37 Quai du Docteur Gailleton, Lyon, France.
Department of Economics and Management, University of Pisa, Italy.
Math Biosci. 2021 Oct;340:108671. doi: 10.1016/j.mbs.2021.108671. Epub 2021 Jul 21.
To mitigate the harmful effects of the COVID-19 pandemic, world countries have resorted - though with different timing and intensities - to a range of interventions. These interventions and their relaxation have shaped the epidemic into a multi-phase form, namely an early invasion phase often followed by a lockdown phase, whose unlocking triggered a second epidemic wave, and so on. In this article, we provide a kinematic description of an epidemic whose time course is subdivided by mitigation interventions into a sequence of phases, on the assumption that interventions are effective enough to prevent the susceptible proportion to largely depart from 100% (or from any other relevant level). By applying this hypothesis to a general SIR epidemic model with age-since-infection and piece-wise constant contact and recovery rates, we supply a unified treatment of this multi-phase epidemic showing how the different phases unfold over time. Subsequently, by exploiting a wide class of infectiousness and recovery kernels allowing reducibility (either to ordinary or delayed differential equations), we investigate in depth a low-dimensional case allowing a non-trivial full analytical treatment also of the transient dynamics connecting the different phases of the epidemic. Finally, we illustrate our theoretical results by a fit to the overall Italian COVID-19 epidemic since March 2020 till February 2021 i.e., before the mass vaccination campaign. This show the abilities of the proposed model in effectively describing the entire course of an observed multi-phasic epidemic with a minimal set of data and parameters, and in providing useful insight on a number of aspects including e.g., the inertial phenomena surrounding the switch between different phases.
为减轻 COVID-19 大流行的有害影响,世界各国采取了各种干预措施,不过时机和力度有所不同。这些干预措施及其放松,使疫情呈现多阶段形式,即早期入侵阶段,通常紧随其后的是封锁阶段,封锁的解除引发了第二波疫情,如此等等。在本文中,我们假设干预措施足以有效防止易感染人群的比例大幅偏离 100%(或任何其他相关水平),为一个按缓解干预措施划分为一系列阶段的疫情提供了运动学描述。通过将这一假设应用于具有年龄感染和分段常数接触和恢复率的一般 SIR 传染病模型,我们提供了一个多阶段传染病的统一处理方法,展示了不同阶段如何随时间展开。随后,通过利用允许简化(无论是简化为常微分方程还是延迟微分方程)的广泛传染性和恢复核类,我们深入研究了一个低维案例,允许对连接传染病不同阶段的瞬态动力学进行非平凡的全解析处理。最后,我们通过对 2020 年 3 月至 2021 年 2 月(即大规模疫苗接种运动之前)意大利 COVID-19 大流行的整体情况进行拟合,说明了我们理论结果的有效性。这表明,该模型能够有效地用最小的数据和参数集描述观察到的多阶段传染病的整个过程,并提供了一些有用的见解,包括不同阶段之间转换时的惯性现象等方面。