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A Bayesian model for repeated cross-sectional epidemic prevalence survey data

Fig 5

Estimates of the growth rate r_t, prevalence P_t, and predictive swab positivity , for SARS-CoV-2 in England between 1 May 2020 and 31 March 2022 using data from the REACT-1 study.

All three approaches are fit assuming a beta-binomial observation distribution. Solid coloured lines show the posterior means while shading and dashed lines show 95% credible intervals (of the posterior distribution for r_t and P_t, and of the posterior predictive distribution for ). Independent daily confidence intervals from the Agresti-Coull method [14] for P_t are shown in vertical grey lines. The data, daily observed swab positivity , are shown in black points. Grey shading indicates the periods in which sampling was conducted. The predictive distribution for swab positivity depends on the number of swabs taken each day n_t, which tends to be lower in the early and late periods of each sampling round, hence the wider credible intervals at the boundaries of each study round.

doi: https://doi.org/10.1371/journal.pcbi.1013515.g005