Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19.

Yang, Xian; Wang, Shuo; Xing, Yuting; Li, Ling; Xu, Richard Yi Da; Friston, Karl J; Guo, Yike

Yang, Xian; Wang, Shuo; Xing, Yuting; Li, Ling; Xu, Richard Yi Da; Friston, Karl J; Guo, Yike.

Afiliación

Yang X; Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region, China.
Wang S; Data Science Institute, Imperial College London, London, United Kingdom.
Xing Y; Data Science Institute, Imperial College London, London, United Kingdom.
Li L; Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.
Xu RYD; Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, China.
Friston KJ; Data Science Institute, Imperial College London, London, United Kingdom.
Guo Y; School of Computing, University of Kent, Kent, United Kingdom.

PLoS Comput Biol ; 18(2): e1009807, 2022 02.

Article en En | MEDLINE | ID: mdl-35196320

Asunto(s)

Número Básico de Reproducción; Teorema de Bayes; COVID-19/epidemiología; SARS-CoV-2/aislamiento & purificación; Algoritmos; COVID-19/transmisión; COVID-19/virología; Humanos; SARS-CoV-2/fisiología

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Teorema de Bayes / Número Básico de Reproducción / SARS-CoV-2 / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: China

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