Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.

ABSTRACT: Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even result in estimation biases. Here, we present a computational modelling framework that allows for the integration of reported case numbers with seroprevalence estimates obtained from representative population cohorts. To account for the time dependence of infection and testing rates, we embed flexible splines in an epidemiological model. The parameters of these splines are estimated, along with the other parameters, from the available data using a Ba

SUBMITTER: Contento L 

PROVIDER: S-EPMC10008049 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature