Journal article
Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number
- Abstract:
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.5MB, Terms of use)
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- Publisher copy:
- 10.1177/09622802211070257
Authors
- Publisher:
- SAGE Publications
- Journal:
- Statistical Methods in Medical Research More from this journal
- Volume:
- 31
- Issue:
- 9
- Pages:
- 1716–1737
- Publication date:
- 2022-01-17
- Acceptance date:
- 2021-11-29
- DOI:
- EISSN:
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1477-0334
- ISSN:
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0962-2802
- Pmid:
-
35037796
- Language:
-
English
- Keywords:
- Pubs id:
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1238688
- Local pid:
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pubs:1238688
- Deposit date:
-
2022-02-11
- ARK identifier:
Terms of use
- Copyright holder:
- Keeling et al.
- Copyright date:
- 2022
- Rights statement:
- ©2022 The Author(s). his article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
- Licence:
- CC Attribution (CC BY)
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