Gning Lucien Diégane, Diop Aba, Diagne Mamadou Lamine, Tchuenche Jean
LERSTAD, UFR Sciences appliquées et Technologie, Université Gaston BERGER, Saint-Louis, BP 234 Senegal.
Equipe de Recherche en Statistique et Modèles Aléatoires(ERESMA), Universitê Alioune Diop, Bambey, Senegal.
Model Earth Syst Environ. 2022;8(4):5713-5721. doi: 10.1007/s40808-022-01483-7. Epub 2022 Aug 4.
COVID-19 is a global health burden. We propose to model the dynamics of COVID-19 in Senegal and in China by count time series following generalized linear models. One of the main properties of these models is that they can detect potentials trends on the contagion dynamics within a given country. In particular, we fit the daily new infections in both countries by a Poisson autoregressive model and a negative binomial autoregressive model. In the case of Senegal, we include covariates in the models contrary to the Chinese case where the fitted models are without covariates. The short-term predictions of the daily new cases in both countries from both models are graphically illustrated. The results show that the predictions given by the negative binomial autoregressive model are more accurate than those given by the Poisson autoregressive model.
新型冠状病毒肺炎（COVID-19）是一项全球卫生负担。我们建议通过遵循广义线性模型的计数时间序列，对塞内加尔和中国的COVID-19动态进行建模。这些模型的主要特性之一是，它们能够检测给定国家内传染病动态的潜在趋势。特别是，我们用泊松自回归模型和负二项自回归模型对两国的每日新增感染病例进行拟合。在塞内加尔的案例中，我们在模型中纳入了协变量，而中国的拟合模型则没有协变量。两个模型对两国每日新增病例的短期预测均以图表形式展示。结果表明，负二项自回归模型给出的预测比泊松自回归模型给出的预测更准确。
