Bayesian negative binomial regression with spatially varying dispersion: Modeling COVID-19 incidence in Georgia.

ABSTRACT: Overdispersed count data arise commonly in disease mapping and infectious disease studies. Typically, the level of overdispersion is assumed to be constant over time and space. In some applications, however, this assumption is violated, and in such cases, it is necessary to model the dispersion as a function of time and space in order to obtain valid inferences. Motivated by a study examining spatiotemporal patterns in COVID-19 incidence, we develop a Bayesian negative binomial model that accounts for heterogeneity in both the incidence rate and degree of overdispersion. To fully capture the heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects in both the mean and dispersion components of the model. The random eff

SUBMITTER: Mutiso F 

PROVIDER: S-EPMC9500097 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature