Di Giamberardino Paolo, Iacoviello Daniela, Papa Federico, Sinisgalli Carmela
Department of Computer, Control and Management Engineering A. Ruberti, Sapienza University of Rome, Rome, Italy.
Institute for Systems Analysis and Computer Science "A. Ruberti" - CNR, Rome, Italy.
Nonlinear Dyn. 2021;106(2):1239-1266. doi: 10.1007/s11071-021-06840-2. Epub 2021 Sep 3.
An epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical control actions implemented to limit the infection spread since its outbreak in Italy. The single submodels provide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffusion. The multi-group structure is specifically designed to investigate the effects of the inter-regional mobility restored at the end of the first strong lockdown in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability properties which provide significant insights on the efficacy of the implemented control measurements. In order to highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March-October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can represent a useful tool for predicting the effects of the combination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial distribution of the infection throughout the country and, combined with the restart of the production, trade, and education activities, determined a time advance of the contagion increase since September 2020.
构建了一个由相互连接的类SEIR结构组成的疫情多群体模型，并将其用于数据拟合，以深入了解新冠疫情动态以及自意大利爆发以来为限制感染传播而实施的非药物控制措施的作用。单个子模型通过一个扩展的SEIR模型，包括无症状感染者类别，对每个亚群体中的新冠疫情演变提供了相当准确的描述，无症状感染者类别被认为是疾病传播的一个决定因素。多群体结构专门设计用于研究意大利首次严格封锁结束时（2020年6月3日）恢复的区域间流动的影响。在其时间不变版本中，该模型显示具有一些分析稳定性特性，这为所实施的控制措施的有效性提供了重要见解。为了突出人员流动对意大利第一波和第二波疫情爆发之间疾病演变的影响，该模型被应用于拟合2020年3月至10月期间三个地理大区的实际流行病学数据，包括夏季假期的大规模出行。模拟结果与数据吻合良好，因此该模型可成为预测遏制措施组合在引发未来疫情情景中的效果的有用工具。特别是，模拟表明，尽管仅无限制的流动似乎不足以引发第二波疫情，但人员流动对于使感染在全国的空间分布均匀化至关重要，并且与生产、贸易和教育活动的重启相结合，决定了自2020年9月以来感染增加的时间提前。
