i.e. motor vehicle accident). Death certificates also have a place to list underlying conditions that likely contributed to death, but did not directly cause the death (i.e. heart disease). This form distills a complex patient story down to three or four words to describe what happened to them. From a medical perspective, this is very much an oversimplification — it misses the vast majority of the details of what happened to the person, which are documented in that person’s medical chart. From a data analysis perspective, distilling this information down into these simple diagnoses is incredibly helpful, as it allows organizations like the CDC to analyze what is going on with patients at a broad level. If we had the level of detail provided in medical charts on death certificates, the clarity on the COVID death count would be worse, not better, as there would be millions of medical notes to go through to figure out what happened to each patient. Thus it is helpful that the doctors summarize it on the death certificates. But again, it’s a simplification.

Here is an example death certificate — you can see it asks for both the underlying cause of death and the chain of events that the underlying cause triggered, ultimately leading to one or more vital organs failing. A person who gets COVID which causes pneumonia which then causes acute respiratory distress syndrome would have all three things listed under Part I: the chain of events that directly caused death. Part II of the form allows the doctor to indicate underlying health conditions that likely predisposed the patient to being ill, but are not the primary cause of death themselves.

Here is the CDC’s explanatory video for physicians on how to fill out these death certificates for COVID, which explains the process:

Up until the COVID pandemic, this process was not called into question. A person with chronic heart disease and decades to live who got into a car crash and later died due to a weak heart that couldn’t compensate for the blood loss might be listed as cardiac arrest (heart stopped beating) secondary to hypovolemic shock (not enough blood to the body) secondary to motor vehicle accident with a significant underlying condition of chronic heart disease. This would get counted as a motor vehicle death, and nobody would be upset. Did the person have heart disease? Yes. Would they still be alive if they hadn’t been in a car crash? Yes. Would they still be alive if they didn’t have heart disease? Perhaps, as maybe someone with a healthier heart could have survived the blood loss. Regardless, it is still appropriate to list car crash as a cause of death, even though their weak heart may have also contributed.

To doctors who have studied the full course of diseases and understand how different medical conditions are interconnected, this makes a lot of sense. However outside of the hospital, when we talk about what somebody “died from,” we usually only think of one cause like “car crash”. We don’t think of “what vital organ ultimately failed, what caused that, and what were the predisposing factors.”  That’s how doctors think because they are trained to do so; it’s not usually how the general public thinks. 

Table 3: Confuse America

This has led to substantial confusion over COVID death certificates: several weeks ago, the CDC released the data on the COVID death certificates, and of course, multiple things were listed (because that is the proper way to fill out a death certificate). You can see the table below. 6% of them had only one cause listed (COVID), which in reality probably meant that those death certificates were incomplete. But this data was rapidly misinterpreted: the general public saw that multiple causes were listed, and many assumed that those other diseases were the true causes of death, and COVID was only listed because they happened to test positive, but they were about to die anyways. Many seemed to assume that people who truly died from COVID should only have one thing listed on their death certificates, and attributed that 6% number to the “true” death toll. To make matters more confusing, the CDC did not separate out the two sections on the death certificate: Part I (which describes the chain of events leading to death like COVID => pneumonia => respiratory failure) and Part II (which describes the underlying health conditions that contributed to death like high blood pressure and obesity) were all mixed together into one table. This, in my opinion, is a mistake on the part of the CDC: this is a very confusing way to present the data. This confused many, and led to the widely circulated rumor that only 6% of COVID deaths were truly due to COVID, and everyone else was super sick already with all these other diseases. In reality, many of the other conditions listed in the table were directly caused by COVID (i.e. pneumonia, respiratory failure). But you couldn’t easily tell that because Part I and Part II were all are mixed together. Furthermore, one can’t determine how “sick” these people were based on this data. If someone has diabetes, they may be a well controlled diabetic with decades to live, or they may be someone who has not taken their medication or controlled their diet for years and only has 6 months to live. Knowing that they have diabetes does give you some information, but it cannot be used to argue that the person was already super sick and on death’s doorstep.

Source: CDC

Then why are heart attacks being counted as COVID deaths?

One of the most common arguments I’ve heard with regard to the accuracy of the COVID death certificates is the confusion over heart attacks. The argument goes something like: someone has a heart attack and also happens to test positive for COVID, and then they’re counting it as a COVID death! This is doesn’t make any sense! What’s going on here? Are these rumors true?

First of all, it’s false that everyone who tests positive for COVID and dies automatically has COVID listed on their death certificate.  Only conditions that caused or contributed to death are listed on the death certificates. Doctors don’t list every diagnosis a patient has, only the ones that, based on the details of that patient’s hospital course, the doctor knows to be pertinent to the disease process that ultimately killed them. This is stated directly on the CDC website:

So ultimately, doctors are using all their knowledge about how disease processes work to decide what actually killed someone. It is not the CDC who is deciding this, it’s doctors/medical examiners, all across the country. By writing “COVID-19” on the death certificate, they are not simply stating that the person who died was COVID positive; they are stating that “yes, COVID caused (or contributed) to this person’s death.” The CDC is not making this determination; the doctors are. Someone who is arguing that the COVID death count is wrong is not arguing with the CDC, they are arguing with all the doctors who actually took care of those patients, saw them die, and then filled out these forms describing what happened. Because these doctors had access to all the patients’ medical records, know the details, and are trained in medicine, they are in the best position to make this call. Looking at the limited information given by the death certificate and trying to argue that the doctor was wrong is like having access to three chapter titles of someone else’s book, not the full text, then trying to argue with the author of the book that they have the story wrong. Trying to do this from a conglomeration of thousands of these death certificates in the table above is even worse, as then you don’t even know whether each condition was listed under Part I (cause of death) or Part II (underlying condition), what other conditions each person had, nor how they were connected. 

