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...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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Elsevier
2020
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| _version_ | 1826284481387954176 |
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| author | Lucas, TCD Pollington, TM Davis, EL Hollingsworth, TD |
| author_facet | Lucas, TCD Pollington, TM Davis, EL Hollingsworth, TD |
| author_sort | Lucas, TCD |
| collection | OXFORD |
| 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. |
| first_indexed | 2024-03-07T01:14:29Z |
| format | Journal article |
| id | oxford-uuid:8e2f396e-b634-45a5-a377-6904449e3913 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:14:29Z |
| publishDate | 2020 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:8e2f396e-b634-45a5-a377-6904449e39132022-03-26T22:55:56ZResponsible modelling: unit testing for infectious disease epidemiology.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:8e2f396e-b634-45a5-a377-6904449e3913EnglishSymplectic ElementsElsevier2020Lucas, TCDPollington, TMDavis, ELHollingsworth, TDInfectious 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. |
| spellingShingle | Lucas, TCD Pollington, TM Davis, EL Hollingsworth, TD 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 |
| work_keys_str_mv | AT lucastcd responsiblemodellingunittestingforinfectiousdiseaseepidemiology AT pollingtontm responsiblemodellingunittestingforinfectiousdiseaseepidemiology AT davisel responsiblemodellingunittestingforinfectiousdiseaseepidemiology AT hollingsworthtd responsiblemodellingunittestingforinfectiousdiseaseepidemiology |