Interoperability of statistical models in pandemic preparedness: principles and reality
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Authors
Nicholson, George
Blangiardo, Marta
Briers, Mark
Diggle, Peter J
Fjelde, Tor Erlend
Ge, Hong
Jersakova, Radka
King, Ruairidh E
Lehmann, Brieuc CL
Mallon, Ann-Marie
Padellini, Tullia
Teh, Yee Whye
Holmes, Chris
Journal Title
Statistical Science
ISSN
0883-4237
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Nicholson, G., Blangiardo, M., Briers, M., Diggle, P. J., Fjelde, T. E., Ge, H., Goudie, R., et al. Interoperability of statistical models in pandemic preparedness: principles and reality. Statistical Science https://doi.org/10.17863/CAM.83325
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 spatial-temporal coronavirus disease
2019 (COVID-19) prevalence and reproduction numbers in England.
Keywords
stat.ME, stat.ME, stat.AP, 62P10
Sponsorship
Engineering and Physical Sciences Research Council (EP/R018561/1)
MRC (Unknown)
Embargo Lift Date
2025-04-07
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.83325
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335891
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