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Communicating uncertainty in epidemic models

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 u...

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Detalles Bibliográficos
Autores principales: McCabe, Ruth, Kont, Mara D., Schmit, Nora, Whittaker, Charles, Løchen, Alessandra, Walker, Patrick G.T., Ghani, Azra C., Ferguson, Neil M., White, Peter J., Donnelly, Christl A., Watson, Oliver J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562068/
https://www.ncbi.nlm.nih.gov/pubmed/34749076
http://dx.doi.org/10.1016/j.epidem.2021.100520
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author McCabe, Ruth
Kont, Mara D.
Schmit, Nora
Whittaker, Charles
Løchen, Alessandra
Walker, Patrick G.T.
Ghani, Azra C.
Ferguson, Neil M.
White, Peter J.
Donnelly, Christl A.
Watson, Oliver J.
author_facet McCabe, Ruth
Kont, Mara D.
Schmit, Nora
Whittaker, Charles
Løchen, Alessandra
Walker, Patrick G.T.
Ghani, Azra C.
Ferguson, Neil M.
White, Peter J.
Donnelly, Christl A.
Watson, Oliver J.
author_sort McCabe, Ruth
collection PubMed
description 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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spelling pubmed-85620682021-11-02 Communicating uncertainty in epidemic models McCabe, Ruth Kont, Mara D. Schmit, Nora Whittaker, Charles Løchen, Alessandra Walker, Patrick G.T. Ghani, Azra C. Ferguson, Neil M. White, Peter J. Donnelly, Christl A. Watson, Oliver J. Epidemics Article 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. Elsevier 2021-12 /pmc/articles/PMC8562068/ /pubmed/34749076 http://dx.doi.org/10.1016/j.epidem.2021.100520 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
McCabe, Ruth
Kont, Mara D.
Schmit, Nora
Whittaker, Charles
Løchen, Alessandra
Walker, Patrick G.T.
Ghani, Azra C.
Ferguson, Neil M.
White, Peter J.
Donnelly, Christl A.
Watson, Oliver J.
Communicating uncertainty in epidemic models
title Communicating uncertainty in epidemic models
title_full Communicating uncertainty in epidemic models
title_fullStr Communicating uncertainty in epidemic models
title_full_unstemmed Communicating uncertainty in epidemic models
title_short Communicating uncertainty in epidemic models
title_sort communicating uncertainty in epidemic models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562068/
https://www.ncbi.nlm.nih.gov/pubmed/34749076
http://dx.doi.org/10.1016/j.epidem.2021.100520
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