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

Autor: Mutiso F; Division of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA., Pearce JL; Division of Environmental Health, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA., Benjamin-Neelon SE; Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.; Lerner Center for Public Health Promotion, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Mueller NT; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.; Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, MD, USA., Li H; Division of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA., Neelon B; Division of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.; Charleston Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson VA Health Care System, Charleston, SC, USA.
Jazyk: angličtina
Zdroj: Spatial statistics [Spat Stat] 2022 Dec; Vol. 52, pp. 100703. Date of Electronic Publication: 2022 Sep 23.
DOI: 10.1016/j.spasta.2022.100703
Abstrakt: 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 effects are assigned bivariate intrinsic conditionally autoregressive priors that promote spatial smoothing and permit the model components to borrow information, which is appealing when the mean and dispersion are spatially correlated. Through simulation studies, we show that ignoring heterogeneity in the dispersion can lead to biased and imprecise estimates. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis-Hastings steps. We apply the model to a study of COVID-19 incidence in the state of Georgia, USA from March 15 to December 31, 2020.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2022 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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