Dipartimento di Scienze Fisiche e Tecnologie per la Materia, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro 7, 00185, Rome, Italy.
Institute for Microelectronics and Microsystems (IMM), Consiglio Nazionale delle Ricerche (CNR), VIII Strada 5, I-95121, Catania, Italy.
Sci Rep. 2021 Dec 1;11(1):23225. doi: 10.1038/s41598-021-02546-y.
We have further extended our compartmental model describing the spread of the infection in Italy. As in our previous work, the model assumes that the time evolution of the observable quantities (number of people still positive to the infection, hospitalized and fatalities cases, healed people, and total number of people that has contracted the infection) depends on average parameters, namely people diffusion coefficient, infection cross-section, and population density. The model provides information on the tight relationship between the variation of the reported infection cases and a well-defined observable physical quantity: the average number of people that lie within the daily displacement area of any single person. With respect to our previous paper, we have extended the analyses to several regions in Italy, characterized by different levels of restrictions and we have correlated them to the diffusion coefficient. Furthermore, the model now includes self-consistent evaluation of the reproduction index, effect of immunization due to vaccination, and potential impact of virus variants on the dynamical evolution of the outbreak. The model fits the epidemic data in Italy, and allows us to strictly relate the time evolution of the number of hospitalized cases and fatalities to the change of people mobility, vaccination rate, and appearance of an initial concentration of people positives for new variants of the virus.
我们进一步扩展了我们的 compartmental 模型，以描述意大利的感染传播情况。与我们之前的工作一样，该模型假设可观察数量（仍对感染呈阳性、住院和死亡人数、康复人数以及感染总人数）的时间演变取决于平均参数，即人群扩散系数、感染截面和人口密度。该模型提供了有关报告的感染病例变化与明确定义的可观察物理量（任何一个人日常活动区域内的平均人数）之间紧密关系的信息。与我们之前的论文相比，我们将分析扩展到意大利的几个地区，这些地区的限制程度不同，并将其与扩散系数相关联。此外，该模型现在包括对繁殖指数、疫苗接种引起的免疫以及病毒变体对疫情动态演变的潜在影响的自洽评估。该模型拟合了意大利的疫情数据，使我们能够严格将住院病例和死亡人数的时间演变与人群流动性的变化、疫苗接种率以及新病毒变体阳性人数的初始浓度联系起来。
