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Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality

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Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality. / Nicholson, George; Blangiardo, Marta; Briers, Mark et al.
In: Statistical Science, Vol. 37, No. 2, 01.05.2022, p. 183-206.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

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

APA

Nicholson, G., Blangiardo, M., Briers, M., Diggle, P. J., 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. Statistical Science, 37(2), 183-206. https://doi.org/10.1214/22-sts854

Vancouver

Nicholson G, Blangiardo M, Briers M, Diggle PJ, Fjelde TE, Ge H et al. Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality. Statistical Science. 2022 May 1;37(2):183-206. doi: 10.1214/22-sts854

Author

Nicholson, George ; Blangiardo, Marta ; Briers, Mark et al. / Interoperability of Statistical Models in Pandemic Preparedness : Principles and Reality. In: Statistical Science. 2022 ; Vol. 37, No. 2. pp. 183-206.

Bibtex

@article{82a712bbd22247a5aef1e454ea3a62fc,
title = "Interoperability of Statistical Models in Pandemic Preparedness: 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 = "Statistics, Probability and Uncertainty, General Mathematics, Statistics and Probability",
author = "George Nicholson and Marta Blangiardo and Mark Briers and Diggle, {Peter J.} 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 Ann-Marie Mallon and Tullia Padellini and Teh, {Yee Whye} and Chris Holmes and Sylvia Richardson",
year = "2022",
month = may,
day = "1",
doi = "10.1214/22-sts854",
language = "English",
volume = "37",
pages = "183--206",
journal = "Statistical Science",
issn = "0883-4237",
publisher = "Institute of Mathematical Statistics",
number = "2",

}

RIS

TY - JOUR

T1 - Interoperability of Statistical Models in Pandemic Preparedness

T2 - Principles and Reality

AU - Nicholson, George

AU - Blangiardo, Marta

AU - Briers, Mark

AU - Diggle, Peter J.

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 - Statistics, Probability and Uncertainty

KW - General Mathematics

KW - Statistics and Probability

U2 - 10.1214/22-sts854

DO - 10.1214/22-sts854

M3 - Journal article

VL - 37

SP - 183

EP - 206

JO - Statistical Science

JF - Statistical Science

SN - 0883-4237

IS - 2

ER -