El Aferni Ahmed, Guettari Moez, Hamdouni Abdelkader
Preparatory Institute of Engineering of Tunis. Materials and Fluids Laboratory, University of Tunis, Tunis, Tunisia.
The Higher Institute of Sciences and Technologies of the Environnent Borj Cedria, University of Carthage, Carthage, Tunisia.
Eur Phys J Plus. 2023;138(5):393. doi: 10.1140/epjp/s13360-023-04014-0. Epub 2023 May 8.
The aim of the current study is to investigate the spread of the COVID-19 pandemic as a multiphase percolation process. Mathematical equations have been developed to describe the time dependence of the number of cumulative infected individuals, , and the velocity of the pandemic, , as well as to calculate epidemiological characteristics. The study focuses on the use of sigmoidal growth models to investigate multiwave COVID-19. Hill, logistic dose response and sigmoid Boltzmann models fitted successfully a pandemic wave. The sigmoid Boltzmann model and the dose response model were found to be effective in fitting the cumulative number of COVID-19 cases over time 2 waves spread ( = 2). However, for multiwave spread ( > 2), the dose response model was found to be more suitable due to its ability to overcome convergence issues. The spread of N successive waves has also been described as multiphase percolation with a period of pandemic relaxation between two successive waves.
本研究的目的是将2019年冠状病毒病（COVID-19）大流行的传播作为一个多阶段渗流过程进行研究。已开发出数学方程来描述累积感染个体数量随时间的变化以及大流行的速度，并计算流行病学特征。该研究着重于使用S形增长模型来研究COVID-19多波疫情。希尔模型、逻辑剂量反应模型和S形玻尔兹曼模型成功拟合了一波疫情。发现S形玻尔兹曼模型和剂量反应模型在拟合两波传播（m = 2）的COVID-19病例累积数量随时间的变化方面是有效的。然而，对于多波传播（m > 2），发现剂量反应模型由于其克服收敛问题的能力而更合适。N次连续波的传播也被描述为多阶段渗流，在两个连续波之间有一段疫情缓和期。
