In this paper, we develop a robust economic model predictive controller for the containment of stochastic susceptible-exposed-infected-vigilant (SEIV) epidemic processes, which drives the process to extinction quickly, while minimizing the rate at which control resources are used. The study we present here is significant in that it addresses the problem of efficiently controlling general stochastic epidemic systems without relying on mean-field approximation, which is an important issue in the theory of stochastic epidemic processes. This enables us to provide rigorous convergence guarantees on the stochastic epidemic model itself, improving over the mean-field type convergence results of most prior work. There are two primary technical difficulties addressed in treating this problem: 1) constructing a means of tractably approximating the evolution of the process so that the designed approximation is robust to the modeling error introduced by the applied moment closure and 2) guaranteeing that the designed controller causes the closed-loop system to drive the SEIV process to extinction quickly. As an application, we use the developed framework for optimizing the use of quarantines in containing an SEIV epidemic outbreak.

中文翻译：

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连续时间流行过程的鲁棒经济模型预测控制


在本文中，我们开发了一种稳健的经济模型预测控制器，用于遏制随机的易感者-暴露-感染-警惕（SEIV）流行病过程，从而推动该过程迅速灭绝，同时最大限度地减少控制资源的使用率。我们在这里提出的研究具有重要意义，因为它解决了在不依赖平均场近似的情况下有效控制一般随机流行病系统的问题，这是随机流行病过程理论中的一个重要问题。这使我们能够为随机流行病模型本身提供严格的收敛保证，从而改进大多数先前工作的平均场类型收敛结果。处理这个问题需要解决两个主要的技术难题：1）构建一种易于处理的近似过程演化的方法，以便设计的近似对于所应用的力矩收敛引入的建模误差具有鲁棒性；2）保证设计的控制器导致闭环系统快速驱动SEIV过程走向灭绝。作为一个应用程序，我们使用开发的框架来优化隔离区的使用，以遏制 SEIV 流行病的爆发。