Work

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

Year: 2020

Type: article

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.

Source: Infectious Disease Modelling

DOI: 10.1016/j.idm.2020.06.008

Authors: 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

Institutions: University of Liverpool, University of Manchester, Manchester Academic Health Science Centre, Salford Royal Hospital, NIHR Manchester Biomedical Research Centre, London School of Hygiene & Tropical Medicine, Manchester University NHS Foundation Trust, Wythenshawe Hospital, The Alan Turing Institute, Public Health England, Sci-Tech Daresbury, North Manchester General Hospital

Language: English

Topic: COVID-19 epidemiological studies

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,100

Funders: Wellcome Trust, Royal Society, Manchester Biomedical Research Centre, Medical Research Council, Engineering and Physical Sciences Research Council

Awards: G2411624253, Epidemic modelling and statistical support for policy: sub-populations, forecasting, and long-term planning, Epidemiological and evolutionary consequences of coinfection: a multi-scale modelling approach, The Alan Turing Institute, G489460497, G5233757163, Use of dynamic network models to explore the role of social media use in HIV transmission and health promotion among gay men and other MSM, G763723936, G8372486692, G8597009950, Operationalising Modern Mathematical Epidemiology, G9000301346

OpenAlex ID: W3040468796

FWCI: 2.374

Cites: 64

Cited by: 89

Related to: 10