Stochastic Modeling and Optimization to Support Emergency Response Systems

该奖项将通过开发新的数学和计算工具，为医院外健康紧急情况(如心脏骤停)的志愿者计划的设计提供信息，从而为国家的健康和福利做出贡献。当患者的心脏进入非典型节律时，就会发生心脏骤停。除非病人得到医疗救治，否则死亡很快就会到来。如果患者迅速接受心肺复苏(CPR)，生命可以得到拯救。最近，志愿者计划开始兴起，志愿者在智能手机上安装一款追踪自己位置的应用程序。接近心脏骤停的志愿者会收到应用程序的通知，并可以选择做出反应，从而节省了宝贵的反应时间来启动CPR。志愿者计划正在全球范围内涌现，但与其设计相关的关键问题仍未得到回答。例如，需要多少志愿者才能确保对存活率的影响，以及何时可以招募多种类型的志愿者，应该优先考虑哪种类型？这个项目开发了研究方法来回答这些问题和其他问题。相关的研究方向涉及宽带连接的潜在影响，宽带连接使远程护理人员或医生能够建议护理人员或救护车官员进行现场治疗，以及如何在紧急服务严重负荷和/或交通拥堵的时期确定调度决策的优先顺序。该奖项开发了数学和计算工具，为志愿者方案的设计提供信息。中心原则包括综合使用空间泊松点过程来对志愿者位置进行凸优化建模，以解决与志愿者能力在整个城市的分布相关的问题，以最大限度地提高患者存活率。采用近似动态规划方法，利用深度神经网络逼近价值函数和策略选择，解决了救护车载重严重时的相关调度问题。模拟优化将是这项工作的某些方面的基础。在这方面，将探讨在搜索中使用有偏梯度估计器。在仿真优化中，大多数梯度估计方法都会产生有偏估计，在实际应用中被认为是不令人满意的。这个项目将探索它们的潜在功效；它们在一些例子中被认为是非常有价值的，研究将更广泛地探索它们的潜力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award will contribute to the Nation's health and welfare by developing new mathematical and computational tools to inform the design of volunteer schemes for out-of-hospital health emergencies such as cardiac arrest. Cardiac arrest occurs when a patient’s heart enters an atypical rhythm. Death follows rapidly unless the patient receives medical attention. Lives can be saved if the patient receives cardiopulmonary resuscitation (CPR) quickly. Recently, volunteer schemes have arisen whereby volunteers install an app on their smartphone that tracks their location. Volunteers near a cardiac arrest are notified by the app and can choose to respond, thereby saving valuable minutes in the response time to initiate CPR. Volunteer schemes are emerging worldwide, but key questions relating to their design remain unanswered. For example, how many volunteers are needed to ensure impact on survival rates, and when one can potentially recruit volunteers of multiple types, which should be prioritized? This project develops research methods to answer these questions and others. Related research directions address the potential impact of broadband connections that enable a remote paramedic or doctor to advise on-scene treatment by a paramedic or ambulance officer, and how to prioritize dispatch decisions in periods when emergency services are severely loaded and/or traffic is congested.This award develops mathematical and computational tools to inform the design of volunteer schemes. Central principles include the integrated use of spatial Poisson point processes to model volunteer locations with convex optimization to solve problems relating to the distribution of volunteer capacity across a city to maximize patient survival. The related question of ambulance dispatch when ambulances are severely loaded is addressed through use of approximate dynamic programming, exploiting deep neural networks to approximate both the value function and the choice of policy. Simulation optimization will underlie some aspects of this work. In that context, the use of biased gradient estimators in search will be explored. Most methods for gradient estimation in simulation optimization result in biased estimators which are viewed in practice as unsatisfactory. This project will explore their potential efficacy; they have been seen to be of great value in some examples and the research will explore their potential more broadly.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.