论文标题
通过随机最佳控制的流行病控制
Epidemic control via stochastic optimal control
论文作者
论文摘要
我们研究随机SIR模型的最佳控制问题。这种类型的模型用于数学流行病学，以捕获高度感染性疾病（例如COVID-19）的时间演变。我们的方法依赖于将汉密尔顿 - 雅各比 - 贝尔曼方程式重新阐述为随机的最低原则。这导致了向前向后的随机微分方程的系统，该系统可以通过蒙特卡洛模拟来适应数值解决方案。我们在各种情况下介绍了系统的许多数值解决方案。
We study the problem of optimal control of the stochastic SIR model. Models of this type are used in mathematical epidemiology to capture the time evolution of highly infectious diseases such as COVID-19. Our approach relies on reformulating the Hamilton-Jacobi-Bellman equation as a stochastic minimum principle. This results in a system of forward backward stochastic differential equations, which is amenable to numerical solution via Monte Carlo simulations. We present a number of numerical solutions of the system under a variety of scenarios.