Then where are these stories coming from about COVID and heart attacks? Well, it turns out that one of the weird complications of COVID is blood clots. For reasons we don’t fully understand, in addition to attacking the lung cells, the virus can also cause blood clots to form more easily than normal. Heart attacks are often caused by blood clots clogging the vessels that bring blood to the heart, which deprives the heart of oxygen and makes it stop working. So it is actually very plausible that COVID could directly cause a heart attack, especially in someone who has high blood pressure and high cholesterol, as these predispose the blood vessels to clot as well. But it can even in occur in someone without these risk factors, as reported here. So if both COVID and heart attack are listed on a death certificate, this does not mean the patient just happened to test positive for COVID right before their heart attack and died “with COVID, not from COVID.” It means that the doctor who filled out that death certificate, based on everything he or she knew about the patient, determined that COVID contributed to that person’s death. Are doctors perfect at assigning cause of death? Probably not, but if you would like to reasonably argue with them, you need both 1. a high level of medical knowledge and 2. access to the details of that patient’s medical records. Without these things, any arguments are pure speculation of what maybe happened based on minimal information, which are frivolous.

But what if doctors are lying?

It has been suggested that perhaps the doctors filling out these death certificates are doing so dishonestly, and falsely writing COVID diagnoses to make money. I’m not gonna lie, this one hurts. If you could see inside hospitals right now and know what doctors are sacrificing for their patients, you would know how much of a slap in the face this is. But let’s put emotions aside and see if this argument holds merit.

First, let’s note that falsely assigning an ICD-10 code for billing purposes is considered fraud, and doctors who do this lose their license. Second, let’s look at the impact COVID has had on hospitals and doctors: has the pandemic brought them lots of business making them rich? No, just the opposite: higher COVID cases has cost hospitals revenue, not increased profits. From a business persective, hospitals are financially incentivized to keep COVID numbers low so that they don’t have to divert resources away from other revenue streams. Falsely elevating their COVID numbers does not help them do this — it is not in the doctor’s nor the hospital’s financial interest to falsely inflate COVID numbers.

But even if there were truly financial incentives to falsely list COVID on the death certificate, would doctors do it? There are bound to be a few corrupt doctors out there, so maybe a handful of doctors would do so. Would this have a significant impact on the death count? No: in order for the effect of corrupt doctors allegedly falsely filling out COVID death certificates to significantly impact the COVID death count, you would need more than a handful of doctors committing this fraud… you’d need like half of the doctors in American doing it. This is usually where conspiracy theories fall apart: a few corrupt people doing something shady is plausible; half of an entire profession doing something shady, conspiring in secret, to deceive the public is not plausible at all. 50% of American doctors are not risking their jobs and purposefully deceiving the public in the middle of a pandemic just to make a little cash. No.

Cross-checking death counts: Are more people dying than normal?

One way we can cross check the COVID death numbers is by looking at total deaths during the pandemic, from all causes. If COVID deaths were being falsely inflated and those dying truly died from underlying causes that were going to kill them anyways, then we would not expect to see a big increase in overall deaths during the pandemic. So what do we see? 

Source: CDC

The CDC went back and looked at all deaths during the pandemic, and compared it to deaths during previous years to determine if their were “excess deaths” (deaths above what’s normally expected for that time of year). They found that as of Oct 3, 2020, there were 299,028 excess deaths, while only 198,061 deaths attributed to COVID on death certificates. So there were actually 100k more excess deaths than were being captured by the COVID death count. This could be explained by several possible factors — it could be that some of the excess deaths were due to COVID but it wasn’t reflected on their death certificate for one reason or another (maybe it was an abnormal presentation and the doctors didn’t realize it was COVID, or it was before widespread testing was available). Or perhaps some of these deaths are from the longer term complications of COVID that we don’t understand yet. Some are also likely due to other impacts of the pandemic, like people delaying care for other illnesses due to shutdowns or concerns about going to the hospital. We can’t say for sure, but the fact that the excess deaths are actually way higher than the official COVID count does not support the idea that the COVID death certificates are falsely inflating the death numbers. If anything, it supports the idea that COVID deaths may be undercounted.

In conclusion: no, the COVID death counts are not dramatically overestimating the number of COVID deaths. These numbers are based on the doctor’s evaluation of what killed the patient, and not simply counting people who just happened to test positive right before they died from something else. Additionally, there is no data to indicate that these were all super sick people who were going to die anyway; how “sick” they were prior to contracting COVID cannot be accurately gleaned from death certificates. The all cause mortality during the pandemic has dramatically increased above normal levels, confirming that way more people than normal are dying right now.

5G Doesn’t Cause COVID: A Case Study in Misinformation

5G Doesn’t Cause COVID: A Case Study in Misinformation
By Kristen Panthagani

Ooooooooook. Deep breaths. Let’s begin.


I started this blog because I saw a lot of misinformation flying around, and well-meaning people are getting legitimately confused. And a lot of that misinformation is half truths or just quotes taken out of context (check out this post for examples of that), and I don’t blame readers for being misled.


