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

ABSTRACT: Epidemic prevalence surveys monitor the spread of an infectious disease by regularly testing representative samples of a population for infection. State-of-the-art Bayesian approaches for analysing epidemic survey data were constructed independently and under pressure during the COVID-19 pandemic. In this paper, we compare two existing approaches (one leveraging Bayesian P-splines and the other approximate Gaussian processes) with a novel approach (leveraging a random walk and fit using sequential Monte Carlo) for smoothing and performing inference on epidemic survey data. We use our simpler approach to investigate the impact of survey design and underlying epidemic dynamics on the quality of estimates. We then incorporate these considerations into the existing approaches and compare all t

SUBMITTER: Steyn N 

PROVIDER: S-EPMC12507252 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature