The latest study is surprisingly encouraging


This would be fantastic news if true, although I’m having trouble squaring the observed reality here and in the UK with the thesis.

Remember this study, from Britain’s Imperial College? That’s the one that convinced Boris Johnson to abandon his plan to ask older people to stay home while letting younger, healthier people essentially go about their business and infect each other. Johnson’s hope was that, because younger adults are better able to weather infection without hospitalization, Britain’s health-care system wouldn’t be overtaxed as they got sick. Then, once more than half of the healthy population had been infected and “herd immunity” had begun to develop, it would gradually become safer to let the older people out of self-quarantine. The problem with that strategy according to the Imperial College is that there would be many thousands of deaths even among the younger, healthier group. Johnson quickly scrapped the plan and turned to aggressive social distancing measures.

Now here comes a study at the University of Oxford that poses a mind-boggling question: What if half of the British population is already infected with coronavirus? What if the percentage of infected people who need hospitalization for COVID-19 is actually teeny tiny because, unbeknownst to us, the overall infected population is actually enormously large? If half of Great Britain is already infected, the country is already well on its way to acquiring herd immunity, which means they may be seeing the worst of the epidemic right now. There’s no true “mass casualty” scenario as the virus spreads. It’s already spread. And it turns out it’s harmless in virtually everyone who gets it.

If the results are confirmed, they imply that fewer than one in a thousand of those infected with Covid-19 become ill enough to need hospital treatment, said Sunetra Gupta, professor of theoretical epidemiology, who led the study. The vast majority develop very mild symptoms or none at all…

The research presents a very different view of the epidemic to the modelling at Imperial College London, which has strongly influenced government policy. “I am surprised that there has been such unqualified acceptance of the Imperial model,” said Prof Gupta…

The Oxford study is based on a what is known as a “susceptibility-infected-recovered model” of Covid-19, built up from case and death reports from the UK and Italy. The researchers made what they regard as the most plausible assumptions about the behaviour of the virus…

If the findings are confirmed by testing, then the current restrictions could be removed much sooner than ministers have indicated.

Gupta didn’t criticize Johnson or his government for locking Britain down. It’s possible that her model is wrong, after all, and if it is, the consequences of letting the disease spread unchecked would be dire. Plus, social distancing measures are helping to limit the spread of the illness at a moment when it may be peaking, which is important to lighten the load on hospitals. Even if the virus only hits one in a thousand infected people hard, an enormous number will need hospitalization at a moment when half the population or so is infected. Locking the country down right now might be the prudent thing to do even if the Oxford model is correct.

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But it would also mean the lockdowns could end relatively soon and normalcy could return. There wouldn’t be many months to come of “managing” the epidemic. Essentially, Britain would already be near or at the top of the first, steep curve in this now famous graph:

Social distancing will help flatten that curve a *little.* But the UK is already getting over the hump, per the Oxford theory.

What do we make of this actual data, then? The theory, I suppose, would be that this graph doesn’t show infections rapidly beginning to take off, which is how it’s normally interpreted:

What it actually shows, assuming the Oxford model is correct, is the UK line approaching the top of the steep curve before it begins to descend. Or, better yet, it doesn’t show anything useful at all. If half the population is infected then the number of infections that’s being detected by laboratory testing is practically meaningless.

How do we know if the Oxford study is right or wrong? We take a “poll” of sorts. Head out into the field and perform serological tests on a *random* sample of the population. Those tests will show who has coronavirus antibodies in their system, the telltale sign of a person who’s been infected and fought the infection off. If half the sample turns out to have those antibodies, there you go. That’s the smoking gun that many, many, many more people have been infected than we realized and just shook off the disease without having had much of an inkling that they had it.

Two obvious questions, though. Is it realistically possible for the virus to have infected 30 million Brits in the two or three months since it made its way from China to the UK? Time imposes limits even on exponential growth. Unless, of course, COVID-19 is also more contagious than anyone has fully realized.

Second, if half the public is infected, why are so many people testing negative? If there’s widespread infection in the UK, presumably there’s also widespread infection in Seattle and Washington state, the site of the first major outbreak in the U.S. Testing at the University of Washington’s Virology Lab, though, shows a positive rate of 7.8 percent in testing, not anything like 50. Why so low if herd immunity is already well on its way towards being established?

The answer, I take it, would be that most infected people have already recovered from their very minor infections and have purged the virus from this system. It’s not going to show up in a swab; there’s nothing left of it in a recovered person’s mouth and nasal passages. The only evidence left in the patient is antibodies in the blood, which no one is testing right now. But if that’s what’s going on, where exactly is Seattle — and the UK — on the proverbial curve at this moment? If half or so of the population is infected right now, shouldn’t the virus be present and detectable in half of the nasal/mouth swabs collected? (Or more, since it tends to be sick people who are tested?) How is it that hospitalizations in New York are beginning to crest, a sign that we’re not yet at the top of the curve, and yet “only” 28 percent or so of tests are turning out positive?

Via New York mag, here’s how one reporter who specializes in covering disease outbreaks explained the importance of serological testing:

One of the things we would love to know right now is how many people who have had pneumonia since January were actually COVID cases? Having answers to that question would make a difference on a policy level. If we were suddenly seeing a surge in hidden pneumonia cases since mid-February, that would tell us we’re in deep, deep doo-doo; that this thing is like Italy; that we’re going to suddenly skyrocket and our hospitals are going to be overwhelmed. But if, by contrast, the same number of cases are found in the historic samples going back to the first of January, that would tell us, “Okay, it’s gradually unfolding, we don’t have to go down to lockdown every single person in New York, we may be able to flatten the curve.” And that makes a big difference in terms of how drastic our policies need to be. There’s a reason that the governor and the mayor and the mayor’s own department advisers are arguing. We just don’t have good, solid data to work with. We’re flying blind in New York City. They’re even blinder outside the city because the population’s more scattered.

Okay, but … if we find the same number of cases in blood samples from early January as we find in more recent samples, how would we explain the crunch in NYC emergency rooms right now? Something has changed in terms of severity in the last week or so. If the Oxford model is right, it may be that much of the city is already infected and we’re simply approaching the top of the first steep curve, with New York soon to see a peak that won’t be replicated again. If the Oxford model is wrong, hoo boy.

I sure hope it’s right, because these numbers are frightening. I’ll leave you with this.

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