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 The article has since been withdrawn due to its obvious flaws, but you can see an archived version here.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

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?

No.

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?

Yes.

 

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.” https://www.acpjournals.org/doi/10.7326/M20-1342.

 

Is this study real?

Yes (full text here), but it has now been retracted.

 

Is the quote above actually in the study?

Yes.

 

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?

No.

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?

No.

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?

Yes.

 

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?

No.

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?

Yes.

 

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?

No.

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?

Yes.

 

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?

No.

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?

No.

 

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?

No.

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.” https://www.medicalnewstoday.com/articles/313110

 

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.” https://arxiv.org/ftp/arxiv/papers/2005/2005.10720.pdf

 

Is this study real?

Yes (though it has not yet been peer-reviewed.)

 

Is the quote above actually in the study?

Yes.

 

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?

No.

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.” https://jamanetwork.com/journals/jama/fullarticle/2762694

 

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?

Yes.

 

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.

Why does it take so long to develop medical treatments?

Why does it take so long to develop medical treatments?
By Kristen Panthagani

Medical research is a loooooooooong process. Like, really long. There are many different steps along the way, and from the outside it can be confusing why there was a study published years ago saying Drug X works for Disease Y, but a decade later we still don’t have a treatment. Why is this?

Medical Research is a Process

In an ideal world, developing medical treatments would look like this.

Somebody has a great idea for a new treatment, and that new treatment quickly gets tested and developed. But because biology is super complicated, and there are lots of variables that we still don’t understand well, it usually ends up looking like this:

Lots of different types of studies build upon each other, ultimately leading to a well-tested, effective, and safe treatment. But even this doesn’t always work out. The vast majority of drug ideas end up failing somewhere along this path.

 

[Note: depending on the treatment under investigation, these steps aren’t always in exactly this order, and it’s often even messier and more squiggly path than the line drawn above. But for the sake of simplicity, I’m breaking it down into these simple steps illustrating the different types of studies that build upon each other to ultimately develop a new therapeutic.]

Step 1: Do the very first study.

Say a scientist or a doctor gets a great idea for a new therapeutic while drinking a beer at the pub. So they design an experiment (or in reality, more like 5-20 different experiments) and test out their idea. Depending on the type of study, the experiments that go into a single research article can take months (or more often years) to complete. Then, it’s published! The world knows! And, the PhD student who did the work gets a thesis. A success!!

 

But many of these ideas don’t end up working out… maybe after several months of experimenting, the data just doesn’t back it up. Some ideas sound great on paper, but fail to work right out of the gate. Even talented scientists come up with lots of ideas that don’t work.

Step 2: Reproduce it.

Ok let’s say the idea worked… in a single study. It’s published! But now comes the most central tenet of scientific research: it must be reproducible. Lots of studies end up failing this test (check out this book if you’re curious why). Depending on the type of scientific question, scientists might try to reproduce the results in a cell line (cells grown in a dish). These are very common experiments and are called in vitro studies (vitro is latin for glass, so basically it translates as: in a glass tube/dish/some other glass container). In reality, these are now usually made of plastic, but that’s ok. While in vitro studies provide valuable information (and are much easier to do than trials in animals or humans), it’s important to note how early they occur on this timeline… they are considered very preliminary evidence for drug efficacy, and you can’t definitively conclude that a drug works based only on in vitro studies. (Much of the early excitement about hydroxychloroquine was based on in vitro studies, and more than one confident gent on the internet sent me these studies as evidence that “it works.” But you can see how early it is on the timeline, and why many scientists remained skeptical of the in vitro studies on hydroxychloroquine, waiting for more rigorous human trials to decide if it really works for COVID-19.)

Step 3: Look at the data in humans.

