Population-scale agent-based simulations of the opioid epidemic help evaluate intervention strategies and overdose outcomes in heterogeneous communities and provide estimates of localized treatment effects, which support the design of locally-tailored policies for precision public health. However, it is prohibitively costly to run simulations of all treatment conditions in all communities because the number of possible treatments grows exponentially with the number of interventions and levels at which they are applied. To address this need efficiently, we develop a metamodel framework, whereby treatment outcomes are modeled using a response function whose coefficients are learned through Gaussian process regression (GPR) on locally-contextualized covariates. We apply this framework to efficiently estimate treatment effects on overdose deaths in Pennsylvania counties. In contrast to classical designs such as fractional factorial design or Latin hypercube sampling, our approach leverages spatial correlations and posterior uncertainty to sequentially sample the most informative counties and treatment conditions. Using a calibrated agent-based opioid epidemic model, informed by county-level overdose mortality and baseline dispensing rate data for different treatments, we obtained county-level estimates of treatment effects on overdose deaths per 100,000 population for all treatment conditions in Pennsylvania, achieving approximately 5% average relative error using one-tenth the number of simulation runs required for exhaustive evaluation. Our bi-level framework provides a computationally efficient approach to decision support for policy makers, enabling rapid evaluation of alternative resource-allocation strategies to mitigate the opioid epidemic in local communities. The same analytical framework can be applied to guide precision public health interventions in other epidemic settings.

翻译：基于人群的鸦片类药物流行模拟有助于评估异质社区中的干预策略和过量用药结果，并提供局部处理效应的估计，从而支持为精准公共卫生设计本地化定制政策。然而，在所有社区中运行所有处理条件的模拟成本过高，因为可能的处理数量随着干预措施及其应用水平的增加呈指数级增长。为高效应对这一需求，我们开发了一个元模型框架，其中处理结果通过响应函数建模，该函数的系数通过基于局部情境化协变量的高斯过程回归（GPR）学习得到。我们应用此框架高效估计了宾夕法尼亚州各县过量用药死亡的处理效应。与传统的部分因子设计或拉丁超立方抽样等经典设计相比，我们的方法利用空间相关性和后验不确定性，顺序抽样信息量最大的县和处理条件。使用一个经过校准的基于代理的鸦片类药物流行模型，并结合县级过量用药死亡率及不同处理的基线分发率数据，我们获得了宾夕法尼亚州所有处理条件下对每10万人口过量用药死亡的县级处理效应估计，仅需穷举评估所需模拟运行次数的十分之一，即实现了约5%的平均相对误差。我们的双层框架为政策制定者提供了一种计算高效的决策支持方法，能够快速评估替代资源分配策略，以缓解当地社区的鸦片类药物流行。同一分析框架可应用于指导其他流行病情境下的精准公共卫生干预。