Lanteri D, Carco D, Castorina P, Ceccarelli M, Cacopardo B
INFN, Sezione di Catania, I-95123, Catania, Italy.
Dipartimento di Fisica e Astronomia, Università di Catania, Italy.
Infect Dis Model. 2021 Apr 7;6:632-642. doi: 10.1016/j.idm.2021.02.003. eCollection 2021.
In all countries the political decisions aim to achieve an almost stable configuration with a small number of new infected individuals per day due to Covid-19. When such a condition is reached, the containment effort is usually reduced in favor of a gradual reopening of the social life and of the various economical sectors. However, in this new phase, the infection spread restarts and, moreover, possible mutations of the virus give rise to a large specific growth rate of the infected people. Therefore, a quantitative analysis of the regrowth pattern is very useful. We discuss a macroscopic approach which, on the basis of the collected data in the first lockdown, after few days from the beginning of the new phase, outlines different scenarios of the Covid-19 diffusion for longer time. The purpose of this paper is a demonstration-of-concept: one takes simple growth models, considers the available data and shows how the future trend of the spread can be obtained. The method applies a time dependent carrying capacity, analogously to many macroscopic growth laws in biology, economics and population dynamics. The illustrative cases of France, Italy and United Kingdom are analyzed.
在所有国家，政治决策旨在实现一种几乎稳定的状态，即因新冠疫情导致每天新增感染人数较少。当达到这种状态时，防控措施通常会放松，转而支持社会生活和各个经济部门逐步重新开放。然而，在这个新阶段，感染传播会再次开始，而且病毒可能发生的变异会导致感染人群出现较大的特定增长率。因此，对疫情反弹模式进行定量分析非常有用。我们讨论一种宏观方法，该方法基于首次封锁期间收集的数据，在新阶段开始几天后，勾勒出新冠疫情在更长时间内传播的不同情景。本文的目的是进行概念验证：采用简单的增长模型，考虑现有数据，并展示如何获得传播的未来趋势。该方法应用了随时间变化的承载能力，类似于生物学、经济学和种群动态学中的许多宏观增长规律。文中分析了法国、意大利和英国的示例情况。
