Bayesian, Universal COVID Testing

During the SARS-COV2 pandemic, there has been a persistent call for universal testing to better inform policy decisions. However, a little considered aspect of this call is the relationship between a tests accuracy and the tested demographic. What are the implications of frequent, universal testing in otherwise asymptomatic demographics? By applying Bayesian statistics, it becomes clear that as the odds of having COVID decreases, there is a non-linear increase in the odds that each positive test is, in fact, a false positive. This phenomenon has precedence in the historical narrative surrounding universal mammogram screening which is no longer recommended due to the unacceptably high rate of false positives. The solution to combat the inflation of false positives is also suggested by Bayesian statistics: intelligently integrating multiple COVID diagnostic tests and symptoms via Bayes Theorem, an approach conceptually similar to pre-screening for mammograms. This extra information is readily available (e.g. olfactory function and fever) and will minimize the economic and emotional costs incurred by false positives while simultaneously improving the information available for policy-makers. In summary, along with the push for universal testing should be an equally rigorous approach to interpreting the test results.