Salvadore Francesco, Fiscon Giulia, Paci Paola
CINECA, HPC Department, Rome Office, Italy.
Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy; Department of Computer, Control and Management Engineering "A. Ruberti" (DIAG), Sapienza University of Rome, Rome, Italy.
Comput Biol Med. 2021 Dec;139:105013. doi: 10.1016/j.compbiomed.2021.105013. Epub 2021 Nov 2.
The COVID-19 pandemic has overwhelmed the life and security of most of the world countries, and especially of the Western countries, without similar experiences in the recent past. In a first phase, the response of health systems and governments was disorganized, but then incisive, also driven by the fear of a new and dramatic phenomenon. In the second phase, several governments, including Italy, accepted the doctrine of "coexistence with the virus" by putting into practice a series of containment measures aimed at limiting the dramatic sanitary consequences while not jeopardizing the economic and social stability of the country. Here, we present a new mathematical approach to modeling the COVID-19 dynamics that accounts for typical evolution parameters (i.e., virus variants, vaccinations, containment measurements). Reproducing the COVID-19 epidemic spread is an extremely challenging task due to the low reliability of the available data, the lack of recurrent patterns, and the considerable amount and variability of the involved parameters. However, the adoption of fairly uniform criteria among the Italian regions enabled to test and optimize the model in various conditions leading to robust and interesting results. Although the regional variability is quite large and difficult to predict, we have retrospectively obtained reliable indications on which measures were the most appropriate to limit the transmissibility coefficients within detectable ranges for all the regions. To complicate matters further, the rapid spread of the English variant has upset contexts where the propagation of contagion was close to equilibrium conditions, decreeing success or failure of a certain measure. Finally, we assessed the effectiveness of the zone assignment criteria, highlighting how the reactivity of the measures plays a fundamental role in limiting the spread of the infection and thus the total number of deaths, the most important factor in assessing the success of epidemic management.
新冠疫情使世界上大多数国家，尤其是西方国家的生活和安全不堪重负，而这些国家近期没有类似经历。在第一阶段，卫生系统和政府的应对杂乱无章，但随后变得果断，这也是出于对一种新的严重现象的恐惧。在第二阶段，包括意大利在内的几个国家的政府接受了“与病毒共存”的理念，实施了一系列遏制措施，旨在限制严重的卫生后果，同时又不损害国家的经济和社会稳定。在此，我们提出一种新的数学方法来模拟新冠疫情动态，该方法考虑了典型的演变参数（即病毒变种、疫苗接种、遏制措施）。由于现有数据可靠性低、缺乏重复模式以及相关参数数量众多且变化大，再现新冠疫情传播是一项极具挑战性的任务。然而，意大利各地区采用相当统一的标准，得以在各种条件下测试和优化该模型，从而得出稳健且有趣的结果。尽管地区差异相当大且难以预测，但我们通过回顾性研究获得了可靠的迹象，表明哪些措施最适合将所有地区的传播系数限制在可检测范围内。更复杂的是，英国变种的迅速传播扰乱了疫情传播接近平衡状态的情况，决定了某项措施的成败。最后，我们评估了区域划分标准的有效性，强调了措施的反应性在限制感染传播以及因此限制死亡总数方面所起的关键作用，而死亡总数是评估疫情管理成功与否的最重要因素。
