At the same time as parts of Italy were being overwhelmed by COVID-19, the UK government was downgrading the risk from COVID-19

I have been reading a couple of reports looking at the UK government’s guidance on the use of Personal Protective Equipment (PPE) for healthcare workers whose patients may be infected with COVID-19. One report by Dinah Gould and Edward Purssell was commissioned by the Royal College of Nursing and is pretty damning of the process by which the guidance was drawn up and the (lack of) evidence cited. But it is the other report, by a Health and Safety consultant David Osborn*, that astonished me when I read it Friday evening. COVID-19, the disease that has killed over 100,000 people in the UK, and millions worldwide, is not classified by the UK government as a “high consequence infectious disease” (HCID). This surprises me. Even more remarkably, it was considered a HCID until mid-March 2020, when it was downgraded. If you don’t believe me (and I wouldn’t blame you here), the official UK government page is here. If you remember, March 2020 was about the time that COVID-19 was overwhelming the healthcare system of parts of northern Italy, forcing the Italian government to send in the army. On the face of it, downgrading the official risk classification of an infectious disease at the same time as that disease is overwhelming the healthcare system of another European country is a surprising decision.

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In scientific research you get what you pay for

We are in the middle of a global pandemic caused by a virus, SARS-CoV-2, that is transmitted in the air we breathe. Especially in the UK it is not going well, with over 1,000 people dying a day. A big part of the problem is political. But it is also true that our knowledge of how diseases that spread in the air we breathe, is very incomplete. This is, I think, contributing to both debates and decision making being poorly done. Examples I can think of include debates and decision making in important areas such as social distancing, mask wearing, and whether in the middle of a pandemic it is a good idea to encourage people to visits restaurants. So why is our knowledge of how diseases such as COVID-19 spread, so incomplete?

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Different perspectives on COVID-19’s attack rate

The attack rate of an infectious disease is the % chance that you contract it. I think it is term was introduced by epidemiologists. A high attack rate is bad of course, a lower one would be better, so we want to know what the attack rate depends on. Epidemiologists typically want to know how the attack rate depends on, for example, “age, symptom status, duration of exposure and household size” — see a recent preprint by Prof Neil Ferguson at Imperial College and coworkers. So here the questions are: Are children more likely to become infected than the elderly? Does longer contact with an infected person increase the chance of infection? And so on. Aerosol scientists such as Prof Jose Jimenez, and at least some medics, have a different perspective on what determines the attack rate. Prof Jimenez has a Google Sheets that estimates attack rates. But here the assumption (not question) is that the attack rate depends on duration, but not age, as well as on other factors such as ventilation. I am wondering about these two different perspectives on the same problem.

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Scientific-looking claims for masks

One of my Christmas presents was a mask. A thoughtful present, although a mask as a Christmas present says a lot about the sort of year 2020 was. Anyway, the mask’s packaging makes a number of scientific-looking claims, and as I have been working on understanding how masks work (preprints here and here), I thought I would go through them. Maybe the manufacturer of your mask makes similar claims.

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We’re all different, and that (probably) matters for infection with COVID-19

Felipe IV de castaño y plata, by Diego Velázquez

Above is Diego Velazquez’s portrait of Philip IV of Spain. He is not the best looking royal, and has what is sometimes called the Habsburg jaw, i.e., a very large jaw, attributed in this case to a lot of cousin marrying by his royal dynasty, the Habsburgs. They were rather inbred. Lab experiments on how diseases are transmitted are often done on mice. Now you might think that lab mice have little in common with the Habsburgs, but in fact they do haveone thing in common: the mice are also inbred. I think biologists want to avoid the variability that would occur if the, say, 50 lab mice they are doing experiments on, have a lot of genetic variability. So they are deliberately inbred.

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When masks work, and when they don’t

With billions wearing face coverings, the question we want answered is: How effective are they at reducing the probability of you becoming infected with COVID-19? This question is very hard to answer. We can split it up into two questions: If you wear a mask, how much does this reduce the dose of virus you breathe in? And how does the chance of you becoming infected decrease, when the dose of virus you breathe in is reduced? Colleagues and I have had a go at answering the first question — preprint here. Here I will just assume that mask wearing reduces the dose by a factor of one half. This does not mean that the probability that you will become infected is reduced by one half. It is not that simple, unfortunately.

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How many SARS-CoV-2 viruses do we need to inhale to become infected?

We don’t know the answer to the question in the title of this post, maybe only a few viruses are enough, maybe it is a hundred or a thousand. In fact, I am not sure we know the answer to this question for any infectious disease. I don’t think we know how many TB bacteria need to be inhaled to become infected, or how many noroviruses you need to pick up to become infected with the norovirus — also sometimes known as the winter vomiting bug. But for the norovirus at least we have a little data on how the probability you become infected increases as you increase the dose — see data by Teunis and coworkers plotted above. Note that the units of dose above are basically arbitrary so the absolute units are irrelevant but what is relevant is that the x axis is a log scale, and the doses vary by a factor of 100 million. Also note the very large (estimated) error bars* (in blue).

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COVID-19 researchers learning from pioneers of TB research

On Wednesday I watched (via Zoom of course) a webinar by Prof Jose Jimenez: Airborne transmission of SARS-CoV-2, and how to protect ourselves: What we know now. You can watch it on YouTube. I learnt a lot from it. Jimenez and coworkers have repurposed a model originally developed for the transmission of TB through the air of a room, to the study of SARS-CoV-2. The model is called the Wells-Riley model, after two of its inventors. Jimenez and coworkers have even implemented it as a Google Sheets spreadsheet, so you can see what this model predicts yourself.

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Teaching students how to check code: Give someone a fish and you feed them for a day; teach someone to fish and you feed them for a lifetime

I am teaching second-year physics students computational physics; I have been doing this for 20 years. One of things that has frustrated me for a few years is students asking me to check results, when they just want to know if the answer is right or wrong. When I help students who ask this question I do try and take students through my reasoning. For example, if the correct result of the calculation is a Gaussian function, I briefly describe what a Gaussian looks like.

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