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Because testing regimens across the world have varied tremendously, the actual mortality and hospitalization rates of COVID-19 have been hard to pin down. But modeling by researchers at the University of Oxford could provide some welcome good news, even if the initial takeaway doesn’t seem so promising. According to hypothetical modeling from Oxford’s Evolutionary Ecology of Infectious Disease lab, half of the population of the United Kingdom may have already been infected with the coronavirus. If this modeling is confirmed in follow-up studies, that a minuscule number of those infected require hospital treatment, with a majority showing very minor symptoms, or none at all.
In the mathematical experiment, the researchers looked at the population who are at risk of a severe infection. While a risk rate of 0.1 percent suggested a high number of infected people — suggesting lower hospitalization and mortality rates — a higher risk rate of 1 percent implied the possibility of a more threatening virus at the population level. In the following chart, ρ represents the risk rate, with the dramatic yellow line representing the possibility of a majority of the U.K. population infected:
According to the mathematical modeling concerning the 0.1 percent scenario, the coronavirus arrived in mid-January at the latest, and spread undetected for over a month before the first cases were confirmed. Based on a susceptibility-infected-recovery model — a commonly used estimate in epidemiology — with data from case and death reports in the U.K. and Italy, the researchers determined that the initial “herd immunity” strategy of the U.K. government could have been sound. “I am surprised that there has been such unqualified acceptance of the Imperial model,” said lead researcher Sunetra Gupta, referring to an academic report predicting that up to 250,000 could be killed if the government maintained its plan to suppress the virus “but not get rid of it completely,” as the country’s chief scientific adviser put it. As of Monday, 87 people in the United Kingdom had died from the coronavirus; out of a total of 90,436 tests, 8,077 were positive.
To see if their math checks out, the Oxford team is now working with researchers at the Universities of Cambridge and Kent to begin antibody testing as soon as this week. “We need immediately to begin large-scale serological surveys — antibody testing — to assess what stage of the epidemic we are in now,” Gupta told the Financial Times.
In an interview with New York’s James Walsh, Pulitzer-winning infectious disease reporter Laurie Garrett explained the public-health necessity of antibody tests:
The most important thing is that that test can be a public-health tool. If we had this antibody test, we can go around randomly selecting people in New York City and find out how many New Yorkers, including right now, have had this virus in their bodies. Since we know the virus has never been in human beings before, anybody who has antibodies against it has been exposed since January.
If we can get this antibody test mass-produced — and I know they’re working on it right now — and put it into commercialization really quickly, this could be a game-changer for the whole pandemic. 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.
Though the Oxford modeling seems promising, like all academic studies reckoning with the coronavirus, it should be read by the public with caution. If antibody tests did not prove the epidemiologists’ best-case findings, the modeling could undercut the success of social distancing measures that public-health experts consider vital to stopping the spread of the virus. It does, however, reveal the necessity of having the proper scale of testing, so that governments can make policy determinations that reflect the actual rates of infection and hospitalization.
This post has been updated.