Science Model Told To Say Masks Work Discovers Masks Work

Ladies and gentlemen—and you, too, Hagfish—I present to you Science! In the form of the picture that heads today’s post. This delightful children’s drawing, presumably winning the Special Needs Science annual Art Festival, becomes figure numero oneo in the peer-reviewed paper “Estimating the population-level effects of nonpharmaceutical interventions when transmission rates of COVID-19 vary by orders of magnitude from one contact to another” by Richard P Sear in Physical Review E.

What we see here is stanky noxious visible disease-ridden breath, containing particles of the very coronadoom itself, wafting their evil way through space toward a poor innocent unsuspecting proper-voting person. Science tells us, and follow me closely here, that the person hacking up a covid-loogie on the left has a better chance of infecting his housemate than sickie on the right because his housemate is standing farther away—and by a window!

Who knew?

Since spreading disease is an immoral act, all the people in the cartoon are white. But never mind.

I have reminded us innumerable times that, in Science, all models only say what they are told to say. Sear built a model to say that masks work in reducing the spread of fearful diseases from spreading. This is of interest to us because of the tendency of Experts to elevate Theory over Reality, which is a key indicator of The Science.

Now whether masks work is a fine question, the very kind of question science can answer. The only way to answer it is to stick masks on some people, and no masks on others, and watch what happens. Which was done, and it was discovered, many times, that masks are next to useless. There are exceptions, of course, like space suits and other full-body sealed contrivances, none of which are viable for a general population.

You can try to discover mask usefulness through models; I mean, it is not logically impossible. But since the point is to use masks in real life, the farther from real life the model is, the less accurate the model will be. You have had four years of panic, many still in that state, and you know how people use masks in real life. We should have learned by now that if masks worked, they would have worked.

We now have so much observational evidence about panicked use of masks, from all over the globe, that there doesn’t seem to be a need to do theoretical models. But you know academics. They can’t leave well enough alone.

To Sear’s model. After putting forth some equations on infectiousness, fluid flow, transmission rate and the like, Sear brings in observations.

So we expect that the transmission rate ? varies over orders of magnitude. I now turn to data from the United Kingdom’s National Health Service (NHS) COVID-19 app [21]. During the COVID-19 pandemic, this app ran on users’ mobile phones and used Bluetooth to detect other users’ mobile phones when they were nearby so it could estimate the length of a contact between two app users. Users also reported infections via the app. 

The weaknesses of this I will trust you can see for yourself, the biggest and most obvious is that it takes no account of the sea of other people with no app by the people with the app, never mind the self-reporting, all of which you’d guess would make the app of little or no value. But whatever.

Sear used this to propose a transmission rate model, lovingly detailing the “fat tails” of the math.

But where are the masks? They weren’t in the app. Sear introduces them thusly (my emphasis):

Now that I have a model for the distribution of transmission rates, Eq. (3), I can develop a simple predictive model for the effect of an NPI [non-pharmaceutical intervention]. I assume that an NPI removes/filters out of a fraction (1-f) of the virus and so scales all transmission rates r by a factor f…

Here’s the big move:

One NPI is the wearing of masks such as N95 or FFP2 masks [11]. N95 masks have been assigned a protection factor of ten, i.e., f=0.1 [15]. 

He works through the math (correctly), and then concludes:

The prediction is that for contacts of all durations, the reduction in risk due to wearing an FFP2/N95 is by a factor of approximately three.

A number which swells to nine when both sides of a conversation wear masks.

The big conclusion?

In conclusion, I have predicted that for a population like that of the UK, wearing FFP2/N95-type masks should reduce the effective reproduction number R by a factor of nine.

I hope at this late date I no longer have to harangue you that he input into his model that masks work, and that his model obligingly said masks work, and therefore his conclusion that masks work is entirely circular reasoning.

Even Sear seems to recognize this, and he, rare for this breed of work, has a small section called “Testing the prediction”. But it’s not what you think. He dismisses all previous observational trails of masks because they “neither disprove the prediction nor strongly support the prediction made here.” He says instead that because Reality-based trials are difficult and “challenging, we may have to rely on predictions such as that obtained here.”

No, Sear. We don’t have to rely on these predictions at all.

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