Atanasov D, Stoimenova Vessela, Yanev Nikolay M
Department of Informatics, New Bulgarian University, Sofia, Bulgaria.
Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria.
J Appl Stat. 2021 Nov 29;50(11-12):2330-2342. doi: 10.1080/02664763.2021.2006154. eCollection 2023.
In this paper, a statistical model for COVID-19 infection dynamics is described, using only the observed daily statistics of infected individuals. For this purpose, two special classes of branching processes without or with an immigration component are considered. These models are intended 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. 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. The model is applied for different regions in the world and the corresponding parameters of the infection dynamics are estimated.
本文描述了一种仅使用观察到的每日感染个体统计数据的COVID-19感染动态统计模型。为此，考虑了两类特殊的分支过程，一类无移民成分，另一类有移民成分。这些模型旨在估计感染的主要参数，并预测未观察到的感染个体群体的平均值。与其他更复杂的模型相比，这是一个显著优势，在其他模型中，官方报告的数据不足以估计模型参数。该模型应用于世界不同地区，并估计了感染动态相应参数。
