| Literature DB >> 35664221 |
Chris Holmes1,2,3, Sylvia Richardson2,4, George Nicholson1, Marta Blangiardo5, Mark Briers1,5,6,7,2,3,8,4,9, Peter J Diggle8, Tor Erlend Fjelde9, Hong Ge9, Robert J B Goudie4, Radka Jersakova2, Ruairidh E King3, Brieuc C L Lehmann1, Ann-Marie Mallon3, Tullia Padellini5, Yee Whye Teh1.
Abstract
We present interoperability as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring and characterising spatial-temporal prevalence and reproduction numbers of SARS-CoV-2 infections in England.Entities:
Keywords: Bayesian graphical models; COVID-19; Evidence synthesis; Interoperability; Modularization; Multi-source inference
Year: 2022 PMID： 35664221 PMCID： PMC7612804 DOI： 10.1214/22-STS854
Source DB: PubMed Journal: Stat Sci ISSN： 0883-4237 Impact factor: 4.015