Riding Herd On the Virus: Alternate Present Realities

Strategies for dealing with the virus hinge on two variables: contagion and mortality. How many people are getting infected? How many of them eventually die from the infection?

Estimated death rates vary widely by country. In Italy it’s 13%; in Germany, 2%; China, 4%; South Korea, 2%. In the US the death rate stands at 3.6 percent. Concerns have been expressed that corona-related deaths are under-reported: some die in the hospital before testing is administered; some die at home without ever being seen by a doctor. But these variations on the actual death count pale when compared with uncertainties about infection rate. In the US, tests are reserved almost exclusively for people whose symptoms have become so severe that they require hospitalization. How many of the infected never get that sick? I’ve calculated some estimates, but the numbers are based on one wild assumption compounded by another. A study published last month in Lancet estimated a mortality rate of 0.6 percent.

California has twice the population of New York but only an eighth as many confirmed diagnoses and only a twelfth as many deaths. The usual explanation is that California instituted social isolation measures sooner in the curve than did New York. Here’s a Stanford study, though, exploring the possibility that California developed a significant level of herd immunity in 2019 before the virus became recognized as a threat.

The state had a significant amount of travel to and from China last year – some 8,000 Chinese visitors a day at California’s airports. Wuhan, China is considered the origin point of the novel coronavirus. It first came to public attention there in December 2019, but researchers are looking at whether it had been around much earlier than that.

“Something is going on that we haven’t quite found out yet,” Hanson said. “When you calculate as well there were people on direct flights, from San Francisco and LAX to Wuhan, ground zero of the outbreak, you’d be naive not to think the California population wasn’t exposed.”

Epidemiological models assume that rigorous social isolation will keep the infection rate at around 5 percent of the population during this initial wave. Suppose it turns out that 50 percent of the California testing sample shows immunity. The flu infects about 20 percent of the population annually, and corona is presumed to be more than twice as contagious as the flu, so a 50% spread during this first wave might be plausible. In that case the mortality rate might be much lower than current estimates — maybe as low as  0.05 percent. Total COVID-related deaths in the US are projected to reach around 60,000 before this first wave of contagion passes through. Suppose half of the US population gets infected: 320 million x .5 x .0005 = 80,000 deaths. That’s not far off from the updated model projection of 60,000 — maybe that relatively modest level of reduced contagion and mortality could be attributed to social isolation.

The implications would be profound. Assuming an infection rate of 5%, then this current wave of contagions and deaths will be the first of several waves washing over the populace, unless the spread is stopped early by rigorous testing and isolation. But if the infection rate is 50%, then there might be only one or two much smaller waves to contend with over the next year or two, the first about as lethal as a typical flu season, the second much less so. The flu virus mutates radically every year, so people who develop immunity to this season’s flu aren’t protected from next year’s variant. In contrast, COVID seems not to be mutating nearly as rapidly as the flu in its spread through the world’s population. It’s likely then that those who develop immunity this time around will be protected from reinfection, at least until a vaccine becomes widely available. Herd immunity will be achieved, and the virus will for the most part extinguish itself for lack of non-immune host bodies to infect.

Trump says that widespread testing of infection and immunity isn’t necessary before rebooting the American economy. Is he just impulsive? Well of course he is. But does he also have access to data-based information indicating that the COVID infection rate is much higher than estimated while the mortality rate is much lower?

For what it’s worth, I find it hard to believe that a widespread 2019 corona contagion would have infected — and immunized — a large percentage of Californians without the virus having spread rapidly throughout the rest of the country. There might have been a lot of air travel between California and Wuhan, but there was far more travel between California and New York, Denver, Chicago… The flu mortality rate for the 2019-2020 season isn’t dramatically higher than prior years, so it seems unlikely that corona deaths wrongly attributed to flu would have been much of a factor.

The mastermind behind the Stanford study of California herd immunity is Victor Davis Hanson, a military historian and conservative political pundit who, according to one reviewer of Hanson’s 2019 book The Case for Trump, “likens Trump to a hero of ancient literature, sacrificing himself for the greater good.” Is it conceivable that Trump will point to Hanson’s study — which hasn’t generated any findings yet — as yet another reason to denigrate the scientific consensus as a fake-news conspiracy to derail his presidency by keeping the economy grounded indefinitely?

In order to calculate the case rate, the  mortality rate, and the contagion rate, you need an accurate measure of the number of people infected by the virus. It’s not necessary to test everyone: systematic sampling could generate an acceptable plus-or-minus estimate. The COVID diagnostic test continues to be plagued by lack of reagents and lab personnel, but the newly developed serologic tests for immunity are already out there,  relatively cheap to administer with results available in a matter of minutes rather than days. Population sampling would generate estimates of the percentages of people who have been infected and recovered — the denominator for accurately calculating mortality rates, as well as the numerator for accurately calculating contagion rates. These sampling studies can surely be conducted and the results analyzed before Trump and company decide when and how to reboot the economy in the most efficient and humane way possible.

Unless they’ve already decided.

 

 

 

2 thoughts on “Riding Herd On the Virus: Alternate Present Realities

  1. Here’s an article written by two Dartmouth professors explaining how random sampling of the population could provide accurate estimates of how many people have been infected and have developed immunity. This is such an obvious thing to do, it’s still hard for me to believe that it hasn’t already been done. It leads me into conspiracy territory: it’s been done, but the results are being withheld…

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  2. Here’s an antibody study in which the investigators estimate from a more-or-less random sample that maybe 3% of Santa Clara CA County residents are immune to covid. That’s about 50 times more than the number who have tested positive for the virus. In my first post in this series I estimated that the actual prevalence of the disease in the US population was about about 30 times the number of test-confirmed case — not too bad. I’ve since been scaling down my estimates for technical reasons, but what if this study’s results can be extrapolated to the national population? The current confirmed US case count stands at 710K: increase that number thirtyfold and you get 21 million, or over 6 percent of the population. The official count goes up by about 30K per day, which means the estimated actual prevalence increases by about 1 million daily. At that rate herd immunity will be achieved in less than a year, even if current social distancing measures remain in place — which they most certainly will not.

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