Department of Mathematics, Vijaygarh Jyotish Ray College, Kolkata - 700032, India.
Institut Camille Jordan, UMR 5208 CNRS, University Lyon 1, 69622 Villeurbanne, France.
Math Biosci Eng. 2020 Nov 2;17(6):7562-7604. doi: 10.3934/mbe.2020386.
An extended SEIQR type model is considered in order to model the COVID-19 epidemic. It contains the classes of susceptible individuals, exposed, infected symptomatic and asymptomatic, quarantined, hospitalized and recovered. The basic reproduction number and the final size of epidemic are determined. The model is used to fit available data for some European countries. A more detailed model with two different subclasses of susceptible individuals is introduced in order to study the influence of social interaction on the disease progression. The coefficient of social interaction K characterizes the level of social contacts in comparison with complete lockdown (K=0) and the absence of lockdown (K=1). The fitting of data shows that the actual level of this coefficient in some European countries is about 0.1, characterizing a slow disease progression. A slight increase of this value in the autumn can lead to a strong epidemic burst.
为了对 COVID-19 疫情进行建模，我们考虑了一个扩展的 SEIQR 型模型。该模型包含易感人群、暴露、感染有症状和无症状、隔离、住院和康复等类别。确定了基本再生数和疫情的最终规模。该模型用于拟合一些欧洲国家的可用数据。引入了一个更详细的模型，其中包含两个不同的易感人群子类，以研究社会互动对疾病进展的影响。社会互动系数 K 用于描述与完全封锁（K=0）和无封锁（K=1）相比的社会接触水平。数据拟合表明，在一些欧洲国家，该系数的实际水平约为 0.1，这表明疾病进展缓慢。如果该系数在秋季略有增加，可能会导致疫情爆发。
