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Bayesian negative binomial regression with spatially varying dispersion: Modeling COVID-19 incidence in Georgia.
Mutiso, Fedelis; Pearce, John L; Benjamin-Neelon, Sara E; Mueller, Noel T; Li, Hong; Neelon, Brian.
  • 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.
  • Mueller NT; Lerner Center for Public Health Promotion, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Li H; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Neelon B; Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, MD, USA.
Spat Stat ; 52: 100703, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2042145
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 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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Randomized controlled trials Language: English Journal: Spat Stat Year: 2022 Document Type: Article Affiliation country: J.spasta.2022.100703

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Randomized controlled trials Language: English Journal: Spat Stat Year: 2022 Document Type: Article Affiliation country: J.spasta.2022.100703