Demongeot J, Griette Q, Magal P
Department of Medicine, Université Grenoble Alpes, AGEIS EA7407, 38700 La Tronche, France.
Department of Medicine, University of Bordeaux, IMB, UMR, 5251, 33400 Talence, France.
R Soc Open Sci. 2020 Dec 2;7(12):201878. doi: 10.1098/rsos.201878. eCollection 2020 Dec.
The article is devoted to the parameters identification in the SI model. We consider several methods, starting with an exponential fit to the early cumulative data of SARS-CoV2 in mainland China. The present methodology provides a way to compute the parameters at the early stage of the epidemic. Next, we establish an identifiability result. Then we use the Bernoulli-Verhulst model as a phenomenological model to fit the data and derive some results on the parameters identification. The last part of the paper is devoted to some numerical algorithms to fit a daily piecewise constant rate of transmission.
本文致力于SI模型中的参数识别。我们考虑了几种方法,首先是对中国大陆SARS-CoV-2的早期累积数据进行指数拟合。当前的方法提供了一种在疫情早期计算参数的方法。接下来,我们建立了一个可识别性结果。然后我们使用伯努利-韦尔胡尔斯模型作为现象学模型来拟合数据,并得出一些关于参数识别的结果。本文的最后一部分致力于一些数值算法,以拟合每日分段恒定的传播率。