A Bayesian spatial-temporal varying coefficients model for estimating excess deaths associated with respiratory infections.

ABSTRACT: Disease surveillance data are used for monitoring and understanding disease burden, which provides valuable information in allocating health programme resources. Statistical methods play an important role in estimating disease burden since disease surveillance systems are prone to undercounting. This paper is motivated by the challenge of estimating mortality associated with respiratory infections (e.g. influenza and COVID-19) that are not ascertained from death certificates. We propose a Bayesian spatial-temporal model incorporating measures of infection activity to estimate excess deaths. Particularly, the inclusion of time-varying coefficients allows us to better characterize associations between infection activity and mortality counts time series. Software to implement this method is a

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12256124 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature