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?