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Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario
Annual Reviews in Control ( IF 9.4 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.arcontrol.2020.09.005
Raffaele Carli 1 , Graziana Cavone 1 , Nicola Epicoco 2 , Paolo Scarabaggio 1 , Mariagrazia Dotoli 1
Affiliation  

The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity.

Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions.

The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion.



中文翻译:

模型预测控制以缓解多区域场景中的 COVID-19 爆发

由于世界各国政府为应对疫情蔓延而采取的限制性措施,COVID-19 疫情正在深刻影响全球社会和经济框架。在意大利等已达疫情高峰的多区域地区,如何在区域范围内找到有针对性、协调一致的最佳退出和重启策略,以有效应对可能出现的疫情进一步爆发,同时有效恢复经济复苏,至关重要。活动强度达到标准水平。

与相关文献不同,在相关文献中,对大流行病传染的建模和控制通常是在国家范围内进行的,本文提出了一种最优控制方法,支持政府制定多国封锁后缓解阶段采取的最有效策略。区域场景。该方法基于非线性模型预测控制方案和改进的基于易感感染者恢复(SIR)的流行病学模型的联合使用,旨在最大限度地减少所谓的非药物干预措施(即,缓解策略),同时确保区域医疗系统网络的容量不被侵犯。此外,所提出的方法支持政策制定者通过综合和结构化模型对不同地区做出有针对性的干预决策,从而既尊重特定区域卫生系统的特征,又通过避免地区不协调的行动来提高整个系统的绩效。

该方法在与意大利地区网络相关的 COVID-19 疫情数据上进行了测试,表明其在正确支持管理 COVID-19 传播的有效区域策略的定义方面的有效性。

更新日期:2020-10-01
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