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A data-driven epidemic model with social structure for understanding the COVID-19 infection on a heavily affected Italian province
Mathematical Models and Methods in Applied Sciences ( IF 3.6 ) Pub Date : 2021-09-29 , DOI: 10.1142/s021820252150055x
Mattia Zanella 1 , Chiara Bardelli 2 , Giacomo Dimarco 3 , Silvia Deandrea 4 , Pietro Perotti 4 , Mara Azzi 4 , Silvia Figini 2 , Giuseppe Toscani 5
Affiliation  

In this work, using a detailed dataset furnished by National Health Authorities concerning the Province of Pavia (Lombardy, Italy), we propose to determine the essential features of the ongoing COVID-19 pandemic in terms of contact dynamics. Our contribution is devoted to provide a possible planning of the needs of medical infrastructures in the Pavia Province and to suggest different scenarios about the vaccination campaign which possibly help in reducing the fatalities and/or reducing the number of infected in the population. The proposed research combines a new mathematical description of the spread of an infectious diseases which takes into account both age and average daily social contacts with a detailed analysis of the dataset of all traced infected individuals in the Province of Pavia. These information are used to develop a data-driven model in which calibration and feeding of the model are extensively used. The epidemiological evolution is obtained by relying on an approach based on statistical mechanics. This leads to study the evolution over time of a system of probability distributions characterizing the age and social contacts of the population. One of the main outcomes shows that, as expected, the spread of the disease is closely related to the mean number of contacts of individuals. The model permits to forecast thanks to an uncertainty quantification approach and in the short time horizon, the average number and the confidence bands of expected hospitalized classified by age and to test different options for an effective vaccination campaign with age-decreasing priority.

中文翻译:

一种具有社会结构的数据驱动流行病模型,用于了解意大利受灾严重的省份的 COVID-19 感染情况

在这项工作中,我们使用国家卫生当局提供的有关帕维亚省(意大利伦巴第大区)的详细数据集,建议确定当前 COVID-19 大流行在接触动态方面的基本特征。我们的贡献致力于为帕维亚省医疗基础设施的需求提供可能的规划,并就疫苗接种运动提出不同的方案,这可能有助于减少死亡人数和/或减少人口中的感染人数。拟议的研究结合了对传染病传播的新数学描述,该描述同时考虑了年龄和平均每日社会接触,并详细分析了帕维亚省所有追踪到的感染者的数据集。这些信息用于开发数据驱动的模型,其中广泛使用模型的校准和馈送。流行病学演变是依靠基于统计力学的方法获得的。这导致研究表征人口年龄和社会接触的概率分布系统随时间的演变。主要结果之一表明,正如预期的那样,疾病的传播与个人的平均接触人数密切相关。由于采用不确定性量化方法,该模型允许在短时间内预测按年龄分类的预期住院患者的平均数量和置信带,并测试不同的选项以进行有效的疫苗接种活动,并以降低年龄为优先级。
更新日期:2021-09-29
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