标题： 一种数据驱动的模型预测控制器来管理流行病：马尔代夫的SARS-CoV-2案例 显示英文标题

标题： A Data-Driven Model Predictive Controller to manage epidemics: The case of SARS-CoV-2 in Mauritius

摘要： 这项工作研究了在流行病期间实施系统化社交隔离政策的好处。 我们开发了一种基于SIHRD模型的混合整数数据驱动模型预测控制（MPC）方案，该模型是根据现有数据确定的。 毛里求斯境内SARS-CoV-2病毒（也称为COVID-19）的传播情况被用作参考点，数据获取时间为2021年12月至2022年5月。 隔离方案设计中，控制决策变量取有限值集合，对应于所需的隔离水平。 控制输入进一步限制为仅在一定时间后才能在不同水平之间切换。 仿真结果验证了我们的设计，表明住院需求始终在卫生中心的容量范围内，通过短时间内提高隔离水平，显著减少了死亡人数，且对社会和经济影响微乎其微。 我们还表明，引入额外的隔离级别可以实现更平滑的控制方法，大幅减轻住院负担。 显示英文摘要

摘要： This work investigates the benefits of implementing a systematic approach to social isolation policies during epidemics. We develop a mixed integer data-driven model predictive control (MPC) scheme based on an SIHRD model which is identified from available data. The case of the spread of the SARS-CoV-2 virus (also known as COVID-19) in Mauritius is used as a reference point with data obtained during the period December 2021 to May 2022. The isolation scheme is designed with the control decision variable taking a finite set of values corresponding to the desired level of isolation. The control input is further restricted to shifting between levels only after a minimum amount of time. The simulation results validate our design, showing that the need for hospitalisation remains within the capacity of the health centres, with the number of deaths considerably reduced by raising the level of isolation for short periods of time with negligible social and economic impact. We also show that the introduction of additional isolation levels results in a smoother containment approach with a considerably reduced hospitalisation burden.