The next step in the process (again depending on the therapeutic) might be to look for epidemiological data to see if the drug works. This is what happened more recently with hydroxychloroquine — because it was being prescribed as an emergency treatment, we could look back and see if the patients who got the drug did better than those who didn’t. (Usually drugs aren’t prescribed before they’ve gone all the way through this process, the pandemic was an exception). These type of observational studies provide better data than studies done in a dish of cells, but they still have problems — the main one is there is no guarantee that the people who got the drug are similar to those who didn’t. For example, maybe hydroxychloroquine was prescribed to patients who were sicker to begin with… this would confound the results of these types of studies. And studies may fail when they reach this level… just because cells in a dish are cured by the drug, that doesn’t mean that real breathing humans will be cured.

Step 4: Test it out in animals.

Most treatments will be evaluated in animals before going to human trials. Animal studies are usually one of the first major steps to test causation — is the thing you are studying really causing the effect that you think it is? Association studies in humans might fool you — some things are associated, but that doesn’t mean one is the cause of the other. In animal studies, we give them the treatment, and we can clearly tell if it’s causing the effect we expect.

Step 5: See if it's safe in humans.

Once we have lots of in vitro experiments and animal experiments showing high promise for a drug, it’s time to test it out in humans. For new drugs that have never been used in humans before, the very first test in humans is always safety. This is called a Phase I Trial — where the primary goal is to determine if the drug is safe. These studies usually start with just a few people who are given a very low dose of the drug, and they are monitored extremely carefully for any side effects. If no side effects are detected, the dose is slowly increased until the target (treatment) dose is tested. Sometimes an extremely promising treatment will fail at this step if it has significant side effects. It does not matter how many mice the drug has cured, if people’s livers don’t tolerate it, we’re not going to use it. About one third of Phase l trials fail at this step.

Step 6: See if it works in humans.

If a drug proves to be safe in a Phase I Trial, the next step is to see if it works in humans. Remember, just because a drug works in a dish of cells or in a bunch of mice does NOT mean it will work in human beings. Phase II Trials test this. These studies usually enroll 50-100 patients who have the disease of interest, and half are given the treatment while half are given the normal care they would get for the disease. If the people who got the treatment do better, then that is evidence the drugs works! They are also closely monitored for safety to see if there are rare side effects not detected in the Phase I Trial. About half of Phase II trials fail and don’t go onto the next step.

Step 7: Test it out in... more humans.

Phase III Trials test if the drug works better than the best treatment currently available. They are generally bigger than Phase II Trials (usually several hundred people) and include a placebo group to control for the placebo effect. Sadly, even though they’re so close to the finish line, even sometimes these studies end up failing (about 60% succeed). They fail for a variety of reasons — the placebo effect can be one of them.

Step 8: Approve it, but keep monitoring it.

Usually once a drug has made it through a Phase III Trials it can be approved by the FDA for use. However, that isn’t the end of the story… drugs that are approved are still monitored for safety and long-term efficacy. This is sometimes called a Phase IV Trial. Maybe there was a side effect that was rare but serious, and couldn’t be detected in the earlier trials. Maybe there are severe drug interactions that weren’t detected. If there are significant side effects that prove to outweigh the benefit of the drug, the drug can be recalled even after getting FDA approval.

And, voila! After decades of work and millions of research dollars, your great idea has actually turned into a treatment that doctors can prescribe! You can see this is a very long process, and the vast majority of treatment ideas end up failing. However, this does not stop the press and the general public from getting excited (which is fine) and confused (which is not fine) at every step along the way.

So now you know… if a study has the words in vitro, mouse, or really anything other than ‘randomized controlled trial’ in the title, it probably still has a very long way to go before we know if it’s going to work as a treatment in humans. (Check out @justsayinmice for one man’s effort to remind the world that many studies in mice are reported as if they have just saved humanity from disease.)

“There is no evidence” can mean very different things

“There is no evidence” can mean very different things
By Kristen Panthagani

There have been a lot of “there is no evidence” statements flying around of late…

 

There is no evidence coronavirus survivors can’t be reinfected.