But this paper is a whole other level. The paper entitled “5G Technology and induction of coronavirus in skin cells” was published in a PubMed-indexed journal (which is supposed to be a mark of credibility) trying to argue that SARS-CoV-2 can spontaneous generate in human skin cells due to exposure to 5G. The article has since been withdrawn due to its obvious flaws, but you can see an archived version here.


This is a whole other level of misinformation — it’s not some random person taking a legitimate study out of context. No — the actual authors are trying to sell you a completely implausible claim by dressing it up in sciency language and publishing it in (what is very likely) a predatory journal. Luckily scientists saw it and called it out, but I think it is still a valuable lesson in how people can dress up nonsense in fancy language to deceive readers, and that there are pay-for-play journals that will give it a legitimate-looking platform. Also there are still a lot of people that think 5G has something to do with COVID, and I hope walking them through the implausibility of these claims might help bring some perspective. So let’s take a look.


First let me note — this study is ignoring some really basic concepts of physics, chemistry, and biology, so in some ways it’s even harder to argue with this than less flawed studies because they are not working in the same physical universe as the rest of us. It’s kind of like trying to argue with someone who is saying there should be a border wall between the US and the moon… that… just doesn’t make sense.

What is the main argument of this article?

Before reading the summary below, take a gander at the actual article. It looks impressive, right? They have fancy diagrams and lots of math equations! They cite laws of physics! Alright, now taking away the science jargon, here is what they are actually arguing.


Plain language summary of what they are trying to say:

5G affects skin cells. COVID-19 affects skin cells, therefore COVID-19 and 5G have similar effects. DNA (which is inside skin cells) has a coil-like structure and is electrically charged. Inductors (coiled wire that can store energy when electricity passes through it) are also coil-shaped, therefore DNA is an inductor. As an inductor, DNA absorbs the 5G and then makes new electromagnetic waves that are DNA-shaped — not the shape of the DNA coils, mind you, but the shape of individual DNA molecules (bases). These DNA base-shaped waves punch DNA base-shaped holes in the liquid inside the cell. To fill these holes, a new DNA base is made, because it is the right shape. These individual DNA.. sorry, I mean RNA bases magically join together in exactly the right order to form the 30,000 RNA base-long coronavirus genome. For this to work, the wavelength of the electromagnetic radiation has to be smaller than the size of a cell. Also, btw, 5G is capable of killing every living thing except some forms of microorganisms. It causes 720 (factorial) diseases.

A similar level of scientific thinking as that displayed by this article.

Now the Science

Let’s walk through a few of the many, many things that are wrong with this argument. First, let’s start with some basic definitions. 5G is a form of electromagnetic radiation just like 4G, radio waves, and visible light. Its wave lengths generally range between 1 and 10 millimeters and are slightly shorter than 4G, but much longer than visible light. The shorter the wavelength, the more energy the wave carries. Thus sunlight carries way, way more energy than 5G.

It is true that some molecules absorb electromagnetic radiation like light (this is the reason color exists). However, whether or not a given molecule absorbs the electromagnetic radiation depends on the wavelength of the radiation. What wavelengths does DNA absorb? Ultraviolet light (~260 nanometer = 0.00026 millimeters). Is this in the range of 5G? No, it is 3,000X-30,000X shorter than 5G. This is why UV light is damaging to DNA (sunburns, skin cancer) and also why scientists are not at all concerned about 5G causing DNA damage.


The authors seem to understand that the wavelength matters — one of the main arguments of this paper is the reason 5G is so bad is because 5G wavelengths are smaller than the size of the cell. (And radio waves aren’t a problem because they are bigger than a cell.) But hold on… how big are human cells? 0.01-0.1 millimeters. How long are 5G wavelengths again? 1-10 millimeters. So, human cells are also way smaller than 5G wavelengths (10-1000X smaller). Already this paper is off to a self-conflicting start.


Alright, now their DNA inductor argument. There are lots of physics-y sounding arguments thrown together here, and I am guessing the authors are hoping that you’re not very familiar with electromagnetism and particle physics and will give up and say “ok maybe that could work?” But let’s distill the argument to something very simple: they are saying DNA emits DNA base-shaped radiation that punches holes in liquids.


Again, they use a partial truth: it is true that, in very specific circumstances, DNA molecules can emit electromagnetic radiation (this is called fluorescence). In fact, lots of molecules do this — this is not a unique property of DNA. However, these waves are not DNA base-shaped, they are…. wave-shaped. But perhaps more importantly, electromagnetic waves do not punch holes in liquids. Electromagnetic waves have no mass. They do not push matter out of the way. They just pass through it.


An example of fluorescence (emission of electromagnetic radiation) – glowing jellyfish.

But, just to humor them, let’s imagine, for a second, that somehow DNA did emit DNA base-shaped electromagnetic waves and those waves did punch DNA base-shaped holes in liquids. What happens when you punch a hole in a liquid? Try punching a swimming pool and see what happens. Is the hole instantly filled with a fist-shaped molecule? No, it just fills back in with water. So, if there were DNA base-shaped holes inside your cells, this would not trigger the cell to make a new DNA base. DNA bases are made through a highly complex series of chemical reactions guided by enzymes, and they are tightly regulated by your cell. They do not spontaneously generate because there is a hole.


