带约束公共卫生干预的离散随机最优化
Discrete Stochastic Optimization for Public Health Interventions with Constraints
摘要：
存在许多公共卫生威胁，促使需要找到最佳干预战略。考虑到威胁的随机性（例如，大流行性流感的传播、药物过量的发生以及与酒精相关的威胁的流行），确定性优化方法可能是不合适的。在本文中，我们实现了一种随机最优化方法，以解决2009年H1N1和COVID-19大流行病的各个方面，疾病的传播分别由开源Monte Carlo模拟，Flute和Covasim建模。优化的目标是确定干预策略的最佳组合，以使社会的经济损失最小，而不是测试每一种可能的选择。为了达到我们的目标，这个面向应用的文件使用离散同时扰动随机逼近方法（DSPSA），递归模拟为基础的优化算法，更新输入参数的疾病模拟软件，使输出迭代地接近最小的经济损失。假设疾病传播的模拟模型（在我们的案例中，H1N1的Flute和COVID-19的Covasim）是所研究人群的准确表示，我们提出的基于模拟的策略为决策者提供了一个强大的工具，以减轻任何流行病造成的潜在人员和经济损失。基本方法也适用于其他公共卫生问题，如阿片类药物滥用和酒后驾车。
英文摘要：
Many public health threats exist, motivating the need to find optimal intervention strategies. Given the stochastic nature of the threats (e.g., the spread of pandemic influenza, the occurrence of drug overdoses, and the prevalence of alcohol-related threats), deterministic optimization approaches may be inappropriate. In this paper, we implement a stochastic optimization method to address aspects of the 2009 H1N1 and the COVID-19 pandemics, with the spread of disease modeled by the open source Monte Carlo simulations, FluTE and Covasim, respectively. Without testing every possible option, the objective of the optimization is to determine the best combination of intervention strategies so as to result in minimal economic loss to society. To reach our objective, this application-oriented paper uses the discrete simultaneous perturbation stochastic approximation method (DSPSA), a recursive simulation-based optimization algorithm, to update the input parameters in the disease simulation software so that the output iteratively approaches minimal economic loss. Assuming that the simulation models for the spread of disease (FluTE for H1N1 and Covasim for COVID-19 in our case) are accurate representations for the population being studied, the simulation-based strategy we present provides decision makers a powerful tool to mitigate potential human and economic losses from any epidemic. The basic approach is also applicable in other public health problems, such as opioid abuse and drunk driving.
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