There is no evidence that coronavirus was accidentally released from a lab.

There is no evidence that wearing masks can activate viruses.

 

This is unfortunately very confusing because “there is no evidence” can actually mean very different things depending on the context (as is the case in the three examples above). It seems this has been leading to a lot of misunderstanding (and perhaps even distrust) of scientists making these statements about the COVID pandemic, so here is a quick breakdown to bring a little clarity.

Meaning #1: There is no evidence... because we haven't studied it yet.

This is basically saying “we don’t know yet, and we’re not going to assume until we know.” Probably the most publicized example of this is the statement the WHO put out saying “There is currently no evidence that people who have recovered from COVID-19 and have antibodies are protected from a second infection.” What the WHO was really saying is we can’t assume (for the sake of immunity passports) that antibodies will provide protection from reinfection until we have clear data backing that claim. But it understandably confused a lot of people, and it sounded like the WHO was saying that antibodies don’t work against coronavirus. In reality, we just don’t know yet.

A doctor who does not know yet.

Down the road when more evidence is available, it may seem like doctors were “wrong” when they said “There’s no evidence for that!” Were they wrong? Were they being deceitful? No.. they were just communicating the state of uncertainty based on the lack of evidence at the time.

Meaning #2: There is no evidence... because we studied it extensively and found no evidence.

Once scientists have done the studies, “there is no evidence” takes on a different meaning. If a claim has been studied extensively, and study after study fails to provide evidence for the claim, then when scientists say “there is no evidence” it no longer means “we don’t know yet” and instead means “we looked and really, there is no evidence.” A good example of this is the idea that the MMR vaccine causes autism. This idea was proposed back 1998 in a small study, and as the cause of autism was unknown, it was worth looking into. Scientists were like “yup we will check this out,” and did a whole lot of studies. Since then the association between MMR vaccination and autism has been studied in hundreds of thousands of children. These studies failed to find evidence linking the MMR vaccine to autism: children who had been vaccinated were no more likely to develop autism than children who had not been vaccinated. Based on this, scientists concluded that there is no evidence that the MMR vaccine causes autism… because they looked (extensively), and no evidence was found.

Meaning #1.5: Somewhere in the middle

Often a “there is no evidence” claim may land somewhere between Meaning 1 and Meaning 2: we’ve studied it a bit and so far have found no (or minimal) evidence, but we’re still not 100% sure so we’re going to keep studying it. This is where I would put hydroxychloroquine right now… so far it looks like there is minimal evidence that it works in COVID-19 patients, but we’re not 100% sure yet, so we’re going to keep studying it.

Meaning #3: There is no evidence... and it's not worth studying.

Sometimes scientists will say “there is no evidence” to support a claim that hasn’t been studied… and should never be studied. For example… there is no evidence that lava cures COVID-19. When scientists make statements like this, they mean three things:

 

     1) there are no studies showing that lava is helpful for COVID-19 patients (of course)

     2) there never will be any studies testing if lava is helpful for COVID-19 patients because

     3) we don’t need a study to tell us this is a bad idea

There is no evidence that lava cures COVID.

Many ideas do not need to be studied because we can use what we already know about biology to determine that they won’t work. Lava kills people, we do not need a study to tell us this. This lava example is very obvious to everyone… the confusing part comes when it takes a bit of science training to spot ideas that we can confidently say won’t work. This is when we get into the territory of scientists saying “this definitely won’t work,” and then they hear “but have you done a double-blinded placebo-controlled trial? No? Then how do you know it won’t work?” The explanation is in the biology that is already known.

 

Next time you hear someone say “there is no evidence” for your favorite COVID-19 hypothesis, before jumping to any conclusions, try to figure out which of these statements they are really making.

Logical Fallacies in the Time of COVID

Logical Fallacies in the Time of COVID
By Kristen Panthagani

As I’ve gone into the world of facebook comments, I have noticed that many people trying to make sense of the pandemic (myself included) are running into a number of logical fallacies. As you listen to other people’s views and form your own, try to keep these fallacies in mind to avoid common but unhelpful missteps in human reasoning.

