An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases.
Computational models of infectious diseases have become valuable tools for research and the public health response against epidemic threats. The reproducibility of computational models has been limited, undermining the scientific process and possibly trust in modeling results and related response st...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-03-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010856 |
| _version_ | 1797844174384398336 |
|---|---|
| author | Darya Pokutnaya Bruce Childers Alice E Arcury-Quandt Harry Hochheiser Willem G Van Panhuis |
| author_facet | Darya Pokutnaya Bruce Childers Alice E Arcury-Quandt Harry Hochheiser Willem G Van Panhuis |
| author_sort | Darya Pokutnaya |
| collection | DOAJ |
| description | Computational models of infectious diseases have become valuable tools for research and the public health response against epidemic threats. The reproducibility of computational models has been limited, undermining the scientific process and possibly trust in modeling results and related response strategies, such as vaccination. We translated published reproducibility guidelines from a wide range of scientific disciplines into an implementation framework for improving reproducibility of infectious disease computational models. The framework comprises 22 elements that should be described, grouped into 6 categories: computational environment, analytical software, model description, model implementation, data, and experimental protocol. The framework can be used by scientific communities to develop actionable tools for sharing computational models in a reproducible way. |
| first_indexed | 2024-04-09T17:19:09Z |
| format | Article |
| id | doaj.art-2af4c24e8f2b459fa5e4734ed2061e56 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-09T17:19:09Z |
| publishDate | 2023-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-2af4c24e8f2b459fa5e4734ed2061e562023-04-19T05:31:32ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-03-01193e101085610.1371/journal.pcbi.1010856An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases.Darya PokutnayaBruce ChildersAlice E Arcury-QuandtHarry HochheiserWillem G Van PanhuisComputational models of infectious diseases have become valuable tools for research and the public health response against epidemic threats. The reproducibility of computational models has been limited, undermining the scientific process and possibly trust in modeling results and related response strategies, such as vaccination. We translated published reproducibility guidelines from a wide range of scientific disciplines into an implementation framework for improving reproducibility of infectious disease computational models. The framework comprises 22 elements that should be described, grouped into 6 categories: computational environment, analytical software, model description, model implementation, data, and experimental protocol. The framework can be used by scientific communities to develop actionable tools for sharing computational models in a reproducible way.https://doi.org/10.1371/journal.pcbi.1010856 |
| spellingShingle | Darya Pokutnaya Bruce Childers Alice E Arcury-Quandt Harry Hochheiser Willem G Van Panhuis An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases. PLoS Computational Biology |
| title | An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases. |
| title_full | An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases. |
| title_fullStr | An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases. |
| title_full_unstemmed | An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases. |
| title_short | An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases. |
| title_sort | implementation framework to improve the transparency and reproducibility of computational models of infectious diseases |
| url | https://doi.org/10.1371/journal.pcbi.1010856 |
| work_keys_str_mv | AT daryapokutnaya animplementationframeworktoimprovethetransparencyandreproducibilityofcomputationalmodelsofinfectiousdiseases AT brucechilders animplementationframeworktoimprovethetransparencyandreproducibilityofcomputationalmodelsofinfectiousdiseases AT aliceearcuryquandt animplementationframeworktoimprovethetransparencyandreproducibilityofcomputationalmodelsofinfectiousdiseases AT harryhochheiser animplementationframeworktoimprovethetransparencyandreproducibilityofcomputationalmodelsofinfectiousdiseases AT willemgvanpanhuis animplementationframeworktoimprovethetransparencyandreproducibilityofcomputationalmodelsofinfectiousdiseases AT daryapokutnaya implementationframeworktoimprovethetransparencyandreproducibilityofcomputationalmodelsofinfectiousdiseases AT brucechilders implementationframeworktoimprovethetransparencyandreproducibilityofcomputationalmodelsofinfectiousdiseases AT aliceearcuryquandt implementationframeworktoimprovethetransparencyandreproducibilityofcomputationalmodelsofinfectiousdiseases AT harryhochheiser implementationframeworktoimprovethetransparencyandreproducibilityofcomputationalmodelsofinfectiousdiseases AT willemgvanpanhuis implementationframeworktoimprovethetransparencyandreproducibilityofcomputationalmodelsofinfectiousdiseases |