Estimating protection afforded by prior infection in preventing reinfection: Applying the test-negative study design

Abstract

Background: The Coronavirus Disease 2019 (COVID-19) pandemic has highlighted an urgent need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection ( PE S ) by novel variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods: Mathematical modeling was used to demonstrate the applicability of the test-negative, case-control study design to derive PE S . Modeling was also used to investigate effects of bias in PE S estimation. The test-negative design was applied to national-level testing data in Qatar to estimate PE S for SARS-CoV-2 infection and to validate this design. Results: Apart from the very early phase of an epidemic, the difference between the test-negative estimate for PE S and the true value of PE S was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of PE S even when PE S began to wane after prior infection. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated PE S , but the underestimate was considerable only when >50% of the population was ever infected. Misclassification of latent infection, misclassification of current active infection, and scale-up of vaccination all resulted in negligible bias in estimated PE S . PE S against SARS-CoV-2 Alpha and Beta variants was estimated at 97.0% (95% CI: 93.6-98.6) and 85.5% (95% CI: 82.4-88.1), respectively. These estimates were validated using a cohort study design. Conclusions: The test-negative design offers a feasible, robust method to estimate protection from prior infection in preventing reinfection.