Open Access
May 2022 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, Ruairidh E. King, Brieuc C. L. Lehmann, Ann-Marie Mallon, Tullia Padellini, Yee Whye Teh, Chris Holmes, Sylvia Richardson
Author Affiliations +
Statist. Sci. 37(2): 183-206 (May 2022). DOI: 10.1214/22-STS854


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.

Funding Statement

MB acknowledges partial support from the MRC Centre for Environment and Health, which is currently funded by the Medical Research Council (MR/S019669/1). RJBG was funded by the UKRI Medical Research Council (MRC) [programme code MC_UU_00002/2] and supported by the NIHR Cambridge Biomedical Research Centre [BRC-1215-20014]. BCLL was supported by the UK Engineering and Physical Sciences Research Council through the Bayes4Health programme [Grant number EP/R018561/1] and gratefully acknowledges funding from Jesus College, Oxford. GN and CH acknowledge support from the Medical Research Council Programme Leaders award MC_UP_A390_1107. CH acknowledges support from The Alan Turing Institute, Health Data Research, U.K., and the U.K. Engineering and Physical Sciences Research Council through the Bayes4Health programme grant. SR is supported by MRC programme grant MC_UU_00002/10; The Alan Turing Institute grant: TU/B/000092; EPSRC Bayes4Health programme grant: EP/R018561/1. HG and TF acknowledge partial support from Huawei Research UK. Infrastructure support for the Department of Epidemiology and Biostatistics is also provided by the NIHR Imperial BRC. Authors in The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory gratefully acknowledge funding from Data, Analytics and Surveillance Group, a part of the UKHSA. This work was funded by The Department for Health and Social Care (Grant ref: 2020/045) with support from The Alan Turing Institute (EP/W037211/1) and in-kind support from The Royal Statistical Society. The computational aspects of this research were supported by the Wellcome Trust Core Award Grant Number 203141/Z/16/Z and the NIHR Oxford BRC. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR, Department of Health, Joint Biosecurity Centre, or PHE.


Chris Holmes and Sylvia Richardson contributed equally to this research.


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George Nicholson. Marta Blangiardo. Mark Briers. Peter J. Diggle. Tor Erlend Fjelde. Hong Ge. Robert J. B. Goudie. Radka Jersakova. Ruairidh E. King. Brieuc C. L. Lehmann. Ann-Marie Mallon. Tullia Padellini. Yee Whye Teh. Chris Holmes. Sylvia Richardson. "Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality." Statist. Sci. 37 (2) 183 - 206, May 2022.


Published: May 2022
First available in Project Euclid: 16 May 2022

MathSciNet: MR4422304
zbMATH: 07535199
Digital Object Identifier: 10.1214/22-STS854

Keywords: Bayesian graphical models , Bayesian melding , Covid-19 , evidence synthesis , interoperability , modularization , multi-source inference

Rights: This research was funded, in whole or in part, by UKRI and/or Plan S sponsored funding (details on grants are at the end of the paper). A CC BY 4.0 license is applied to this article arising from this submission, in accordance with the grant’s open access conditions.

Vol.37 • No. 2 • May 2022
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