Fazio Martina, Pluchino Alessandro, Inturri Giuseppe, Le Pira Michela, Giuffrida Nadia, Ignaccolo Matteo
Department of Physics and Astronomy, University of Catania, Catania, Italy.
INFN Section of Catania, Catania, Italy.
J Transp Health. 2022 Jun;25:101373. doi: 10.1016/j.jth.2022.101373. Epub 2022 Apr 26.
The recent health emergency caused by the COVID-19 pandemic forced people to change their mobility habits, with the reduction of non-essential travels and the promotion online activities. During the first phase of the emergency in 2020, governments considered several mobility restrictions to avoid the pandemic diffusion. However, it is difficult to quantify the actual effects of these restrictions on the virus spreading, especially due to the biased data available. Notwithstanding the big role of data analysis to understand the pandemic phenomenon, it is also important to have more general models capable of predicting the impact of different policy scenarios, including territorial parameters, independently from the available infection data. In this respect, this paper proposes an agent-based model to simulate the impact of mobility restrictions on the spreading of the COVID-19 at a large scale level, by considering different factors that can be attributed to the diffusion and lethality of the virus and population mobility patterns.
The first step of the method includes a zonation of the study area, according to administrative boundaries. A risk index is calculated for each zone considering indicators which can influence the virus spreading and people lethality: mean winter temperature, housing concentration, healthcare density, population mobility, air pollution and the percentage of population over 60 years old. The agent-based model associates the risk index to the agents and determines their "status" ("susceptible", "infected", "isolated", "recovered" or "dead") by combining the risk index with the mean infection duration, using a SIR-based approach (i.e. susceptible-infective-removed).
The study is applied to Italy. Several scenarios based on different mobility restrictions have been simulated, including the one based on the official data (). The main results show that characterizing zones with a risk index allows to adopt local policies with almost the same effectiveness as in the case of restrictions extended to the full study area; scenario simulations return an increase in terms of infected (+20%) and deaths (+25%) with respect to the . These results underline the importance of finding a trade-off between socio-economic benefits and health impact.
The reproducibility of the proposed methodology and its scalability allow to apply it to different contexts and at a different administrative level, from the urban scale to a national one. Moreover, the model is able to provide a decision-support tool for the design of strategic plans to contrast pandemics based on respiratory diseases.
由新冠疫情引发的近期健康紧急状况迫使人们改变出行习惯，减少非必要出行并推广线上活动。在2020年紧急状况的第一阶段，政府考虑了多项出行限制措施以避免疫情扩散。然而，难以量化这些限制措施对病毒传播的实际影响，尤其是由于现有数据存在偏差。尽管数据分析对于理解疫情现象起着重要作用，但拥有更通用的模型来预测不同政策情景（包括地域参数）的影响也很重要，且不依赖于现有的感染数据。在这方面，本文提出一种基于主体的模型，通过考虑可归因于病毒传播和致死率以及人口流动模式的不同因素，来大规模模拟出行限制对新冠病毒传播的影响。
该方法的第一步包括根据行政边界对研究区域进行分区。针对每个区域计算一个风险指数，考虑能够影响病毒传播和人员致死率的指标：冬季平均温度、住房集中度、医疗保健密度、人口流动性、空气污染以及60岁以上人口的百分比。基于主体的模型将风险指数与主体相关联，并通过使用基于SIR的方法（即易感-感染-康复），将风险指数与平均感染持续时间相结合来确定它们的“状态”（“易感”、“感染”、“隔离”、“康复”或“死亡”）。
该研究应用于意大利。模拟了基于不同出行限制的几种情景，包括基于官方数据的情景。主要结果表明，用风险指数对区域进行特征描述能够采用与将限制扩展到整个研究区域时几乎相同有效性的地方政策；情景模拟显示，与[具体情景]相比，感染人数增加了20%，死亡人数增加了25%。这些结果强调了在社会经济效益和健康影响之间找到权衡点的重要性。
所提出方法的可重复性及其可扩展性使其能够应用于不同背景和不同行政级别，从城市规模到国家规模。此外，该模型能够为制定基于呼吸道疾病的大流行防控战略计划提供决策支持工具。
