During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic.
Bibliographical noteFunding Information:
PD and TF are supported by the NIHR Manchester Biomedical Research Centre . LP, HS and CO are funded by the Wellcome Trust and the Royal Society (grant 202562/Z/16/Z ). EF is funded by the MRC (grant MR/S020462/1 ). MF supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1 . TH is supported by the Royal Society (Grant Number INF/R2/180067 ) and Alan Turing Institute for Data Science and Artificial Intelligence . IH is supported by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response and the National Institute for Health Research Policy Research Programme in Operational Research (OPERA) and Alan Turing Institute for Data Science and Artificial Intelligence.
Open Access: This is an open access article under the CC BY license.
Publisher Copyright:© 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.
Citation: Overton, Christopher E., et al. "Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example." Infectious Disease Modelling 5 (2020): 409-441.
- Epidemic modelling
- Parameter estimation