A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA

Surveillance data obtained by public health agencies for COVID-19 are likely inaccurate due to undercounting and misdiagnosing. Using a Bayesian approach, we sought to reduce bias in the estimates of prevalence of COVID-19 in Philadelphia, PA at the ZIP code level. After evaluating various modeling...

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Detalles Bibliográficos
Autores principales: Goldstein, Neal D., Wheeler, David C., Gustafson, Paul, Burstyn, Igor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7833121/
https://www.ncbi.nlm.nih.gov/pubmed/33509436
http://dx.doi.org/10.1016/j.sste.2021.100401

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7833121/
https://www.ncbi.nlm.nih.gov/pubmed/33509436
http://dx.doi.org/10.1016/j.sste.2021.100401

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