Ok, but let’s pretend for a second that there were DNA shaped holes in your cells and those holes were filled in with DNA bases. Now the article switches to RNA because they forgot SARS-CoV-2 is an RNA virus. Ok now the holes are magically filled up with RNA bases. Could this lead to the construction of a RNA viral genome?


No. The SARS-CoV-2 RNA genome is ~30,000 bases long. 30,000 individual RNA bases would have to, by pure coincidence, line up and connect to each other in exactly the right order to build the SARS-CoV-2 genome. First, individual RNAs cannot connect to each other by themselves. They need an enzyme to help them, which, like everything else in the cell, is tightly regulated. Second, the chances of them lining up in the right order to make the SARS-CoV-2 genome is infinitesimal. This is like throwing thousands of fridge word magnets out your window and expecting to walk down and see a complete work of Shakespeare. The statistical chance of this happening in one cell is (1/4)^30,000, which is 0. (I put this in my calculator and literally it came out with 0… there weren’t enough decimal places allowed by my calculator to capture how small of a chance this is.) Ok… maybe let’s allow for some substitutions given that there is some variability in the SARS-CoV-2 genome. Let’s say only 90% of the bases have to be in the right order. That is (1/4)^(30,000*0.9). Still 0.


Furthermore, this has to happen by chance separately in everyone who has COVID. 30,000 bases magically lining up in the same way in people all across the globe. (Unless you think that most people have COVID because the virus emerged once and then was spread to other people. Congratulations! You have just discovered that 5G does not cause COVID).


As a side note — there truly are individual RNA and DNA bases floating around inside your cell — these are what’s used to build copies of your genome and various RNAs. We didn’t need 5G-induced DNA-shaped holes in liquid to get to this step. This is already a thing. But, they do not spontaneously join together to make 30,000 base long viral genomes. They can only do that when they have a template to work from and enzymes to guide the process, which is what a virus does inside the cell to make more copies of itself. Without the original template, nothing will happen.


How to dress up nonsense as real science

This paper does not obey the basic rules of physics, chemistry, or biology, yet on first glance it looks like it could be legitimate. I’m guessing many non-scientists who came across it wouldn’t be sure what to think. So let’s look at the tools they used to dress up their nonsense to fool their audience.


Step 1: Use lots of technical jargon.

Technical jargon serves two purposes: 1. it gives the writer an air of legitimacy (they’re using technical words! they must be an expert!) and 2. it hides what they’re actually saying (because the average person cannot understand them). To someone outside the field, it is very hard to read a paragraph with lots of jargon and process what it’s saying. Usually the reader walks away with a vague sense that ‘these people must know what they’re talking about,’ but they have little understanding of the details. This is the perfect combination to sell misinformation.


Step 2: Use half truths.

This article does contain some true scientific statements, and mixing these in with the false ones further serves to give it an air of legitimacy. Someone with a little science knowledge might recognize the true statements (DNA molecules are charged, etc.), which makes it seem more believable.


Step 3: Include lots of scary math.

This paper includes over two pages of formulas, which serves the same purpose as the technical jargon: it makes the paper look sophisticated, and most readers won’t understand it.


Step 4: Pay to have it published in a predatory journal that will give it a look of authenticity.

The reason this particular paper caused such a stir is that it was published in a PubMed-indexed journal. PubMed is a search engine of medical and scientific publications that is supposed to only include legitimate journals. Unfortunately, in the world of science, predatory publishing is a thing — people make up journals that have legitimate sounding names (like the “Academy of Science and Engineering (ASE)” and allow pretty much anyone to publish in them if they’re willing to pay the fee, just so they can make money. PubMed is supposed to include only legitimate journals to prevent the crap in these predatory journals from showing up in search results. That’s why it caused quite a fuss when this ridiculous 5G COVID paper showed up on PubMed — something went wrong with PubMed’s indexing system. It is an important lesson to realize that there is an entire world of predatory publishing out there that publishes fake and/or seriously flawed science in a legitimate looking package simply to make money. Unfortunately, that means that just because something looks like a real science publication doesn’t mean it is. And I absolutely don’t blame you if you don’t know how to tell the difference. Usually, checking if it’s on PubMed is a good first step.


It is important to remember we live in a world where people (even scientists with real credentials) make up completely ludicrous science, and that sketchy journals exist that are willing to publish that ludicrous science to make some cash. Unfortunately, that means it’s important not to trust something just because it sounds technical or looks official. If the claim sounds outrageous, it probably is. Also, 5G doesn’t cause COVID.

Masked Science: Fact-checking Mask Studies

Masked Science: Fact-checking Mask Studies
By Kristen Panthagani, PhD

I saw this post on the interwebs citing a bunch of studies, suggesting that masks are ineffective at preventing infection and/or are unsafe. (And also suggesting there is some sort of cover-up of this science). Since there is still controversy around mask-wearing (and the post gave me PubMed ID’s! thank you!) I decided to dig into to see if there was any merit to what they were saying. Here is what I found.

Quote #1

“Preliminary Report on Surgical Mask Induced Deoxygenation During Major Surgery…Our study revealed a decrease in the oxygen saturation of arterial pulsations (SpO2)”

PMID 18500410.


Is this study real?

Yes (full text here).


Is the quote above actually in the study?

Yes, but it is missing the second half of the sentence. Full quote reads “Our study revealed a decrease in the oxygen saturation of arterial pulsations (SpO2 ) and a slight increase in pulse rates compared to preoperative values in all surgeon groups.”


