A Bayesian hierarchical approach to account for evidence and uncertainty in the modeling of infectious diseases: an application to COVID-19

DOI10.1002/BIMJ.202200341zbMATH Open1547.62422WikidataQ129203327 ScholiaQ129203327MaRDI QIDQ6625403

Sabine Hoffmann, Eva Rehfuess, Raphael Rehms, Ulrich Mansmann, Nicole Ellenbach, Jacob Burns

Publication date: 28 October 2024

Published in: Biometrical Journal (Search for Journal in Brave)

zbMATH Keywords

infectious diseaseshierarchical modelsBayesian modelingCOVID-19modeling uncertainty

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Inference from iterative simulation using multiple sequences
• Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011
• Handbook of Markov Chain Monte Carlo
• Monte Carlo sampling methods using Markov chains and their applications

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