Tchorbadjieff A, Tomov L P, Velev V, Dezhov G, Manev V, Mayster P
Institute of Mathematics and Informatics Bulgarian Academy of Sciences, Sofia, Bulgaria.
Department of Informatics, New Bulgarian University, Sofia, Bulgaria.
J Appl Stat. 2023 Feb 13;50(11-12):2343-2359. doi: 10.1080/02664763.2023.2177625. eCollection 2023.
The COVID-19 pandemic has had a very serious impact on societies and caused large-scale economic changes and death toll worldwide. The first cases were detected in China, but soon the virus spread quickly worldwide and the intensity of newly reported infections grew high during this initial period almost everywhere. Later, despite all imposed measures, the intensity shifted abruptly multiple times during the two-year period between 2020 and 2022 causing waves of too high infection rates in almost every part of the world. To target this problem, we assume the data heterogeneity as multiple consecutive regime changes. The research study includes the development of a model based on automatic regime change detection and their combination with the linear birth-death process for long-run data fits. The results are empirically verified on data for 38 countries and US states for the period from February 2020 to April 2022. Finally, the initial phase (conditions) properties of infection development are studied.
新冠疫情对社会产生了极其严重的影响,在全球范围内引发了大规模的经济变化和人员死亡。首例病例在中国被发现,但病毒很快在全球迅速传播,在这一初始阶段,几乎所有地方新报告感染病例的强度都很高。后来,尽管采取了所有强制措施,但在2020年至2022年的两年期间,感染强度多次突然转变,几乎在世界每个地区都引发了感染率过高的浪潮。为了解决这个问题,我们将数据异质性假设为多个连续的状态变化。该研究包括开发一个基于自动状态变化检测的模型,并将其与线性生死过程相结合以进行长期数据拟合。研究结果在2020年2月至2022年4月期间38个国家和美国各州的数据上进行了实证验证。最后,研究了感染发展的初始阶段(条件)特性。