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

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...

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Detalles Bibliográficos
Autores principales: Mutiso, Fedelis, Pearce, John L., Benjamin-Neelon, Sara E., Mueller, Noel T., Li, Hong, Neelon, Brian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500097/
https://www.ncbi.nlm.nih.gov/pubmed/36168515
http://dx.doi.org/10.1016/j.spasta.2022.100703

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500097/
https://www.ncbi.nlm.nih.gov/pubmed/36168515
http://dx.doi.org/10.1016/j.spasta.2022.100703

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