Title : Assessing Immunity from previous Infections in preventing reinfection: Applying the test-negative study design
Abstract:
Background: The COVID-19 pandemic has highlighted the need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection (PEs) by novel SARS-CoV-2 variants.
Methods: Mathematical modeling was used to demonstrate a theoretical foundation for applicability of the test-negative, case-control study design to derive. This modeling work is not intended to provide a simulation of a specific empirical study, but to provide a theoretical demonstration of the applicability of such design to derive credible estimates for, despite specific sources of bias.
Results: Apart from the very early phase of an epidemic, the difference between the test-negative estimate for and true value of was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of and its waning. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated, 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. The test-negative design was applied to national-level testing data in Qatar to estimate for SARS-CoV-2 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.
Conclusion: The test-negative design offers a feasible and robust method to estimate protection of prior infection in preventing reinfection. This method should be considered to provide rapid, rigorous estimates of protection offered by prior infection for different variants of SARS-CoV-2, including those that emerged recently.
