Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example.

Infectious Disease Modelling
Christopher E. OvertonLuke Webb

Abstract

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.

Citations

Apr 20, 2021·Royal Society Open Science·Francesca ScarabelJianhong Wu
Jun 1, 2021·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Martyn FylesThomas House
Jun 1, 2021·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Ian HallUNKNOWN University of Manchester COVID-19 Modelling Group and the Public Health England Modelling Team
Jun 1, 2021·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Lorenzo PellisUNKNOWN University of Manchester COVID-19 Modelling Group
Sep 28, 2021·Statistical Methods in Medical Research·Carolin VegvariPieter Trapman

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