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On the Use of MPC Techniques to Decide Intervention Policies against COVID-19

机译：关于MPC技术来决定Covid-19的干预政策

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This paper aims at demonstrating how and that model predictive control (MPC) strategies can be used to determine optimal intervention policies against the COVID-19 pandemic. Especially for the time after a first wave of infection and before a vaccine can be safely distributed to a sufficient extent, the intervention experience from the first outbreak can be utilized to guide the policy decision in this period. The MPC problem in this paper takes the pandemic in different regions of a country and its neighboring countries into account, while policies such as wearing masks or social distancing are selected as inputs to be optimized. This optimized policy balances the risk of a second outbreak and socio-economic costs, while considering that the measure should not be too severe to be rejected by the population. Effectiveness of this policy compared to standard intervention policies is compared through numerical simulations.
机译：本文旨在展示如何以及该模型预测控制（MPC）策略来确定对Covid-19大流行的最佳干预政策。 特别是对于第一波感染之后的时间和在疫苗之前可以安全地分布到足够的程度，可以利用第一次爆发的干预经验来指导在此期间的政策决定。 本文的MPC问题涉及一个国家和其邻国的不同地区的大流行，而佩戴面具或社会偏差等政策被选为要优化的投入。 这种优化的政策余额余额爆发和社会经济成本的风险，同时考虑到该措施不应该过于严重，无法被人口拒绝。 通过数值模拟比较了与标准干预策略相比该政策的有效性。

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