Multi-outputs Gaussian process for predicting Burkina Faso COVID-19 spread using correlations from the weather parameters
Rattachement africain : Burkina Faso. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
The novel coronavirus has affected all regions of the world, but each country has experienced different rates of infection. In West Africa, in particular, infection rates remain low as compared to other parts of the world. This heterogeneity in the spread of COVID-19 raises a lot of questions that are still unanswered. However, some studies point out that people's mobility, size of gatherings, rate of testing, and weather have a great impact on the COVID-19 spread. In this work, we first evaluate the correlation between meteorological parameters and COVID-19 cases using Spearman's rank correlation. Secondly, multi-output Gaussian processes (MOGP) are used to predict the daily confirmed COVID-19 cases by exploring its relationships with meteorological parameters. The number of daily reported COVID-19 cases, as well as, weather variables collected from March 9, 2020, to October 18, 2021, were used in the analysis. The weather variables considered in the analysis are the mean temperature, relative humidity, wind direction, insolation, precipitation, and wind speed. The predicting model was constructed exploiting the correlation between the data of the daily confirmed COVID-19 cases and data of the weather variables. The results show that a significant correlation between the daily confirmed COVID-19 cases was found with humidity, wind direction, wind speed, and insolation. These parameters are used to construct the predictive model using the Multi-Output Gaussian process (MOGP). Different combinations of the data of meteorological parameters together with the data of daily reported COVID-19 cases were used to derive different models. We found that the best predictor is obtained using the combination of Humidity and insolation. This model is then used to predict the daily confirmed COVID-19 cases knowing the humidity and Insolation.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Multi-outputs Gaussian process for predicting Burkina Faso COVID-19 spread using correlations from the weather parameters
- Date Crossref
- 01/09/2022
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Direction Générale de la Météorologie Burkina Faso (code pays fourni par la source)Institution
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École Polytechnique de Ouagadougou Institut du Génie Informatique et Telecom École Polytechnique de Ouagadougou, Burkina Faso (pays nommé en fin d’affiliation)Université ou école supérieure
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Agence Nationale de la Météorologie du Burkina Faso Burkina Faso (pays nommé en fin d’affiliation)Organisme public
Direction Générale de la Météorologie (Burkina Faso), Institut du Génie Informatique et Telecom — École Polytechnique de Ouagadougou (École Polytechnique de Ouagadougou, Burkina Faso) et Agence Nationale de la Météorologie du Burkina Faso (Burkina Faso). Pays d’affiliation : Burkina Faso.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.