Interoperability of statistical models in pandemic preparedness: principles and reality

DOI10.1214/22-STS854OpenAlexW3202542567MaRDI QIDQ2143942

Marta Blangiardo, Robert J. B. Goudie, Hong Ge, George Nicholson, Ruairidh E. King, Tor Erlend Fjelde, Christopher C. Holmes, Sylvia Richardson, Ann-Marie Mallon, Tullia Padellini, Radka Jersakova, Brieuc C. L. Lehmann, Mark Briers, Yee Whye Teh, Peter J. Diggle

Publication date: 31 May 2022

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2109.13730

zbMATH Keywords

evidence synthesismodularizationinteroperabilityCOVID-19Bayesian graphical modelsBayesian meldingmulti-source inference

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (3)

Data science in a time of crisis: lessons from the pandemic ⋮ Data, science, and global disasters ⋮ Variational inference for cutting feedback in misspecified models

Uses Software

• Julia
• Turing

Cites Work

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