What did the study do?

This study measured SpO2 (blood oxygen saturation) of surgeons before and after surgery. All the surgeons were wearing masks during the surgery. As a control group, they also measured SpO2 of individuals in the operating room who were observing (not participating in surgery) and who were not wearing a mask.


What did they find?

For short operations (< 60 min) there was no decrease in SpO2 after surgery. For longer operations (1-2h, 2-3h, 3-4h) there were subtle but statistically significant decreases in the SpO2 after surgery in the mask-wearing surgeons who were operating, but not the non-mask wearing observers. The largest drop in SpO2 was recorded after the longest surgeries (3-4h), but was still quite small (it dropped from ~97.6% SpO2 before the surgery to ~96% SpO2 after the surgery — these are both within normal range.)


What does this tell us about masks?

SpO2 stayed well within normal range for every study participant, indicating it is safe to wear masks for extended periods of time. There was a slight drop in SpO2 in surgeons after long operations, but unfortunately, this study doesn’t tell us whether this was due to the mask or due to operating. The control group tells us this decrease in SpO2 was not from standing for that period of time, so we can rule out that explanation out. However, given that surgery requires intense focus and many of us tend to hold our breaths when we are focusing on fine motor tasks, it’s possible this slight reduction in SpO2 is due to changes in surgeon breathing and/or other effects of performing surgery, and not due to the mask. Alternatively, it is possible that wearing a mask for several hours slightly decreases SpO2, but not to a level that is unsafe. The authors do acknowledge that they cannot attribute this drop to the mask: “This change in SpO2 may be either due to the facial mask or the operational stress, since similar changes were observed in the group performing surgery without a mask.” I think it’s unfortunate they chose a title that didn’t reflect this uncertainty.


Simple Conclusion:

This study indicates that surgeons in their study group had a slight drop in blood oxygen saturation after long surgeries, but that this drop is minimal, and does not present a health concern. This study does not provide evidence regarding the effect of masks on blood oxygen saturation, as there were significant confounding variables in the study design.


Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?


Quote #2

“We know that wearing a mask outside health care facilities offers little, if any, protection from infection.”

PMID: 32237672.


Is this study real?

The article is real, but it’s not a study — it is a perspective/opinion piece. Here is the full text.


Is the quote above actually in the study?



What did the article say?

This is an opinion piece discussing the pro’s and con’s of universal masking in hospitals during the COVID-19 pandemic, presumably written in March 2020 (published April 1, 2020). The quoted line above is the only mention of mask-wearing outside of hospitals, and the rest discusses whether every doctor/nurse/etc. needs to wear a mask in hospitals. The article does not question the effectiveness at proper mask use in hospitals, but discusses other factors that need to be weighed (supply availability, if people who wear a mask will touch their face more, etc.).


What does this tell us about masks?

They do not provide citations nor data backing the quoted claim above that wearing a mask outside health care facilities offers little protection, so this article does not provide any data on that topic. Overall, this piece is consistent with the thinking at the time it was written (the very beginning of the pandemic in the US) — it was not recommended that everyone wear masks at that point. (See below for discussion as to why.)


Simple Conclusion:

This article is an opinion piece written at the beginning of the pandemic that does not provide any data on whether masks are effective or not.


Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No, this is not a study, it’s outdated advice.

Quote #3

“.. both surgical and cotton masks seem to be ineffective in preventing the dissemination of SARS-CoV-2 from the coughs of patients with COVID-19 to the environment and external mask surface.”


Is this study real?

Yes (full text here), but it has now been retracted.


Is the quote above actually in the study?



What did the study do?

They had 4 people with COVID-19 cough on petri dishes with and without masks (cotton and surgical, but not N95’s), and they measured the amount of virus they could detect with and without masks. The authors concluded that because they could not find a difference in the viral load on the petri dishes with and without masks and because they detected virus on the outside of the masks, surgical and cotton masks do not block SARS-CoV-2 when people are coughing.


Why was this study retracted?

Retraction means the study was so flawed that it can no longer be considered reliable — it is like “unpublishing” an article. The study was retracted because it had a Limit of Detection problem — the method they used to measure how much virus was present on the petri dishes could only measure viral loads above a certain threshold, and some of their samples were below that threshold. When samples are below the limit of detection, the experiment can’t tell the difference between no virus, a little bit of virus, and a little bit more virus. (This is kind of like squinting at a ruler from 20 feet away and trying to tell the difference between 0.4 inches and 0.33 inches… your eyesight is not reliable to detect subtle differences like that.) Unfortunately, the type of experiment they ran will still spit out numbers even below the limit of detection, even though those numbers aren’t reliable at all. It’s the scientists’ job to know when those numbers are reliable versus unreliable. These scientists made a mistake and thought their numbers were reliable, when in fact they weren’t.


Simple Conclusion

This study provides no evidence on the effectiveness or ineffectiveness of masks due to a problem in their methodology (there wasn’t enough virus to measure it accurately). Because of this issue, it was retracted.


Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?


Quote #4

“Most healthcare workers develop de novo PPE (such as N95 face mask) associated headaches or exacerbation of their pre-existing headache disorders.”

PMID: 32232837


Is this study real?

Yes, full text here.


Is the quote above actually in the study?

