Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number.

ABSTRACT: The time-varying reproduction number (R_t) can change rapidly over the course of a pandemic due to changing restrictions, behaviours, and levels of population immunity. Many methods exist that allow the estimation of R_t from case data. However, these are not easily adapted to point prevalence data nor can they infer R_t across periods of missing data. We developed a Bayesian P-spline model suitable for fitting to a wide range of epidemic time-series, including point-prevalence data. We demonstrate the utility of the model by fitting to periodic daily SARS-CoV-2 swab-positivity data in England from the first 7 rounds (May 2020-December 2020) of the REal-time Assessment of Community Transmission-1 (REACT-1) study. Estimates of R_t over the period of t

SUBMITTER: Eales O 

PROVIDER: S-EPMC9220254 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature