Coronavirus Testing: Accurate Data on Immunity Vital



Health workers start the process to test people in a car as they use a newly approved saliva-based coronavirus test at a site in Edison, N.J., April 15, 2020. (Eduardo Munoz/Reuters)

A number of NRO writers have offered today some valuable cautionary data about antibody testing and herd immunity.

Certainly, one cannot yet anticipate what ongoing and planned antibody testing in particular areas might reveal. Perhaps based on anecdotal new reports and a few samplings from abroad, we might expect anything: that from 0-3 percent of the population has been previously infected, to even 15 percent.

I don’t think anyone believes that the results will reveal absolute herd immunity, which can be defined by varying high percentages, according to the infectiousness and viability of particular viruses, is now on the immediate horizon.

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At least from their op-eds, interviews, and essays, many researchers who doubt the methodologies of pessimistic modeling believe that antibody testing could both correct flawed assumptions and offer some optimism — quite apart from the unlikely notion that a herd immunity of, say, 60-70 percent already exists, suggesting that the virus is just about kaput, at least for this year.

A modern bad flu peters out often, due to the combination of 30-60 million getting infected, a few million on their own social distancing, another 150-170 million being vaccinated and thus, depending on the quality of the vaccination, perhaps resulting in some of them becoming immune or better equipped to endure the infection, some with existing immunity from dozens of past flu exposures, and with some help from warming weather and more people outdoors.

Rather, the presence of antibodies — if they really do provide immunity or even do so for a few months — in even a small percentage of the population could have lots of consequences in our reactions to the epidemic and in a variety of ways. If optimistically in some areas 10-20 percent of the population was revealed to have developed antibodies, that likely fact would at least slow down the virus’s spread and also might offer one reason why it has not yet spiked in places such as California.

But far more important, accurate data on any level of existing immunity would be welcome in ways that transcend identifying those who likely could go back to work, or aiding doctors in their immediate treatment of those who present with coronavirus-like symptoms.

In the current epidemic, the denominator of cases is determined by the number of recorded positive test results. But as experts have pointed out ad nauseam, such a misleadingly precise number is widely acknowledged as inaccurate.

In theory, given constant Chinese backdating and warping of data, the virus could have been here before late January, its official American patient-zero date, and thus already infected thousands, and/or the more recently and currently infected from late January onward either may have not known they were sick or attributed their symptoms to flu-like illnesses. In any case, antibody testing might calibrate how large the infected pool is both in real numbers and in relation to those positive cases currently used to determine both the total number of infected and the lethality of the virus.

In practical terms, such a development would mean that the various scary hourly meters of death and infections by the virus on the Internet, at least in their present form, would be rendered mostly worthless and in immediate need of recalibration.

We certainly do not approximate annual flu cases by counting up only the number of those who feel or are sick who test positive for influenza at doctors’ offices or emergency rooms. We instead prefer to use models, which often first try to count the number of deaths that were likely mostly caused by the flu, and then apply prior common fatality percentages to guess about the pool of infected — explaining why each year the cautious CDC often offers more precise figures about deaths from the flu than infections from the flu.

In the case of the coronavirus, we could end the unfortunate current practice of using a precise but misleading number as the denominator that can mislead us both about both case to fatality figures and the number of those infected.

Once some antibody tests are done, and the initial studies gain credibility for reflecting representative populations based on age, sex, race, health, geography, etc., then the approach to the virus could change.

The California paradox, for example, continues as the state of 40 million currently has suffered fewer than 900 deaths, or about 23 per million population. Except for Texas, no large state has gotten off so far so lightly. Indeed, in terms of deaths per million, California has been more fortunate than large countries in Europe, including Germany, that are currently seen as a model of containment and thus gearing up to return to graduated normalcy.

Lots of researchers are curious why a state once modeled in end-of-days-fashion, and deemed so vulnerable given its exposure to Chinese visitation, its staggering number of residents living below the poverty level and dependent on state assistance, and its less than impressive per capita number of hospital beds, nurses, and doctors is not yet in extremis.

In discussions all sorts of logical and unlikely theories arise, from less dense California populations — at least in the suburbs and the state’s interior and north — and less reliance on public transportation, high-rise living, elevators, and other foci of infection, to the governor’s March 18/19 shut-down order, to warmer weather or poor or tardy data or a different strain of the virus, to speculation that waiting and seeing what really will hit California soon is more reflective of reality, and on and on.

