Estimating SARS-CoV-2 infection incidence and detection rates: Demonstrating a novel surveillance method

Abstract

ObjectivesAssessing the cumulative incidence of infection conventionally relies on documented infections or serological surveys, both of which have limitations. This study introduces a novel and practical method leveraging testing variation in a population to estimate SARS-CoV-2 infection rates in the population of Qatar. Study designCohort study and mathematical modeling. MethodsA cohort study was conducted from February 28, 2020, to March 04, 2024, to derive testing rates and estimate cumulative incidence of documented infection and hazard rates of documented infection in different testing groups. A deterministic mathematical model, applied to the cohort study data, was employed to simulate infection transmission, testing and infection documentation, and estimate the cumulative incidence of documented and undocumented infections, along with the infection detection rate. ResultsAt the conclusion of the pre-Omicron phase, the model-estimated cumulative incidence of documented infection, undocumented infection, and all infections was 9.8 %, 29.7 %, and 39.5 %, respectively. By the end of the first-Omicron wave, cumulatively from the onset of the pandemic, these figures rose to 16.9 %, 56.3 %, and 73.2 %, and in the post-first Omicron phase, to 18.8 %, 77.9 %, and 96.7 %, respectively. The infection detection rate in the population was 24.9 %, 21.0 %, and 9.1 % in each of the pre-Omicron phase, first-Omicron wave, and post-first Omicron phase, respectively. Uncertainty and sensitivity analyses confirmed these results. ConclusionsLeveraging readily available testing data, the introduced method was validated in a real-world setting and has the potential for diverse applications to enhance infectious disease surveillance for both emerging and endemic infections.