Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis.

Kim, Joanne; Lawson, Andrew B; Neelon, Brian; Korte, Jeffrey E; Eberth, Jan M; Chowell, Gerardo

Kim, Joanne; Lawson, Andrew B; Neelon, Brian; Korte, Jeffrey E; Eberth, Jan M; Chowell, Gerardo.

Afiliação

Kim J; Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA. Joanne.Kim@osumc.edu.
Lawson AB; Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
Neelon B; Usher Institute, Centre for Population Health Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK.
Korte JE; Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
Eberth JM; Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
Chowell G; Department of Health Management and Policy, Drexel University, Philadelphia, PA, USA.

BMC Med Res Methodol ; 23(1): 171, 2023 07 22.

Article em En | MEDLINE | ID: mdl-37481553

Assuntos

COVID-19; Doenças Transmissíveis; Humanos; Teorema de Bayes; COVID-19/epidemiologia; Funções Verossimilhança; Pandemias; Estudos Prospectivos; Doenças Transmissíveis/epidemiologia

Palavras-chave

Bayesian spatiotemporal analysis; Infectious disease outbreak modeling; Public health surveillance

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Transmissíveis / COVID-19 Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Humans Idioma: En Revista: BMC Med Res Methodol Assunto da revista: MEDICINA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google