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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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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. |
format | Online Article Text |
id | pubmed-7690327 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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