Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number. (September 2022)

• Record Type: Journal Article
• Authors: Keeling, Matt J.; Dyson, Louise; Guyver-Fletcher, Glen; Holmes, Alex; Semple, Malcolm G; Tildesley, Michael J.; Hill, Edward M.
• Other Names: De Angelis Daniela guest-editor.; Birrell Paul guest-editor.; Funk Sebastian guest-editor.; House Thomas guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 31:Number 9(2022)
• Page Start: 1716
• Page End: 1737
• Publication Date: 2022-09
• Subjects: COVID-19 -- severe acute respiratory syndrome coronavirus 2 -- mathematical modelling -- Markov chain Monte Carlo -- Bayesian inference -- epidemiology -- growth rate -- reproduction number -- short-term forecasts; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/09622802211070257
• Languages: English
• ISSNs: 0962-2802

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.