社会异质性条件下疫情传播的最优控制

G. Dimarco, G. Toscani, M. Zanella
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引用次数: 19

摘要

在大多数国家,通过非药物干预措施遏制了COVID-19的传播。特别是,在这方面最有效的措施是企业和学校的居家和关闭战略。然而,全国范围内的封锁远非最佳状态,带来了严重的经济后果。因此,现在人们对设计更有效的限制有着浓厚的兴趣。在这项工作中,从考虑到个体社会接触所描述的异质性的最近的动力学型模型开始,我们分析了在系统中引入最优控制策略的效果,以有选择地限制接触的平均数量,从而减少感染病例的数量。由于数据驱动的方法,我们表明这个新的数学模型允许我们评估社会限制的影响。最后,利用这里介绍的模型,从现有数据出发,我们证明了所提出的选择性措施抑制流行病趋势的有效性。本文是“社会和经济的动态交换模型”主题的一部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal control of epidemic spreading in the presence of social heterogeneity
The spread of COVID-19 has been thwarted in most countries through non-pharmaceutical interventions. In particular, the most effective measures in this direction have been the stay-at-home and closure strategies of businesses and schools. However, population-wide lockdowns are far from being optimal, carrying heavy economic consequences. Therefore, there is nowadays a strong interest in designing more efficient restrictions. In this work, starting from a recent kinetic-type model which takes into account the heterogeneity described by the social contact of individuals, we analyse the effects of introducing an optimal control strategy into the system, to limit selectively the mean number of contacts and reduce consequently the number of infected cases. Thanks to a data-driven approach, we show that this new mathematical model permits us to assess the effects of the social limitations. Finally, using the model introduced here and starting from the available data, we show the effectiveness of the proposed selective measures to dampen the epidemic trends. This article is part of the theme issue ‘Kinetic exchange models of societies and economies’.
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