 

First, what are logical fallacies? My good friend, Dr. Gretchen Ellefson (Assistant Professor of Philosophy) just taught a class on critical thinking that included this very topic! Here is her basic intro to fallacies:

 

First, at a basic level, fallacies are flaws in reasoning or faulty forms of reasoning. What we mean when we say this is that people have arguments in mind when they form beliefs, meaning that they have reasons supporting a given conclusion. An argument has a fallacy when the reasons fail to do their job–they don’t actually support the conclusion.

 

The second thing is that fallacies are super normal and common and everyone uses them. The reason we want to talk about them is because recognizing where fallacies come in can help us do better at making sure we have good reasons for what we think is true. This means that when we see people using fallacies, we should recognize that this is a normal human thing, not a sign of stupidity.

 

That said, fallacies do tend to track forms of reasoning that human brains, for whatever reason, find compelling, even though they shouldn’t be. This means that fallacies CAN be used in really manipulative ways. If someone wants to convince their audience of something, and they know that there aren’t good reasons for their audience to accept that thing, then they may look for ways to use these kinds of patterns of reasoning which people will be less likely to immediately recognized as flawed.

 

Fallacy 1: Some people who have stayed home from work ended up getting COVID-19, therefore staying home doesn’t help prevent the spread of COVID-19.

This is a false dilemma fallacy — where the arguer tries to make it seem there are only two options (staying home protects 100% of people or it doesn’t protect at all), when in fact other explanations exist. In this case, an alternative explanation exists: staying home helps slow the spread of COVID-19 so that fewer people get sick, but it is not a guarantee that no one will get sick. (This also has elements of the false cause fallacy).

 

Fallacy 2: I have been around lots of people and I haven’t gotten sick, therefore social distancing isn’t really needed.

This is the anecdotal fallacy which assumes that one person’s experience must be reflective of everyone in the population. A similar argument could be made for drunk driving; someone could say that because they drove drunk last night and nobody got hurt, avoiding drunk driving is not necessary. Obviously that is not true — most would realize that this person was just lucky that they didn’t cause an accident. We have to look at the effect of both social distancing and drunk driving on the population, not just individual cases. Why? Because a lot of people who are exposed to the coronavirus do not end up getting symptoms, so if you are one of those lucky people, you may be around tons of people (and get exposed) and never get sick. Or maybe you have been lucky and just haven’t been exposed yet, even if you’ve been around lots of people. This does not mean that we can assume that will be the case for everybody. (This also has elements of the false cause fallacy).

 

Fallacy 3: If someone is concerned about the economy, they must not be taking the pandemic seriously. And alternatively, if someone is advocating for social distancing, they must not care about the economy.

These are also false dilemma fallacies, where issues are falsely divided into simple boxes creating an either/or scenario, when reality is in fact much more complex.

 

Fallacy 4: There are videos of Dr. Fauci and Bill Gates discussing the likelihood of a pandemic several years ago; these videos are evidence that these men were involved in planning the pandemic.

This is a false cause fallacy (also perhaps a post hoc fallacy), which assumes that if two events are associated, one must be the cause of the other. A similar but more obvious fallacy would be an oncologist giving a cancer prognosis that turns out to be accurate, and then suspecting them of murder because they “knew” when the patient was going to die. Or accusing meteorologists of manipulating hurricanes because they accurately predicted it was going to hit a major city. Science can be used to predict lots of things (with varying degrees of certainty), including the possibility of a pandemic.

 

Fallacy 5: If the government forces us to wear masks for the sake of public health, they will soon encroach on our liberty in more extreme ways.

This is the slippery slope fallacy, which avoids discussing the issue at hand (mandatory masks) by distracting with a far more extreme claim, and assuming that these two things must be connected without providing any evidence that they are connected.

