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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8511| Fitting SIR model to COVID-19 pandemic data and comparative forecasting with machine learning | |
| Mouhamadou Aliou Mountaga Tall Baldé | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2020.04.26.20081042 | |
| https://www.medrxiv.org/content/10.1101/2020.04.26.20081042v1 | |
| In this work, we use a classical SIR model to study COVID-19 pandemic. We aim, to deal with the SIR model fitting to COVID-19 data by using different technics and tools. We particularly use two ways: the first one start by fitting the total number of the confirmed cases and the second use a parametric solver tool. Finally a comparative forecasting, machine learning tools, is given. | |
| bioRxiv | |
| 01-05-2020 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Fitting SIR model to COVID-19 pandemic data and comparative forecasting with machine learning.pdf | 751.78 kB | Adobe PDF | Visualizar/Abrir |