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 two subsequent rounds (6-8 weeks) and single rounds (2-3 weeks) inferred using the Bayesian P-spline model were broadly consistent with estimates from a simple exponential model, with overlapping credible intervals. However, there were sometimes substantial differences in point estimates. The Bayesian P-spline model was further able to infer changes in R t over shorter periods tracking a temporary increase above one during late-May 2020, a gradual increase in R t over the summer of 2020 as restrictions were eased, and a reduction in R t during England’s second national lockdown followed by an increase as the Alpha variant surged. The model is robust against both under-fitting and over-fitting and is able to interpolate between periods of available data; it is a particularly versatile model when growth rate can change over small timescales, as in the current SARS-CoV-2 pandemic. This work highlights the importance of pairing robust methods with representative samples to track pandemics.
Keywords: Bayesian P-spline; COVID-19; Cross-sectional study; Reproduction number; SARS-CoV-2.
【저자키워드】 COVID-19, SARS-CoV-2., cross-sectional study, Reproduction number, Bayesian P-spline, 【초록키워드】 SARS-CoV-2, pandemic, Bayesian, lockdown, SARS-CoV-2 pandemic, Prevalence, Epidemic, Pandemics, Alpha variant, population immunity, estimate, assessment, utility, England, overlapping, growth, point estimates, National, reproduction, intervals, highlight, robust, Course, subsequent, changes in, increase in, reduction in, 【제목키워드】 Prevalence, estimate, growth,