Gning Lucien, Ndour Cheikh, Tchuenche J M
Laboratoire d'études et de recherches en statistiques et développement, Université Gaston BERGER, Saint-Louis, Senegal.
Laboratoire de mathématiques et de leurs applications, Université de Pau et des Pays de l'Ardour, Pau, France.
Physica A. 2022 Jul 1;597:127245. doi: 10.1016/j.physa.2022.127245. Epub 2022 Mar 17.
The rapid spread of the COVID-19 pandemic has triggered substantial economic and social disruptions worldwide. The number of infection-induced deaths in Senegal in particular and West Africa in general are minimal when compared with the rest of the world. We use count regression (statistical) models such as the generalized Waring regression model to forecast the daily confirmed COVID-19 cases in Senegal. The generalized Waring regression model has an advantage over other models such as the negative binomial regression model because it considers factors that cannot be observed or measured, but that are known to affect the number of daily COVID-19 cases. Results from this study reveal that the generalized Waring regression model fits the data better than most of the usual count regression models, and could better explain some of the intrinsic characteristics of the disease dynamics.
新冠疫情的迅速蔓延在全球引发了重大的经济和社会混乱。特别是在塞内加尔以及整个西非地区，与世界其他地区相比，因感染导致的死亡人数极少。我们使用诸如广义华林回归模型等计数回归（统计）模型来预测塞内加尔每日确诊的新冠病例数。广义华林回归模型比其他模型（如负二项回归模型）具有优势，因为它考虑了那些无法观察或测量但已知会影响每日新冠病例数的因素。本研究结果表明，广义华林回归模型比大多数常用的计数回归模型更能拟合数据，并且能够更好地解释疾病动态的一些内在特征。
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