Imperial College London
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ProfessorGrahamCooke

Faculty of Medicine, Department of Infectious Disease

Vice Dean (Research); Professor of Infectious Diseases
 
 
 
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Contact

 

g.cooke

 
 
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Location

 

Infectious Diseases SectionMedical SchoolSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Keeling:2022:10.1177/09622802211070257,
author = {Keeling, MJ and Dyson, L and Guyver-Fletcher, G and Holmes, A and Semple, MG and Tildesley, MJ and Hill, EM},
doi = {10.1177/09622802211070257},
journal = {Statistical Methods in Medical Research},
title = {Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number},
url = {http://dx.doi.org/10.1177/09622802211070257},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provide a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, R, has taken on special significance in terms of the general understanding of whether the epidemic is under control (R<1). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first wave (March–June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the time course of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.
AU  - Keeling,MJ
AU  - Dyson,L
AU  - Guyver-Fletcher,G
AU  - Holmes,A
AU  - Semple,MG
AU  - Tildesley,MJ
AU  - Hill,EM
DO  - 10.1177/09622802211070257
PY  - 2022///
SN  - 0962-2802
TI  - Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number
T2  - Statistical Methods in Medical Research
UR  - http://dx.doi.org/10.1177/09622802211070257
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000749890000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://journals.sagepub.com/doi/10.1177/09622802211070257
UR  - http://hdl.handle.net/10044/1/94927
ER  -
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