Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality: Principles and Reality

George Nicholson; Marta Blangiardo; Mark Briers; Peter 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

doi:10.1214/22-sts854

Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality: Principles and Reality

George Nicholson, Marta Blangiardo, Mark Briers, Peter 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

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

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.

Original language	English
Pages (from-to)	183-206
Number of pages	24
Journal	Statistical Science
Volume	37
Issue number	2
DOIs	https://doi.org/10.1214/22-sts854
Publication status	Published - 1 May 2022
Externally published	Yes

Keywords

Bayesian graphical models
Bayesian melding
Covid-19
Evidence synthesis
Interoperability
Modularization
Multisource inference

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10.1214/22-sts854

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Nicholson, G., Blangiardo, M., Briers, M., Diggle, P., Fjelde, T. E., Ge, H., Goudie, R. J. B., Jersakova, R., King, R. E., Lehmann, B. C. L., Mallon, A. M., Padellini, T., Teh, Y. W., Holmes, C., & Richardson, S. (2022). Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality: Principles and Reality. Statistical Science, 37(2), 183-206. https://doi.org/10.1214/22-sts854

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title = "Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality: Principles and Reality",

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.",

keywords = "Bayesian graphical models, Bayesian melding, Covid-19, Evidence synthesis, Interoperability, Modularization, Multisource inference",

author = "George Nicholson and Marta Blangiardo and Mark Briers and Peter Diggle and Fjelde, \{Tor Erlend\} and Hong Ge and Goudie, \{Robert J.B.\} and Radka Jersakova and King, \{Ruairidh E.\} and Lehmann, \{Brieuc C.L.\} and Mallon, \{Ann Marie\} and Tullia Padellini and Teh, \{Yee Whye\} and Chris Holmes and Sylvia Richardson",

year = "2022",

month = may,

day = "1",

doi = "10.1214/22-sts854",

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Nicholson, G, Blangiardo, M, Briers, M, Diggle, P, Fjelde, TE, Ge, H, Goudie, RJB, Jersakova, R, King, RE, Lehmann, BCL, Mallon, AM, Padellini, T, Teh, YW, Holmes, C & Richardson, S 2022, 'Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality: Principles and Reality', Statistical Science, vol. 37, no. 2, pp. 183-206. https://doi.org/10.1214/22-sts854

TY - JOUR

T1 - Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality: Principles and Reality

AU - Nicholson, George

AU - Blangiardo, Marta

AU - Briers, Mark

AU - Diggle, Peter

AU - Fjelde, Tor Erlend

AU - Ge, Hong

AU - Goudie, Robert J.B.

AU - Jersakova, Radka

AU - King, Ruairidh E.

AU - Lehmann, Brieuc C.L.

AU - Mallon, Ann Marie

AU - Padellini, Tullia

AU - Teh, Yee Whye

AU - Holmes, Chris

AU - Richardson, Sylvia

PY - 2022/5/1

Y1 - 2022/5/1

N2 - 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.

AB - 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.

KW - Bayesian graphical models

KW - Bayesian melding

KW - Covid-19

KW - Evidence synthesis

KW - Interoperability

KW - Modularization

KW - Multisource inference

U2 - 10.1214/22-sts854

DO - 10.1214/22-sts854

M3 - Article

SN - 0883-4237

VL - 37

SP - 183

EP - 206

JO - Statistical Science

JF - Statistical Science

IS - 2

ER -

Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality: Principles and Reality

Abstract

Keywords

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