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

doi:10.1016/j.epidem.2021.100520

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 journal › Article › peer-review

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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 language	English
Article number	100520
Journal	Epidemics
Volume	37
Early online date	2 Nov 2021
DOIs	https://doi.org/10.1016/j.epidem.2021.100520
Publication status	Published - Dec 2021
Externally published	Yes

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

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.epidem.2021.100520Licence: CC BY

McCabe2021CommunicatingUncertaintyInEpidemicModelsEpidemicsFinal published version, 4.02 MBLicence: CC BY

Cite this

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title = "Communicating uncertainty in epidemic models",

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.",

keywords = "COVID-19, Communicating uncertainty, Data visualisation, Decision-making, Transmission modelling",

author = "Ruth McCabe and Kont, {Mara D.} and Nora Schmit and Charles Whittaker and Alessandra L{\o}chen and Walker, {Patrick G.T.} and Ghani, {Azra C.} and Ferguson, {Neil M.} and White, {Peter J.} and Donnelly, {Christl A.} and Watson, {Oliver J.}",

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: {\textcopyright} 2021 The Authors. Published by Elsevier B.V. Citation: Ruth McCabe, Mara D. Kont, Nora Schmit, Charles Whittaker, Alessandra L{\o}chen, 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.",

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AU - McCabe, Ruth

AU - Kont, Mara D.

AU - Schmit, Nora

AU - Whittaker, Charles

AU - Løchen, Alessandra

AU - Walker, Patrick G.T.

AU - Ghani, Azra C.

AU - Ferguson, Neil M.

AU - White, Peter J.

AU - Donnelly, Christl A.

AU - Watson, Oliver J.

N1 - 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.

PY - 2021/12

Y1 - 2021/12

N2 - 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.

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VL - 37

JO - Epidemics

JF - Epidemics

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Communicating uncertainty in epidemic models

Abstract

Bibliographical note

Keywords

UN SDGs

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