Infectious Disease Modelling (Jan 2020)
Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example
- Christopher E. Overton,
- Helena B. Stage,
- Shazaad Ahmad,
- Jacob Curran-Sebastian,
- Paul Dark,
- Rajenki Das,
- Elizabeth Fearon,
- Timothy Felton,
- Martyn Fyles,
- Nick Gent,
- Ian Hall,
- Thomas House,
- Hugo Lewkowicz,
- Xiaoxi Pang,
- Lorenzo Pellis,
- Robert Sawko,
- Andrew Ustianowski,
- Bindu Vekaria,
- Luke Webb
Affiliations
- Christopher E. Overton
- Department of Mathematics, University of Manchester, UK; Department of Mathematical Sciences, University of Liverpool, UK; Corresponding author. Department of Mathematics, University of Manchester, UK.
- Helena B. Stage
- Department of Mathematics, University of Manchester, UK; Corresponding author.
- Shazaad Ahmad
- Department of Virology, Manchester Medical Microbiology Partnership, Manchester Foundation Trust, UK; Manchester Academic Health Sciences Centre, UK
- Jacob Curran-Sebastian
- Department of Mathematics, University of Manchester, UK
- Paul Dark
- Division of Infection, Immunity and Respiratory Medicine, NIHR Biomedical Research Centre, University of Manchester, UK; Critical Care Unit, Salford Royal Hospital, Northern Care Alliance NHS Group, UK
- Rajenki Das
- Department of Mathematics, University of Manchester, UK
- Elizabeth Fearon
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK
- Timothy Felton
- Division of Infection, Immunity and Respiratory Medicine, NIHR Biomedical Research Centre, University of Manchester, UK; Intensive Care Unit, Wythenshawe Hospital, Manchester University NHS Foundation Trust, UK
- Martyn Fyles
- Department of Mathematics, University of Manchester, UK; The Alan Turing Institute, UK
- Nick Gent
- Emergency Response Department, Public Health England, UK
- Ian Hall
- Department of Mathematics, University of Manchester, UK; Emergency Response Department, Public Health England, UK
- Thomas House
- Department of Mathematics, University of Manchester, UK; IBM Research, Hartree Centre, SciTech Daresbury, UK
- Hugo Lewkowicz
- Department of Health Sciences, University of Manchester, UK; Department of Mathematics, University of Manchester, UK
- Xiaoxi Pang
- Department of Mathematics, University of Manchester, UK
- Lorenzo Pellis
- Department of Mathematics, University of Manchester, UK
- Robert Sawko
- IBM Research, Hartree Centre, SciTech Daresbury, UK
- Andrew Ustianowski
- Regional Infectious Diseases Unit, North Manchester General Hospital, UK; School of Medical Sciences, University of Manchester, UK
- Bindu Vekaria
- Department of Mathematics, University of Manchester, UK
- Luke Webb
- Department of Mathematics, University of Manchester, UK
- Journal volume & issue
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Vol. 5
pp. 409 – 441
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.
