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Model predictive control for optimal social distancing in a type SIR-switched model

机译:SIR切换模型中最优社会偏移的模型预测控制

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Social distancing strategies have been adopted by governments to manage the COVID-19 pandemic, since the first outbreak began. However, further epidemic waves keep out the return of economic and social activities to their standard levels of intensity. Social distancing interventions based on control theory are needed to consider a formal dynamic characterization of the implemented SIR-type model to avoid unrealistic objectives and prevent further outbreaks. The objective of this work is twofold: to fully understand some dynamical aspects of SIR-type models under control actions (associated with second waves) and, based on it, to propose a switching non-linear model predictive control that optimize the non-pharmaceutical measures strategy. Opposite to other strategies, the objective here is not just to minimize the number of infected individuals at any time, but to minimize the final size of the epidemic while minimizing the time of social restrictions and avoiding the infected prevalence peak to overpass a maximum established by the healthcare system capacity. Simulations illustrate the benefits of the aforementioned proposal.
机译:由于第一次爆发开始,各国政府采取了社会疏远策略来管理Covid-19大流行。然而,进一步的疫情浪潮远离经济和社会活动的回报,以其标准的强度水平。需要基于控制理论的社会偏移干预措施来考虑实施的先生型模型的正式动态特征,以避免不切实际的目标并防止进一步的爆发。这项工作的目的是双重的:为了在控制动作(与第二波相关联)下完全了解SIR型模型的一些动态方面,并且基于它,提出优化非药物的切换非线性模型预测控制措施策略。与其他策略相反,这里的目的不仅可以在任何时候最小化受感染的个体的数量,而是最小化疫情的最终规模,同时最大限度地减少社会限制的时间,避免感染的流行峰达到立交机医疗保健系统能力。模拟说明了上述提案的好处。

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