The end result of these various policy mis-steps is fear of asymptomatic
transmission leading to release of rushed and highly flawed tests
(Bandler et al. 2020), which were used to screen widely in asymptomatic
populations, which then seemed to find large numbers of asymptomatic
carriers (who were actually in the large majority of cases false
positives), creating a vicious cycle of faulty data.
This problem relates to more than just misidentifying positive COVID-19
cases; it also is relevant to data on hospitalizations and death rates.
After testing became widely available, it became standard practice to
test all patients admitted to hospitals in the U.S., regardless of
symptoms. While this may have been a necessarily cautious step in order
to minimize outbreaks in hospitals, it significantly inflated
hospitalizations and deaths attributed to COVID-19. A positive test
result was the primary basis for defining COVID-19 hospitalizations and
deaths since no symptoms were required to designate a COVID-19
hospitalization or death as such.
In other words, since the CDC and WHO case definitions took the
unprecedented step of defining a “confirmed case” as simply a positive
lab test result, and then most jurisdictions also defined a COVID-19
hospitalization and a COVID-19 death in the same manner, if the large
majority of positive test results are false positives, it is necessary
to re-examine the pandemic surveillance data chain from the beginning.
We’ll close with a homework problem for the reader: think through how to
apply this Bayesian thinking to the J&J baseline PCR testing, which as
mentioned above, found 0.5% baseline PCR test positives at the
beginning of the trial period. How many of those positive results were
true positives? How many were false positives?