Abstract
AbstractThe 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 provides 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 timecourse 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.
Publisher
Cold Spring Harbor Laboratory
Reference39 articles.
1. A Novel Coronavirus from Patients with Pneumonia in China, 2019
2. World Health Organization. Novel Coronavirus (2019-nCoV) Situation Report - (2020). URL https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200121-sitrep-1-2019-ncov.pdf?sfvrsn=20a99c104. [Online] (Accessed: 25 July 2021).
3. World Health Organization. Coronavirus diseases (COVID-19) Situation Report - 196. (2020). URL https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200803-covid-19-sitrep-196-cleared.pdf?sfvrsn=8a8a3ca44. [Online] (Accessed: 25 July 2021).
4. UK Government. Scientific Pandemic Influenza Group on Modelling (SPI-M) (2020). URL https://www.gov.uk/government/groups/scientific-pandemic-influenza-subgroup-on-modelling. [Online] (Accessed: 25 July 2021).
Cited by
31 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献