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During the COVID-19 pandemic, many institutions such as universities andworkplaces implemented testing regimens with every member of some populationtested longitudinally, and those testing positive isolated for some time.Although the primary purpose of such regimens was to suppress disease spread byidentifying and isolating infectious individuals, testing results were oftenalso used to obtain prevalence and incidence estimates. Such estimates arehelpful in risk assessment and institutional planning and various estimationprocedures have been implemented, ranging from simple test-positive rates tocomplex dynamical modeling. Unfortunately, the popular test-positive rate is abiased estimator of prevalence under many seemingly innocuous longitudinaltesting regimens with isolation. We illustrate how such bias arises andidentify conditions under which the test-positive rate is unbiased. Further, weidentify weaker conditions under which prevalence is identifiable and propose anew estimator of prevalence under longitudinal testing. We evaluate theproposed estimation procedure via simulation study and illustrate its use on adataset derived by anonymizing testing data from The Ohio State University.

Overcoming Repeated Testing Schedule Bias in Estimates of Disease Prevalence