Let’s end the COVID-19 panic

What if the true COVID-19 case fatality rate is 50-85 times less than we were led to believe? That may be the case according to a recent study published on medRxiv. The authors state “many epidemic projections and policies addressing COVID-19 have been designed without seroprevalence data to inform epidemic parameters.” As I stated in my post The Perils of Predicting COVID-19, such data are absolutely crucial to knowing how serious the pandemic is. Without this data the fatality rate can’t be reliably estimated because we don’t have a denominator. The equation becomes

$$fatality\ rate= \frac{deaths}{???}.$$

Until now the experts have been using this equation to estimate the fatality rate

$$fatality\ rate= \frac{deaths}{confirmed\ cases},$$

which gives us a fatality rate of
$$fatality\ rate= \frac{45063}{825041}=5.4619\%$$based on European CDC data for the entire USA through 12am April 22, 2020. But these numbers are based on testing only those people with symptoms of COVID-19, hardly a random sample. And most of those tested are health workers or elderly patients with health problems from the tri-city area around New York City, the epicenter of the COVID-19 pandemic. At the very best this number represents an upper bound on the true rate of infection. What the study attempts to do is get a better estimate.

The study measured the seroprevalence of antibodies to SARS-CoV-2 in Santa Clara County.


On 4/3-4/4, 2020 they tested county residents for antibodies to SARS-CoV-2 using a lateral flow immunoassay. Participants were recruited using Facebook ads targeting a representative sample of the county by demographic and geographic characteristics. They report the prevalence of antibodies to SARS-CoV-2 in a sample of 3,330 people, adjusting for zip code, sex, and race/ethnicity. They also adjusted for test performance characteristics using 3 different estimates: (i) the test manufacturer’s data, (ii) a sample of 37 positive and 30 negative controls tested at Stanford, and (iii) a combination of both.


The unadjusted prevalence of antibodies to SARS-CoV-2 in Santa Clara County was 1.5%, and the population-weighted prevalence was 2.81%. Under the three scenarios for test performance characteristics, the population prevalence of COVID-19 in Santa Clara ranged from 2.49% to 4.16%.

“These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50-85-fold more than the number of confirmed cases.”


The population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection is much more widespread than indicated by the number of confirmed cases. Population prevalence estimates can now be used to calibrate epidemic and mortality projections.

Based on the study estimates the fatality rate becomes$$fatality\ rate= \frac{45063}{825041*50}=0.06\%\ to\ fatality\ rate= \frac{45063}{825041*85}=0.11\%.$$How accurate is this estimate? Not very. It’s based on a self-selected group in one county in a state that isn’t particularly hard hit by the coronavirus. But it strongly suggests that we keep our heads about us and obtain more data. Let’s question both the numerator and the denominator. Most of all let’s not panic.

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