But if antibody testing should suggest that even, say, 1, 2, or 3 percent of the state’s population had developed antibodies, the result would not just be that perhaps hundreds of thousands or so were now likely for a time immune from the disease, but a quite different perception of the virus in the public mind. After all, lockdowns prevent the spread of the disease, they do not necessarily affect its innate rate of lethality — other than giving temporary respite to assumed overtaxed hospitals and medical care.

If antibody tests were to suggest that, say, 400,000, 800,000 or 1.2 million Californians had antibodies, then the resulting denominator would imply that the virus is actually comparable to a bad flu year in its effects, resulting in the deaths of 1-2 of every 1,000 of the public, rather than say 3 of every 100.

Whether people return to work and restart the economy in time to prevent a depression depends not entirely on what President Trump or, in California, Gov. Newsom decide.

It ultimately hinges on whether the people who have been shut in their homes for a month listening to mostly doomsday modeling (e.g., the March 19 governor’s warning that without his draconian measures 25.5 million Californians could be infected within 8 weeks [a number at fluctuating fatality percentages could imply that over 800,000 state residents will be dead mid-May]) and media sensationalism, decide what to do. The people, not the politicians, will decide.

That is, do people dare to venture out to restaurants, take the bus, fly on airplanes, book a trip, see a movie, or have people over to dinner, or instead voluntarily continue partially or fully quarantining themselves, and thus ignoring public encouragement that it is wise or safe or necessary to resume normal lives for most who do not fall into vulnerable groups?

In other words, if the tests were seen as reliable and if they revealed possibly a 1-3 percent existing immunity, then the public might conclude that 2020 is, in fact, analogous to the 2017 flu season in at least some regards, which it weathered without the present economically catastrophic reactions. Curbing the number of cases by a continued lockdown, without knowing of how lethal the virus really is, may limit cases and by extension deaths, but not in any fashion that societies have traditionally dealt with contagions that may kill only 1-3 per thousand infected.

This is aside from considerations that tens of thousands would feel they had little chance of infection, or that they would likely bump into as many or more people who had antibodies than had tested positive for an active case of the infection, and some guarded optimism that medical researchers are making rapid progress in sizing up the virus and its effects.

Of course, debate won’t end.

Some will claim that pessimistic models were valuable in shocking public officials into draconian measures that alone rendered the virus to the lethal status of a bad flu — and thus must continue. So why trash the quarantines that allowed the discussion to return to work to proceed or indeed give us the opportunity to compare deaths to a bad flu year? Their point is that even if the virus could be found to be no more lethal than the flu, by curbing cases, deaths are also curbed, even if only 1-3 per 1000 infected.

Others will point out that the fatality to case percentage rate is not so important, given that this nasty virus can mysteriously kill on rare occasions those in their 30s-to-50s in a way not associated with the flu. It’s supposedly like a lone sniper that can take out anyone anytime, regardless of age, health, and location. After all, in 2017, when over 60,000 died from the flu, we did not read horrific stories of individual influenza suffering and tragic deaths in a way we daily read in the case of the current infection.

And still more will object that if quite radical changes in public hygiene were necessary just to reduce the virus to just possibly flu percentages, then it is inherently more contagious, dangerous, and lethal.

A few will insist that risking 60,000 deaths is not worth the gamble of restoring the economy, and that by inference our prior reactions to 1957 or 2017 were misguided and only now do we see our tragically enfeebled and derelict past responses.

Those could be valid public worries.

But right now, the nation’s purposes are twofold: Don’t escalate the virus into a true pandemic of 1957 or 1918 proportions, and don’t wreck the economy with untold health consequences for hundreds of millions and financial burdens for generations yet to come.

Throwing some 20 million people out of work, destroying trillions in liquidity, sending GDP into Depression-like descendance, and shutting up over 100 million people in their homes is having health consequences that could ripple out far more so than from the virus itself. We will soon start reading of them, and they will be frightening. Of course, economists will have to provide us with more persuasive models of current and impending economic damage than epidemiological statisticians so far have done with the virus.

If we learn from antibody testing that the coronavirus, while different from influenzas in many ways, with proper caution can still be reduced to something like a flu-like challenge, then the public can be coaxed back gradually to a normal life. And the economy will in a few months start to recovery its prior vitality, ensuring us the material and financial wherewithal to finish off the virus this year or next, while not endangering out collective health in ways that transcend the current threat.

NRO contributor Victor Davis Hanson is the Martin and Illie Anderson Senior Fellow at the Hoover Institution and the author, most recently, of The Case for Trump.


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