CAREER: Big Computation and the Management of Emerging Infectious Diseases

新发传染病占全球疾病负担的25%以上，占全球死亡人数的32%以上。目前的EID，如中东呼吸综合征冠状病毒(MERS)和抗药性超级细菌，可能会对公共卫生造成毁灭性的影响。该项目正在开发的方法可用于将有关EID的实时数据转化为关于在哪里、何时和向谁进行干预的建议，以便在减少总体资源消耗的同时最大限度地减少疾病的负面影响。此外，这些建议被设计成可以立即在主题背景下解释，从而使决策者能够将来自复杂和不同种类的数据流的信息纳入疾病管理。应用这些方法有可能以比现有管理计划更低的成本减少死亡率和发病率。此外，支持干预建议的模型将产生有关EID动态的新知识。本研究项目旨在为在线序贯决策做出基础性贡献，并为EID的数据驱动管理创建一个新的统计框架。我们将EID概念化为分布在一组有限的位置上，这些位置可能是空间中的物理位置或网络中的节点。分配策略使EID的管理正规化，并由一系列功能表示，每个干预决定一个功能将EID上的最新信息映射到建议治疗的位置的子集。最优分配策略在EID持续时间内最大化一些平均效用函数。根据EID上的数据构建最佳分配战略具有挑战性，因为：(1)分配的数量与地点的数量呈指数关系；(2)估计和管理必须同时进行；(3)空间接近会导致因果干扰；(4)分配战略必须是专题专家可以解释的。我们整合了统计学、计算机科学、优化和疾病生态学的思想来克服这些挑战。我们结合模拟优化和策略搜索算法来构建最优分配策略的在线估计器；该策略权衡了探索改善疾病动态估计的分配选择与利用当前估计的动态来立即减缓EID的传播。我们证明了治疗分配问题可以转化为一个无限维强盗问题。我们利用这种联系来推导可扩展到非常大的分配问题的估计算法，并服从于理论研究。我们将我们的策略搜索和基于强盗的估计器与一类新颖的分配策略结合在一起，这些策略可以表示为主题专家可以立即解释的IF-THEN语句序列，并且可以根据专家的判断轻松进行调整。在这一类中，我们得到了估计的分配策略逼近误差的一个非参数下界；这个下界被用来对估计的最优分配策略进行拟合优度检验。

Emerging infectious diseases (EIDs) account for more than 25% of global disease burden and more than 32% of global deaths. Current EIDs like Middle East Respiratory Syndrome Coronavirus (MERS) and antibiotic-resistant superbugs have the potential to make devastating impacts on public health. The methodologies under development in this project can be used to translate real-time data on EIDs into recommendations about where, when, and to whom to apply interventions so as to minimize negative impacts of the disease while reducing overall resource consumption. Furthermore, these recommendations are designed to be immediately interpretable in a subject matter context, thereby empowering decision makers to incorporate information from complex and heterogeneous data streams into disease management. Application of these methodologies has the potential to reduce mortality and morbidity at lower cost than existing management plans. Furthermore, models underpinning intervention recommendations will generate new knowledge about EID dynamics. This research project aims to make fundamental contributions to online sequential decision making and to create a new statistical framework for data-driven management of EIDs. We conceptualize the EID as spreading across a finite set of locations, which might be physical locations in space or nodes in a network. An allocation strategy formalizes management of an EID and is represented by a sequence of functions, one per intervention decision, that map up-to-date information on an EID to a subset of locations recommended for treatment. An optimal allocation strategy maximizes some mean utility function over the duration of the EID. Construction of an optimal allocation strategy from data on an EID is challenging because: (i) the number of allocations is exponential in the number of locations; (ii) estimation and management must occur simultaneously; (iii) spatial proximity induces causal interference; and (iv) an allocation strategy must be interpretable to subject matter experts. We integrate ideas from statistics, computer science, optimization, and disease ecology to overcome these challenges. We combine simulation-optimization with policy-search algorithms to construct an online estimator of the optimal allocation strategy; this strategy trades off exploring allocation choices that improve estimates of disease dynamics with exploiting current estimated dynamics to immediately slow spread of the EID. We show that the treatment allocation problem can be recast as an infinite-dimensional bandit problem. We leverage this connection to derive estimation algorithms that scale to very large allocation problems and are amenable to theoretical study. We combine our policy-search and bandit-based estimators with a novel class of allocation strategies that can be expressed as a sequence of if-then statements that are immediately interpretable to subject-matter experts and can be readily adjusted based on expert judgment. We derive a non-parametric lower bound on the approximation error of an estimated allocation strategy within this class; this bound is used to perform goodness-of-fit tests for the estimated optimal allocation strategy.