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Responsible modelling: Unit testing for infectious disease epidemiology

Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and...

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Detalles Bibliográficos
Autores principales: Lucas, Tim C.D., Pollington, Timothy M, Davis, Emma L, Hollingsworth, T Déirdre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7690327/
https://www.ncbi.nlm.nih.gov/pubmed/33307443
http://dx.doi.org/10.1016/j.epidem.2020.100425
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author Lucas, Tim C.D.
Pollington, Timothy M
Davis, Emma L
Hollingsworth, T Déirdre
author_facet Lucas, Tim C.D.
Pollington, Timothy M
Davis, Emma L
Hollingsworth, T Déirdre
author_sort Lucas, Tim C.D.
collection PubMed
description Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically obliged to make our code as free of error as possible. Unit testing is a coding method to avoid such bugs, but it is rarely used in epidemiology. We demonstrate how unit testing can handle the particular quirks of infectious disease models and aim to increase the uptake of this methodology in our field.
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spelling pubmed-76903272020-11-27 Responsible modelling: Unit testing for infectious disease epidemiology Lucas, Tim C.D. Pollington, Timothy M Davis, Emma L Hollingsworth, T Déirdre Epidemics Review Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically obliged to make our code as free of error as possible. Unit testing is a coding method to avoid such bugs, but it is rarely used in epidemiology. We demonstrate how unit testing can handle the particular quirks of infectious disease models and aim to increase the uptake of this methodology in our field. Elsevier 2020-12 /pmc/articles/PMC7690327/ /pubmed/33307443 http://dx.doi.org/10.1016/j.epidem.2020.100425 Text en © 2020 The Authors http://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 Review
Lucas, Tim C.D.
Pollington, Timothy M
Davis, Emma L
Hollingsworth, T Déirdre
Responsible modelling: Unit testing for infectious disease epidemiology
title Responsible modelling: Unit testing for infectious disease epidemiology
title_full Responsible modelling: Unit testing for infectious disease epidemiology
title_fullStr Responsible modelling: Unit testing for infectious disease epidemiology
title_full_unstemmed Responsible modelling: Unit testing for infectious disease epidemiology
title_short Responsible modelling: Unit testing for infectious disease epidemiology
title_sort responsible modelling: unit testing for infectious disease epidemiology
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7690327/
https://www.ncbi.nlm.nih.gov/pubmed/33307443
http://dx.doi.org/10.1016/j.epidem.2020.100425
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