 

Fallacy 6: Don’t worry about the civil liberty implications of shut downs, think about all the people who are suffering from the virus right now!

This is an appeal to emotion fallacy, where instead of providing a logical argument, the arguer attempts to manipulate the person’s emotions.

 

Fallacy 7: The vast majority of people support social distancing, therefore it’s the right thing to do. This is the bandwagon fallacy, which asserts something is true because lots of people think it’s true. This is, of course, not how reality works; there have been many examples where popular opinion was flat out wrong.

 

Fallacy 8: Because the last argument (Fallacy 7) was a fallacy, social distancing is not the right thing to do.

This is a fallacy fallacy, which asserts that any conclusion that is based on a fallacy must be wrong. This isn’t necessarily the case; it just means that the statement didn’t pull from valid evidence or arguments, but it may be the case that valid arguments do exist.

 

Fallacy 9: Getting immunity naturally is better than getting a vaccine because vaccines are artificial.

This is the appeal to nature fallacy, which asserts that because there are so many good things that come from nature, everything from nature is always better than something that is created by humans. This is of course untrue, there are many bad things that come from nature (like hurricanes, and viruses) and many good things that are man-made (like breakfast tacos).

 

Fallacy 10: The person with higher scientific credentials must be right.

This is the appeal to authority fallacy, which asserts that authority figures (experts) are always right. But of course, this is not always true; reality does not bend to the will or whims of experts. 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 if you don’t know how to judge the quality of the data and analysis for yourself, how do you tell the difference? My best advice is to look at what the majority of scientists are saying — there are very few things that scientists dislike more than bad science (hence the existence of this blog), and we will call it out, no matter how many degrees the person promoting it may have. The consensus of the majority of scientists (who have expertise on the question at hand) will usually* lead you to the right answer.

 

*There are a few cases in history where the majority of scientists were wrong and the lone “crazy one” was right. Here is one of my favorite examples.

Lessons from Graduate School for the COVID Pandemic

Lessons from Graduate School for the COVID Pandemic

By Kristen Panthagani

Data, medicine, and research have all of a sudden become part of every day life, and I have noticed the world is getting tripped up on a few lessons that every PhD student learns in their first few years of graduate school. Here is a cheat sheet to help you interpret the COVID-19 pandemic, and all the uncertainty around the data and studies we are seeing.

1. There is no such thing as perfect data.

There is only better data and worse data. I have seen people want to throw out all data on COVID-19 comparisons because none of the data is perfectly accurate. It is true that there is no database that has perfect counts of COVID-19 cases or deaths, and every plot of data you see likely has some (or many) inaccuracies in it. However, this does not mean it is useless. Some simple guidelines to follow are (1) find the most reputable data source you can, and (2) when you look at the data, take potential flaws into consideration, then (3) make the best conclusion you can with the data you have available, considering the flaws (Try “hmmm I think I see a trend here, but there is some uncertainty due to differences in testing” rather than “I know for sure that state A is doing a better job at social distancing than state B” or “this is all utter rubbish.”) This is the process used to interpret all data in science, not just rapidly emerging pandemic data. Yes… some data is so flawed that it should be thrown out because it’s trash, but if your standard for throwing out data is if it contains any flaws or inaccuracies at all, you will be throwing out pretty much every data set in existence.

2. It is easy to only pay attention to the data that supports your hypothesis (and ignore
the data that goes against your hypothesis).

It is easy, but oh so wrong. This is why I personally remained skeptical of the anecdotal evidence for hydroxychloroquine/azithromycin efficacy against COVID-19… we have heard reports from physicians saying that it does work, which is exciting. But is it possible that only the positive stories are getting circulated? If a doctor tries this drug combination and it doesn’t work, are they going to be interviewed by the evening news or shared all over facebook? Probably not. Likewise, early on there were multiple small studies on whether hydroxychloroquine/azithromycin works in humans… with conflicting results. Should we only pay attention to the studies that say it works, and ignore studies that say it doesn’t work? Nope. We have to judge each of these studies based on the quality of the study, not on whether it gives us the answer we want.

