Overcoming Repeated Testing Schedule Bias in Estimates of Disease Prevalence
研究了在纵向检测方案中，隔离措施导致检测阳性率对患病率估计产生偏差的问题，提出了新的无偏估计方法，并通过模拟和俄亥俄州立大学数据验证。
During the COVID-19 pandemic, many institutions such as universities and workplaces implemented testing regimens with every member of some population tested longitudinally, and those testing positive isolated for some time. Although the primary purpose of such regimens was to suppress disease spread by identifying and isolating infectious individuals, testing results were often also used to obtain prevalence and incidence estimates. Such estimates are helpful in risk assessment and institutional planning and various estimation procedures have been implemented, ranging from simple test-positive rates to complex dynamical modeling. Unfortunately, the popular test-positive rate is a biased estimator of prevalence under many seemingly innocuous longitudinal testing regimens with isolation. We illustrate how such bias arises and identify conditions under which the test-positive rate is unbiased. Further, we identify weaker conditions under which prevalence is identifiable and propose a new estimator of prevalence under longitudinal testing. We evaluate the proposed estimation procedure via simulation study and illustrate its use on a dataset derived by anonymizing testing data from The Ohio State University. Supplementary materials for this article are available online.