论文标题
分支随机过程作为COVID-19的流行发展模型
Branching stochastic processes as models of Covid-19 epidemic development
论文作者
论文摘要
本文的目的是描述两种Covid-19感染动力学的模型。为此，考虑了具有两种类型个体的特殊类分支过程。这些模型旨在仅使用观察到的每日统计数据来估计感染的主要参数，并预测被感染个体的非观察群体的平均值。在过程接纳移民组成部分的情况下，也考虑了类似的问题。与其他更复杂的模型相比，这是一个严重的优势，在该模型中，正式报告的数据不足以估算模型参数。通过这种方式，也考虑了所有国家的共同发展的特定开发，就像在专门创建的网站http://ir-statistics.net/covid-19中给出的那样，每天都会更新获得的结果。
The aim of the paper is to describe two models of Covid-19 infection dynamics. For this purpose a special class of branching processes with two types of individuals is considered. These models are intended to use only the observed daily statistics to estimate the main parameter of the infection and to give a prediction of the mean value of the non-observed population of the infected individuals. Similar problems are considered also in the case when the processes admit an immigration component. This is a serious advantage in comparison with other more complicated models where the officially reported data are not sufficient for estimation of the model parameters. In this way the specific development of the Covid-19 epidemics is considered also for all countries as it is given in the specially created site http://ir-statistics.net/covid-19 where the obtained results are updated daily.
