Communicating uncertainty in epidemic models

Ruth McCabe*, Mara D. Kont, Nora Schmit, Charles Whittaker, Alessandra Løchen, Patrick G.T. Walker, Azra C. Ghani, Neil M. Ferguson, Peter J. White, Christl A. Donnelly, Oliver J. Watson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

While mathematical models of disease transmission are widely used to inform public health decision-makers globally, the uncertainty inherent in results are often poorly communicated. We outline some potential sources of uncertainty in epidemic models, present traditional methods used to illustrate uncertainty and discuss alternative presentation formats used by modelling groups throughout the COVID-19 pandemic. Then, by drawing on the experience of our own recent modelling, we seek to contribute to the ongoing discussion of how to improve upon traditional methods used to visualise uncertainty by providing a suggestion of how this can be presented in a clear and simple manner.

Original languageEnglish
Article number100520
JournalEpidemics
Volume37
Early online date2 Nov 2021
DOIs
Publication statusPublished - Dec 2021

Bibliographical note

Funding Information: This work was supported by the National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections, a partnership between Public Health England (PHE), University of Oxford, University of Liverpool and Liverpool School of Tropical Medicine (grant number NIHR200907 to R.M. and C.A.D.); by the MRC Centre for Global Infectious Disease Analysis (grant number MR/R015600/1 to A.C.G. N.M.F. P.J.W. and C.A.D.), which is jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union (EU); by Community Jameel (to N.M.F.); by the Imperial College Medical Research Council Doctoral Training Partnership (to M.D.K. N.S. and C.W.); by the HPRU in Modelling and Health Economics, a partnership between PHE, Imperial College London and LSHTM (grant number NIHR200908 to N.M.F. and P.J.W.); and by the Wellcome Trust and FCDO (to O.J.W. P.G.T.W. A.C.G. and N.M.F). O.J.W. is also supported by the Schmidt Science Fellows, in partnership with the Rhodes Trust. The views expressed are those of the authors and not necessarily those of the UK Department of Health and Social Care, EU, FCDO, MRC, National Health Service, NIHR or PHE. The funding bodies had no role in the design of the study, analysis and interpretation of data and in writing the manuscript.

Open Access: . This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Publisher Copyright: © 2021 The Authors. Published by Elsevier B.V.

Citation: Ruth McCabe, Mara D. Kont, Nora Schmit, Charles Whittaker, Alessandra Løchen, Patrick G.T. Walker, Azra C. Ghani, Neil M. Ferguson, Peter J. White, Christl A. Donnelly, Oliver J. Watson, Communicating uncertainty in epidemic models, Epidemics, Volume 37, 2021, 100520, ISSN 1755-4365,

DOI: https://doi.org/10.1016/j.epidem.2021.100520.

Keywords

  • Communicating uncertainty
  • COVID-19
  • Data visualisation
  • Decision-making
  • Transmission modelling

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