ePyDGGA: automatic configuration for fitting epidemic curves
Abstract Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-43958-2 |
| _version_ | 1797355769744588800 |
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| author | Josep Alòs Carlos Ansótegui Ivan Dotu Manuel García-Herranz Pol Pastells Eduard Torres |
| author_facet | Josep Alòs Carlos Ansótegui Ivan Dotu Manuel García-Herranz Pol Pastells Eduard Torres |
| author_sort | Josep Alòs |
| collection | DOAJ |
| description | Abstract Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provide a model-agnostic framework for epidemic parameter fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the fitted parameters. Briefly, we have developed a Python framework that expects a Python function (epidemic model) and epidemic data and performs parameter fitting using automatic configuration. Our framework is capable of fitting parameters for any type of epidemic model, as long as it is provided as a Python function (or even in a different programming language). Moreover, we provide the code for different types of models, as well as the implementation of 4 concrete models with data to fit them. Documentation, code and examples can be found at https://ulog.udl.cat/static/doc/epidemic-gga/html/index.html . |
| first_indexed | 2024-03-08T14:15:50Z |
| format | Article |
| id | doaj.art-1b06186294c54861a0ea1393c2b45109 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T14:15:50Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-1b06186294c54861a0ea1393c2b451092024-01-14T12:23:45ZengNature PortfolioScientific Reports2045-23222024-01-0114111310.1038/s41598-023-43958-2ePyDGGA: automatic configuration for fitting epidemic curvesJosep Alòs0Carlos Ansótegui1Ivan Dotu2Manuel García-Herranz3Pol PastellsEduard Torres4Logic and Optimization Group, University of LleidaLogic and Optimization Group, University of LleidaGiga, UNICEFFrontier Data Technologies Unit, UNICEFLogic and Optimization Group, University of LleidaAbstract Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provide a model-agnostic framework for epidemic parameter fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the fitted parameters. Briefly, we have developed a Python framework that expects a Python function (epidemic model) and epidemic data and performs parameter fitting using automatic configuration. Our framework is capable of fitting parameters for any type of epidemic model, as long as it is provided as a Python function (or even in a different programming language). Moreover, we provide the code for different types of models, as well as the implementation of 4 concrete models with data to fit them. Documentation, code and examples can be found at https://ulog.udl.cat/static/doc/epidemic-gga/html/index.html .https://doi.org/10.1038/s41598-023-43958-2 |
| spellingShingle | Josep Alòs Carlos Ansótegui Ivan Dotu Manuel García-Herranz Pol Pastells Eduard Torres ePyDGGA: automatic configuration for fitting epidemic curves Scientific Reports |
| title | ePyDGGA: automatic configuration for fitting epidemic curves |
| title_full | ePyDGGA: automatic configuration for fitting epidemic curves |
| title_fullStr | ePyDGGA: automatic configuration for fitting epidemic curves |
| title_full_unstemmed | ePyDGGA: automatic configuration for fitting epidemic curves |
| title_short | ePyDGGA: automatic configuration for fitting epidemic curves |
| title_sort | epydgga automatic configuration for fitting epidemic curves |
| url | https://doi.org/10.1038/s41598-023-43958-2 |
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