Yes with a slight edit — the original quote reads “Most healthcare workers develop de novo PPE‐associated headaches or exacerbation of their pre‐existing headache disorders.” PPE stands for personal protective equipment.


What did the study do?

They gave questionnaires to 158 health care professionals working in high risk hospital areas in Singapore at the beginning of the COVID-19 pandemic. The questionnaires asked if they previously had headaches, if they had developed new headaches since the pandemic (and where the headaches were, how severe they were, and how often they had them), and what types of PPE they were wearing (at that hospital anyone caring for a COVID patient was required to wear goggles, close-fitting N95 masks, gowns, surgical gloves.)


What did they find?

The hospital workers wore goggles and face masks an average of ~6 hours per day. Over a 30 day period, 81% of participants reported sometimes having headaches when wearing the N95 mask with or without goggles, with the location of the headaches often corresponding to the points where the mask/goggles put pressure on the face. About half of the people attributed their headache to the mask, while half attributed it to the goggles. Most people graded their headache as mild and infrequent (1-4 days / 30 days). People who wore both N95 face masks and goggles for > 4 hours/day and those who had a pre-existing headache diagnosis were more likely to report headaches.


What does this tell us about masks?

Tight-fitting PPE like fit-tested N95 masks and goggles may cause mild headaches if worn for an extended period of time, likely due to the pressure from the straps/seal on the face.


Simple Conclusion:

Health care workers and other professions wearing tight-fitting masks and goggles for multiple hours may get mild headaches.


Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?


Quote #5

“This study showed that the filtering efficiency of cloth face masks were relatively lower, and washing and drying practices deteriorated the efficiency.”

PMID: 31289698.


Is this study real?

Yes, here is the full text.


Is the quote above actually in the study?



What did the study do?

The focus of this study was the effectiveness of cloth masks worn in developing countries on preventing inhalation of air pollution. They bought cloth masks from Kathmandu, Nepal and measured the pore size and filtering efficiency of air pollution compared to surgical masks. They found that cloth masks had larger pores and lower filtering efficiencies (63-84%) than surgical masks (94%). They also found that repeatedly washing cloth masks decreased the filtering efficiency further.


What does this tell us about masks?

While this study did not look at anything related to viruses or transmission of infectious diseases, it does tell us that cloth masks (at least those purchased in Kathmandu, Nepal) have larger pores than surgical masks, and that surgical masks are better at filtering small particles than cloth masks (though cloth masks still filter out a significant percentage of small particles in the air.) This is consistent with the idea that N95 > surgical mask > cloth mask > no mask.


Simple Conclusion

Surgical masks filter out more particles than cloth masks purchased in Nepal. Depending on the type of fabric, this may be true for cloth masks commonly used in the US as well.


Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?


Quote #6

“None of the studies established a conclusive relationship between mask/respirator use and protection against influenza infection.”

PMID: 22188875.


Is this study real?

Yes, here is the full text.


Is the quote above actually in the study?



What did the study do?

This study was a systematic review (a review of all studies on a particular topic) looking at whether or not the use of masks and/or respirators prevent transmission of influenza. One of the goals of this study was to figure out if it is worth it for people to wear masks every year to lower the transmission of seasonal influenza. They looked at 8 studies of masks use for preventing influenza, and 2 showed an effect (masks help prevent infection) and 6 did not show an effect (no difference in infection rates with and without masks.) Because there weren’t a lot of studies on influenza, they also looked at 9 studies of mask use during the first SARS epidemic. 8 out of 9 studies showed an effect (mask use was associated with reduced risk of SARS infection). However, the authors note that many of the SARS studies were suboptimal, and that the results can’t necessarily be generalized to influenza, since they are very different viruses. All together, they conclude that there wasn’t enough published evidence to support the idea of using masks to prevent transmission of the seasonal flu.


What does this study tell us about masks?

This study tells us that (at least as of 2012 when this study was published), there was not evidence to support wearing masks every year to prevent the seasonal flu. It does suggest that masks work against SARS, as 8/9 studies showed a protective effect, however better studies were warranted as there were some methodological flaws in those studies. Overall (as of 2012), the authors highlight that more research is needed.


Simple Conclusion

As of 2012, there was not enough evidence published to support the use of masks to prevent seasonal influenza. The authors want more (and better) research on the topic. There are quite a few studies indicating masks helped protect people in the first SARS epidemic, but those studies have some methodological weaknesses.


Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?


Quote #7

“Face mask use in health care workers has not been demonstrated to provide benefit in terms of cold symptoms or getting colds.”

PMID: 19216002.


Is this study real?

Yes, abstract here. I couldn’t access the full-text so this is based off the abstract.


Is the quote above actually in the study?



What did the study do?

A total of 32 health care workers were studied for 77 consecutive days — they were randomized to either wear masks or not wear masks, and the presence of cold symptoms was tracked. Only two people got colds, (one in each group). They also found that the group who wore masks were more likely to experience headaches.


What does this study tell us about masks?

This study doesn’t really tell us much about the effectiveness of masks at preventing infection because it didn’t study enough people — if only two total people got colds throughout the course of the study, if there was a difference in cold prevention we would need far more people to detect this difference. It does suggest wearing masks may increase risk of headaches in health care workers.


Simple Conclusion

This very small study suggests healthcare workers who wear masks daily may have increased risk of headaches. It does not give us reliable information on whether or not masks prevent colds. A bigger study is needed to address this question.


Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?


