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Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example.
Overton, Christopher E; Stage, Helena B; Ahmad, Shazaad; Curran-Sebastian, Jacob; Dark, Paul; Das, Rajenki; Fearon, Elizabeth; Felton, Timothy; Fyles, Martyn; Gent, Nick; Hall, Ian; House, Thomas; Lewkowicz, Hugo; Pang, Xiaoxi; Pellis, Lorenzo; Sawko, Robert; Ustianowski, Andrew; Vekaria, Bindu; Webb, Luke.
  • Overton CE; Department of Mathematics, University of Manchester, UK.
  • Stage HB; Department of Mathematical Sciences, University of Liverpool, UK.
  • Ahmad S; Department of Mathematics, University of Manchester, UK.
  • Curran-Sebastian J; Department of Virology, Manchester Medical Microbiology Partnership, Manchester Foundation Trust, UK.
  • Dark P; Manchester Academic Health Sciences Centre, UK.
  • Das R; Department of Mathematics, University of Manchester, UK.
  • Fearon E; Division of Infection, Immunity and Respiratory Medicine, NIHR Biomedical Research Centre, University of Manchester, UK.
  • Felton T; Critical Care Unit, Salford Royal Hospital, Northern Care Alliance NHS Group, UK.
  • Fyles M; Department of Mathematics, University of Manchester, UK.
  • Gent N; Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK.
  • Hall I; Division of Infection, Immunity and Respiratory Medicine, NIHR Biomedical Research Centre, University of Manchester, UK.
  • House T; Intensive Care Unit, Wythenshawe Hospital, Manchester University NHS Foundation Trust, UK.
  • Lewkowicz H; Department of Mathematics, University of Manchester, UK.
  • Pang X; The Alan Turing Institute, UK.
  • Pellis L; Emergency Response Department, Public Health England, UK.
  • Sawko R; Department of Mathematics, University of Manchester, UK.
  • Ustianowski A; Emergency Response Department, Public Health England, UK.
  • Vekaria B; Department of Mathematics, University of Manchester, UK.
  • Webb L; IBM Research, Hartree Centre, SciTech Daresbury, UK.
Infect Dis Model ; 5: 409-441, 2020.
Article in English | MEDLINE | ID: covidwho-632576
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.
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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Infect Dis Model Year: 2020 Document Type: Article Affiliation country: J.idm.2020.06.008

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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Infect Dis Model Year: 2020 Document Type: Article Affiliation country: J.idm.2020.06.008