ABSTRACT
BackgroundThe 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 (PES) by novel variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). MethodsMathematical modeling was used to demonstrate the applicability of the test-negative, case-control study design to derive PES. Modeling was also used to investigate effects of bias in PES estimation. The test-negative design was applied to national-level testing data in Qatar to estimate PES for SARS-CoV-2 infection and to validate this design. ResultsApart from the very early phase of an epidemic, the difference between the test-negative estimate for PES and the true value of PES was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of PES even when PES began to wane after prior infection. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated PES, 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 PES. PES 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. ConclusionsThe test-negative design offers a feasible, robust method to estimate protection from prior infection in preventing reinfection.