Quote #8

“There is little evidence to support the effectiveness of face masks to reduce the risk of infection.”

PMID: 20092668.


Is this study real?

Yes, full text here.


Is the quote above actually in the study?

Yes, but it makes the conclusion sound simpler than it is. Here is the full paragraph:


“In conclusion there remains a substantial gap in the scientific literature on the effectiveness of face masks to reduce transmission of influenza virus infection. While there is some experimental evidence that masks should be able to reduce infectiousness under controlled conditions [7], there is less evidence on whether this translates to effectiveness in natural settings. There is little evidence to support the effectiveness of face masks to reduce the risk of infection. Current research has several limitations including underpowered samples, limited generalizability, narrow intervention targeting and inconsistent testing protocols, different laboratory methods, and case definitions.”


What did the study do?

This is a systematic review of studies that have looked at whether or not face masks, often in combination with other protective measures like hand washing, help protect against influenza infection. They looked at 12 different studies — some were underpowered and didn’t provide any useful information, some did not show a protective effect of mask use from influenza and/or cold symptoms, and some showed a protective effect of mask use or face shield. Sometimes this effect was only observed when combined with hand hygiene (hand washing). There were significant limitations in many of the studies, and the authors call for further research.


What does this tell us about masks?

As of 2010 when this review was published, there wasn’t strong evidence to support the idea that healthy people should wear face masks to protect against influenza infection. Many studies had limitations, and more research is needed. Remember not to confuse absence of evidence with evidence of absence — sometimes there is “not strong evidence” for something because we simply haven’t done the studies needed to find the evidence, and sometimes there is not strong evidence for something because we looked and there was not evidence to be found. (Check out this post for more on that.) Here is the conclusion provided by the authors: “There is some evidence to support the wearing of masks or respirators during illness to protect others, and public health emphasis on mask wearing during illness may help to reduce influenza virus transmission. There are fewer data to support the use of masks or respirators to prevent becoming infected. Further studies in controlled settings and studies of natural infections in healthcare and community settings are required to better define the effectiveness of face masks and respirators in preventing influenza virus transmission.


Simple Conclusion:

As of 2010, there was not strong evidence to support mask-wearing by healthy people to prevent influenza infection in a non-pandemic setting. Some of the studies were pretty crappy, and more research is needed.


Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?



Quote #9

“..laboratory-confirmed virus were significantly higher in the cloth masks group.. Penetration of cloth masks by particles was almost 97%.. This study is the first RCT of cloth masks, and the results caution against the use of cloth masks.. Moisture retention, reuse of cloth masks and poor filtration may result in increased risk of infection.”

PMID: 25903751.


Is this study real?

Yes, here is the full text.


Is the quote above actually in the study?

Yes, but it is somewhat taken out of context.


What did the study do?

This is a randomized-controlled trial looking at the effect of different types of masks on preventing infections in medical personnel in a Vietnam hospital. There were three groups: those who wore medical masks (surgical masks), those who wore cloth masks, and a control group, which, very importantly, was “usual practice, which included mask wearing.” So many in the control group were also wearing masks, which makes the results of this study very difficult to interpret (i.e. there is no “no mask” group). They looked at infections (respiratory illness, influenza-like lillness, and laboratory-confirmed respiratory virus infection) and found that the rates of infection were lowest in those who wore surgical masks and highest in those who wore cloth masks. The “control” group (which isn’t really a control group but more of a mystery group) was in the middle. Notably, a lot of people in the study didn’t actually wear the masks all the time like they were supposed to, which also limits the conclusions one can draw from this study.


What does this tell us about masks?

This study suggests that surgical masks may work better than cloth masks (at least those used by hospital workers in this study) at preventing influenza-like illness in a non-pandemic hospital setting. However many of the participants weren’t compliant with mask wearing, so I don’t put a lot of weight on the results of this study. The control arm is completely useless, so this study tells us nothing about the effectiveness of mask versus no mask.


Simple conclusion.

Once again, surgical mask > cloth mask (probably, but mostly this study was just messy).


Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?


Quote #10

“Respiratory acidosis develops when air inhaled into and exhaled from the lungs does not get adequately exchanged between the carbon dioxide from the body and oxygen from the air.”


Is this real?

Yes, but this is just a website about respiratory acidosis, and has nothing to do with masks.


What does this tell us about masks?

Absolutely nothing.


Simple Conclusion

This is just a medical definition.


Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No, this is not a study, it is a lesson in medical vocabulary.

Quote #11

“Conversely, surgical and hand-made masks, and face shields, generate significant leakage jets that have the potential to disperse virus-laden fluid particles by several metres. The different nature of the masks and shields makes the direction of these jets difficult to be predicted, but the directionality of these jets should be a main design consideration for these covers. They all showed an intense backward jet for heavy breathing and coughing conditions. It is important to be aware of this jet, to avoid a false sense of security that may arise when standing to the side of, or behind, a person wearing a surgical, or handmade mask, or shield.”


Is this study real?

Yes (though it has not yet been peer-reviewed.)


Is the quote above actually in the study?



What did the study do?

They used some fancy methods to track where a person’s breath goes when they breath and cough while wearing different types of masks/PPE (surgical mask, handmade mask, FFP1, FFP2, respirator, face shields.) They found that all masks (except the respirator, which is not designed to stop exhaled air) significantly decrease how far a person’s breath travels in the forward direction. They found that masks that seal (like FFP1, FFP2 masks) don’t have significant leakage to the side and back, while masks that don’t seal (surgical masks, cloth masks) do have leakage to the side and back. They caution people to remember that non-sealing masks can leak to the side and back.


