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Bayesian estimation of real-time epidemic growth rates using Gaussian processes : local dynamics of SARS-CoV-2 in England
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Guzmán-Rincón, Laura M., Hill, Edward M., Dyson, Louise, Tildesley, Michael J. and Keeling, Matt J. (2023) Bayesian estimation of real-time epidemic growth rates using Gaussian processes : local dynamics of SARS-CoV-2 in England. Journal of the Royal Statistical Society Series C: Applied Statistics . qlad056. doi:10.1093/jrsssc/qlad056 ISSN 1467-9876. (In Press)
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WRAP-Bayesian-estimation-real-time-epidemic-growth-rates-using-Gaussian-processes-local-dynamics-SARS-CoV-2-England-2023.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (4Mb) | Preview |
Official URL: http://dx.doi.org/10.1093/jrsssc/qlad056
Abstract
Quantitative assessments of the recent state of an epidemic and short-term projections for the near future are key public-health tools that have substantial policy impacts, helping to determine if existing control measures are sufficient or need to be strengthened. Key to these quantitative assessments is the ability to rapidly and robustly measure the speed with which an epidemic is growing or decaying. Frequently, epidemiological trends are addressed in terms of the (time-varying) reproductive number R. Here, we take a more parsimonious approach and calculate the exponential growth rate, r, using a Bayesian hierarchical model to fit a Gaussian process to the epidemiological data. We show how the method can be employed when only case data from positive tests are available, and the improvement gained by including the total number of tests as a measure of the heterogeneous testing effort. Although the methods are generic, we apply them to SARS-CoV-2 cases and testing in England, making use of the available high-resolution spatio-temporal data to determine long-term patterns of national growth, highlight regional growth, and spatial heterogeneity.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | Q Science > QA Mathematics R Medicine > RA Public aspects of medicine |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Mathematics |
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| Library of Congress Subject Headings (LCSH): | COVID-19 (Disease) -- England, COVID-19 (Disease) -- Epidemiology -- Mathematical models, Bayesian statistical decision theory, Multilevel models (Statistics), Gaussian processes, Public health -- Statistical methods | |||||||||||||||
| Journal or Publication Title: | Journal of the Royal Statistical Society Series C: Applied Statistics | |||||||||||||||
| Publisher: | Oxford University Press (OUP) | |||||||||||||||
| ISSN: | 1467-9876 | |||||||||||||||
| Official Date: | 27 June 2023 | |||||||||||||||
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| Article Number: | qlad056 | |||||||||||||||
| DOI: | 10.1093/jrsssc/qlad056 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | In Press | |||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
| Date of first compliant deposit: | 12 July 2023 | |||||||||||||||
| Date of first compliant Open Access: | 12 July 2023 | |||||||||||||||
| RIOXX Funder/Project Grant: |
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