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arxiv: 2109.13730 · v1 · pith:UMLYZKS4 · submitted 2021-09-28 · stat.ME · stat.AP

Interoperability of statistical models in pandemic preparedness: principles and reality

George Nicholson , Marta Blangiardo , Mark Briers , Peter J. Diggle , Tor Erlend Fjelde , Hong Ge , Robert J. B. Goudie , Radka Jersakova
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Ruairidh E. King Brieuc C. L. Lehmann Ann-Marie Mallon Tullia Padellini Yee Whye Teh Chris Holmes Sylvia Richardson
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classification stat.ME stat.AP
keywords interoperabilitypandemicstatisticaldiseasemodelspreparednessprinciplesadaptable
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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 spatial-temporal coronavirus disease 2019 (COVID-19) prevalence and reproduction numbers in England.

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