3. It is easy to get fooled by early data.

This one I think nearly every scientist has been guilty of at one time or another. You start an experiment, have a few samples, and it looks like there is an amazing result! You get so excited and want to publish immediately! You’re about to win the Nobel Prize!!! However, as you do the more robust experiments (with more samples), those exciting results start to look less exciting. This has happened to me on multiple occasions in my research (minus the Nobel Prize bit). The need for an adequate sample size (with proper controls) is a real thing… but I think it’s hard to appreciate how important this is until you’ve been fooled by your own data.

4. There is a big difference between studies done in human cells in a dish (in vitro), in
animals, and in real breathing humans.

The vast majority of treatments that look promising in a dish or in animals end up failing in humans. If you’re curious why this is (besides the obvious fact that neither cells in a dish nor mice can capture the full biological complexity of a human being), check out the book Rigor Mortis: How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Billions (it’s not nearly as depressing as that title sounds). But the tl;dr version is — wait for the human clinical trials to decide if a drug really works.

5. Scientific and medical expertise does not readily transfer fields.

An expert in physics is not the right person to critique an epidemiological model predicting how many COVID-19 cases there are going to be. Not all medical doctors are trained to do scientific research and design experiments. And a researcher (PhD) may know everything there is to know about the biology of a specific virus, but they do not know how to treat a decompensating ICU patient sick with that virus. Do your best to listen to people who are speaking out of their expertise and who know their own limitations.

6. It takes a long, long time to be sure of anything in science.

Most of the things we “know” in science are based on hundreds (if not thousands) of studies from research that has been building for decades. We scientists do get excited about a single new study that shows something surprising and new, but we always take it with a big grain of salt, because we know that no single study is perfect. Instead we rely on evidence from many studies before a scientific hypothesis is converted to a scientific fact.

UV Light and Disinfectants as COVID Treatments?

UV Light and Disinfectants as COVID Treatments?

By Kristen Panthagani

There was lots of excitement and kerfuffle surrounding the idea of using UV light or disinfectants to treat coronavirus. Here is the science behind how these things kill viruses.

UV Light

UV light causes permanent damage to genetic material (like DNA and RNA) by changing its chemical structure. If enough UV light is shined on virus particles, it will break its genetic material, meaning it can’t replicate anymore. This is why UV light works to disinfect surfaces. Does that mean it could be used as a treatment in people? No… because UV light does the same thing to human genetic material. Med school 101: severe DNA damage = cancer and/or death. Additionally, it would be very hard to deliver the UV light to the cells infected with the virus – just shining UV light on a person would not work as the skin blocks it from entering the body. It is physically impossible to dissolve light in liquids so you can’t give people a UV light IV infusion… I guess people would have to inhale nanoparticles emitting UV light if you really wanted to deliver it to the lungs.. but if they did that it would severely damage the lungs, either leading to death now or lung cancer down the road. This is why it is not possible to use UV light to treat humans… it is a potent carcinogen.

Disinfectants (Bleach, Hydrogen Peroxide)

Bleach and hydrogen peroxide are very reactive molecules that like to react with everything… they will try to rearrange the chemical structure of everything they come into contact with. This is why they’re so good at disinfecting… they will just destroy everything in their path. And this of course is why they can’t be used as a treatment for humans… if they were injected into the blood stream, they would destroy the blood vessels before getting very far… if they were swallowed, they would destroy the mouth, esophagus, and stomach… if they were somehow vaporized and inhaled, they would destroy the lungs. Bleach and hydrogen peroxide are like a wrecking ball, and antivirals are like a sharpshooter… Yes the wrecking ball might destroy the virus, but it will destroy everything else along with it. Antivirals are precise and don’t attack the healthy human cells, they only attack the virus.