A Bayesian framework for modeling COVID‐19 case numbers through longitudinal monitoring of SARS‐CoV‐2 RNA in wastewater

Wastewater‐based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID‐19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibi...

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Veröffentlicht in:	Statistics in medicine 2024-03, Vol.43 (6), p.1153-1169
Hauptverfasser:	Dai, Xiaotian, Acosta, Nicole, Lu, Xuewen, Hubert, Casey R. J., Lee, Jangwoo, Frankowski, Kevin, Bautista, Maria A., Waddell, Barbara J., Du, Kristine, McCalder, Janine, Meddings, Jon, Ruecker, Norma, Williamson, Tyler, Southern, Danielle A., Hollman, Jordan, Achari, Gopal, Ryan, M. Cathryn, Hrudey, Steve E., Lee, Bonita E., Pang, Xiaoli, Clark, Rhonda G., Parkins, Michael D., Chekouo, Thierry
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayes Theorem Bayesian analysis Bayesian negative binomial regression Bayesian Poisson regression COVID-19 COVID-19 - epidemiology Data analysis Emergency medical care Epidemiology functional data analysis Humans Longitudinal studies Pandemics Public health Regression analysis RNA, Viral - genetics SARS-CoV-2 - genetics SARS‐CoV‐2 Wastewater wastewater epidemiology Water treatment
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