The Wall Street Journal Should Publish Better Data About Asymptomatic Coronavirus Cases

The Wall Street Journal Should Publish Better Data About Asymptomatic Coronavirus Cases

It is true that there are asymptomatic cases not being counted, and we need better testing and studies to refine our data. But this Wall Street Journal article is speculative.
Nathanael Blake

We all want reasons for hope right now. The Chinese coronavirus (COVID-19) bestrides the globe, inflicting pestilence, death and poverty upon us. Most of us are stuck at home, watching as our investments, jobs, and businesses shrink and disappear, and we wonder if the virus is really so dangerous as to be worth all of this. There is therefore an eager audience for claims that the virus is not so bad, and that we are overreacting and should mostly go back to life as normal.

Of course, we should reevaluate our response to the coronavirus as more data comes in. But we should also scrutinize claims about the dangers of the disease. For example, a recent article in the Wall Street Journal argues that current estimates of COVID-19’s lethality may be hugely overblown. If true, this would be wonderful news. Unfortunately, there are many reasons to doubt it.

The article’s thesis is simple: we may be massively undercounting coronavirus infections, leading to a massive overestimate of the fatality rate. There is some surface plausibility to this, as testing is certainly skewed toward those who are sick or known to have been exposed. Consequently, asymptomatic or mild cases of community transfer are being missed.

But for this to make a significant enough difference in the fatality rate there would need to be huge numbers of these uncounted cases, and most of the examples this article cites do not make that case. Indeed, some undermine it.

Evacuees Are Not a Representative Sample

The first example consists of planeloads of people evacuated from Wuhan. The first problem with this sample is that foreign citizens who were evacuated are obviously not a representative sample of the general Wuhan population. Secondly, this example takes the Chinese figures at face value, despite the Chinese communist government’s extensive history of deceit, both in general and as regards this virus.

It is also peculiar that, as regards this sample, the authors omit any follow-up regarding the rate of asymptomatic cases to deaths or cases requiring hospitalization. Perhaps those numbers were not available to them, but it would have provided an excellent data pool by which to test their theory, which requires a vast majority of cases to be asymptomatic.

This objection becomes even more pertinent with regard to their next example, the Italian town of Vò. This was the location of Italy’s first coronavirus death, which arose from an early cluster of cases. The town was locked down and everyone tested, a strategy that successfully eradicated the virus.

This data set provides an excellent test of the thesis that there is a vast number of uncounted, asymptomatic COVID-19 infections. But the authors do not discuss that. Instead, they extrapolate the prevalence of the disease in Vò across the entire province, despite the implausibility of a small town with an early outbreak cluster being representative of the region as a whole.

Poor Data on Asymptomatic Carriers

Perhaps the reason they do not address the data from Vò is that it undermines their argument. If their thesis were correct, we would expect an overwhelming number of asymptomatic cases out of those who tested positive. Instead, the Italian researchers report only that “asymptomatic or quasi-symptomatic subjects represent a good 70% of all virus-infected people.”

Another news story clarifies that the true asymptomatic rate was about half of those who were positive for the virus. So out of a sample of 90 people who were infected, half were asymptomatic, about 20 percent had mild symptoms, and the rest had significant symptoms, with one person dying. That rate is lower than the worst-case numbers coming out of Italy, but it is not far off from most global estimates, and it certainly does not show an overwhelming majority of asymptomatic cases.

In their final example, the authors turn to the United States and all but admit that they are peddling speculative junk science. They look at the number of NBA players who have tested positive, and extend the rate of infection among NBA players to the cities that host NBA teams. But as they admit, NBA players are a small, extremely non-representative sample. Extrapolating the NBA infection rate to host cities is as silly as assuming that the average height of NBA players is the norm for the rest of us.

It is comforting to believe that the coronavirus cases we catch are the tip of the infection iceberg, and that there are vast numbers of asymptomatic infections hidden from our sight. If there were dozens or even hundreds of unknown cases for every one we identify, then the horrifying estimates of COVID-19 fatality rates would be way off, and we could mostly get back to life as normal.

It is true that there are asymptomatic cases not being counted, and we need better testing and serological studies to find them and refine our data. But this Wall Street Journal article is speculative, rather than data driven. Even though it cites Vò as an example, it avoids referencing the details of that town’s data, which suggest the current conventional estimates regarding the disease are fairly accurate. Notably, this article also avoids any mention of South Korea, which has a very aggressive testing regime, yet still has a fatality rate over 1 percent.

This Wall Street Journal article provides little reason to believe that there is a vast hidden reservoir of mild or asymptomatic cases that would radically change the dreadful math of this plague for the better if we only knew about them. In the absence of better data, it was irresponsible of the authors and publishers to promote what are almost certainly false hopes.

Nathanael Blake is a senior contributor to The Federalist and a postdoctoral fellow at the Ethics and Public Policy Center.

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