Estimation of Some Epidemiological Parameters With the COVID-19 Data of Mayotte
We study in this article some statistical methods to fit some epidemiological parameters. We first consider a fit of the probability distribution which underlines the serial interval distribution of the COVID-19 on a given set of data collected on the viral shedding in patients with laboratory-confi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2022.870080/full |
| _version_ | 1811315699456409600 |
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| author | Solym M. Manou-Abi Solym M. Manou-Abi Yousri Slaoui Julien Balicchi |
| author_facet | Solym M. Manou-Abi Solym M. Manou-Abi Yousri Slaoui Julien Balicchi |
| author_sort | Solym M. Manou-Abi |
| collection | DOAJ |
| description | We study in this article some statistical methods to fit some epidemiological parameters. We first consider a fit of the probability distribution which underlines the serial interval distribution of the COVID-19 on a given set of data collected on the viral shedding in patients with laboratory-confirmed. The best-fit model of the non negative serial interval distribution is given by a mixture of two Gamma distributions with different shapes and rates. Thus, we propose a modified version of the generation time function of the package R0. Second, we estimate the time-varying reproduction number in Mayotte. Using a justified mathematical learning model, we estimate the transmission parameters range values during the outbreak together with a sensitivity analysis. Finally, using some regression and forecasting methods, we give some learning models of the hospitalized, intensive care, and death cases over a given period. We end with a discussion and the limit of this study together with some forthcoming theoretical developments. |
| first_indexed | 2024-04-13T11:35:55Z |
| format | Article |
| id | doaj.art-1a261d145dfb40c4bf5224c173c3578d |
| institution | Directory Open Access Journal |
| issn | 2297-4687 |
| language | English |
| last_indexed | 2024-04-13T11:35:55Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj.art-1a261d145dfb40c4bf5224c173c3578d2022-12-22T02:48:27ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872022-08-01810.3389/fams.2022.870080870080Estimation of Some Epidemiological Parameters With the COVID-19 Data of MayotteSolym M. Manou-Abi0Solym M. Manou-Abi1Yousri Slaoui2Julien Balicchi3Institut Montpelliérain Alexander Grothendieck, Univ. Montpellier, CNRS, Montpellier, FranceCentre Universitaire de Formation et de Recherche, Dembeni, FranceLaboratoire de Mathématiques et Applications, UMR CNRS 7031, Poitiers, FranceAgence Régionale de Santé de Mayotte, Centre Kinga, Mamoudzou, FranceWe study in this article some statistical methods to fit some epidemiological parameters. We first consider a fit of the probability distribution which underlines the serial interval distribution of the COVID-19 on a given set of data collected on the viral shedding in patients with laboratory-confirmed. The best-fit model of the non negative serial interval distribution is given by a mixture of two Gamma distributions with different shapes and rates. Thus, we propose a modified version of the generation time function of the package R0. Second, we estimate the time-varying reproduction number in Mayotte. Using a justified mathematical learning model, we estimate the transmission parameters range values during the outbreak together with a sensitivity analysis. Finally, using some regression and forecasting methods, we give some learning models of the hospitalized, intensive care, and death cases over a given period. We end with a discussion and the limit of this study together with some forthcoming theoretical developments.https://www.frontiersin.org/articles/10.3389/fams.2022.870080/fullhealth statisticsparameter estimationreproduction numberepidemiologymodeltransmission rates |
| spellingShingle | Solym M. Manou-Abi Solym M. Manou-Abi Yousri Slaoui Julien Balicchi Estimation of Some Epidemiological Parameters With the COVID-19 Data of Mayotte Frontiers in Applied Mathematics and Statistics health statistics parameter estimation reproduction number epidemiology model transmission rates |
| title | Estimation of Some Epidemiological Parameters With the COVID-19 Data of Mayotte |
| title_full | Estimation of Some Epidemiological Parameters With the COVID-19 Data of Mayotte |
| title_fullStr | Estimation of Some Epidemiological Parameters With the COVID-19 Data of Mayotte |
| title_full_unstemmed | Estimation of Some Epidemiological Parameters With the COVID-19 Data of Mayotte |
| title_short | Estimation of Some Epidemiological Parameters With the COVID-19 Data of Mayotte |
| title_sort | estimation of some epidemiological parameters with the covid 19 data of mayotte |
| topic | health statistics parameter estimation reproduction number epidemiology model transmission rates |
| url | https://www.frontiersin.org/articles/10.3389/fams.2022.870080/full |
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