What does this tell us about masks?

Non-sealing masks do not block everything and redirect some airflow to the side and back. This was based on a study of one person, so it should be verified in a larger sample size. It is not yet peer-reviewed, so there may be flaws that have not been caught yet.


Simple Conclusion

Non-sealing masks may leak to the side/back.


Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?


Quote #12

“Face masks should not be worn by healthy individuals to protect themselves from acquiring respiratory infection because there is no evidence to suggest that face masks worn by healthy individuals are effective in preventing people from becoming ill.”


Is this study real?

Yes, but it’s not a study — it’s a summary of information about masks published in early March 2020.


Is the quote above actually in the publication?



What does this tell us about masks?

This article reflects the thinking at the time it was written — reserve masks for healthcare workers and people who are sick. It does not provide new data on the topic, only summary recommendations.


Simple Conclusion

At the beginning of the pandemic, the recommendation was that healthy people not wear masks. (More details on that below).


Does this study indicate that masks are unsafe or ineffective against SARS-CoV-2?

No, this is not a study, it’s outdated advice.

Summary: What did we learn from these studies?

  1. There is not enough evidence to suggest everyone wear masks every year during influenza season in a non-pandemic setting (and we don’t.)

  2. Surgical masks are probably better than cloth masks at preventing infection (though there may be variation on the type of cloth mask used). Both leak somewhat.

  3. Use of tight-fitting masks for long hours by health care workers may be associated with increased risk of mild headaches.

  4. While many of the quotes (as well as the title “masked science” with the crying masked person) make it sound like these articles provide evidence that masks are unsafe and/or don’t work against SARS-CoV-2, none of these articles actually provide any evidence to support this claim (and in some cases, provide evidence to the contrary.)


Was this post misinformation?

While none of the quotes or studies were fabricated, they were often taken out of context and/or presented in a misleading way. Read the quotes again now that you’ve gone through the details of the studies.

The quotes often avoid key details about the studies which results in misleading the reader.. for example, the studies on cloth masks were often compared to surgical masks, but you couldn’t tell that from the quotes. So reading these quotes you might assume they were comparing to not wearing masks at all, which changes the conclusion from “surgical masks work better than cloth masks” (what the studies actually showed) to “cloth masks don’t work” (what this post seems to be trying to imply). This is obviously very misleading, and most people would only catch it if they went and looked up the studies. Additionally, some of the quotes feel cherry-picked to reveal a specific point of view and don’t really reflect the conclusion of the authors of the studies. A lot of the studies are about influenza/common cold, but the way they’re presented makes it seem like these results should apply to SARS-CoV-2, which isn’t an accurate assumption (why? There’s probably many reasons, but a major one is SARS-CoV-2 is way more infectious than the seasonal flu). The random inclusion of a respiratory acidosis definition is just silly — thrown in there with all these quotes from studies, it would make many readers think there was a study that had shown respiratory acidosis was associated with mask use (which is a myth that has been circulating), when in fact this had absolutely nothing to do with masks. All together, this feels like it was put together by someone who was trying to convince people masks aren’t safe/don’t work against SARS-CoV-2 in spite of the evidence, not because of it. It is an important reminder that you can’t trust something just because it has lots of citations.

Are masks effective in slowing the transmission of SARS-CoV-2?

None of these studies actually looked at the effectiveness of masks for SARS-CoV-2 (except the retracted one, which is useless) — are there studies indicating that masks do protect against SARS-CoV-2 transmission? Yes, check out this meta-analysis of SARS-CoV-2, SARS, and MERS studies and this summary about cloth masks (note the study being asked about at the beginning of this summary is the problematic study from quote #9).

Why were we told not to wear masks at the beginning of the pandemic?

So if masks work against SARS-CoV-2, why were they not recommended for everyone at the beginning of the pandemic in the US? (They were recommended for healthcare workers and those who were showing symptoms). While I was not involved in making these recommendations (of course), here is my best guess. At the beginning of the pandemic, we were in the following situation: 1. we didn’t understand the virus, 2. we thought that the prevalence of COVID-19 in the US was quite low, 3. we didn’t have strong evidence that people could be infected without showing symptoms and 4. people were freaking out and panic-buying toilet paper. From that perspective, the risk of telling “healthy” people (people who weren’t showing symptoms) to wear masks outweighed the benefit… we weren’t sure how much it would help (we didn’t know so many “healthy” people were actually infected), and it would likely deplete mask supplies for healthcare workers if everyone started panic-buying N95s. Some argue that we should have told everyone to wear cloth masks back then as to not confuse everyone about the effectiveness of masks; in retrospect I think that would have been a good idea. Now, our situation is different: 1. we have data to show that wearing masks helps slow transmission, 2. the prevalence of COVID-19 in the US is way, way higher than it was in March (and we know a lot of cases are going undetected), 3. we know that many people are infectious without showing symptoms (meaning they can spread it when they think they are healthy), 4. we’re not as concerned about people panic-buying all the N95 masks, and 5. many places are trying to re-open, so masks provide a way to lower the rate of transmission without resorting to a full shutdown again. Given these changes, it makes sense for everyone to wear masks when around others, even if they aren’t showing symptoms, because they may be infected without knowing it.