Merging machine learning and mechanistic models to improve prediction and inference in emerging epidemics

项目总结 当一种既有或新发的传染病爆发时，我们会问一套标准的问题 这对拯救生命的公共卫生对策至关重要：未来的发病率将发生在哪里？有多少案件会 有吗？我们可以在哪里进行最有效的干预？建议的研究是受现实世界的推动 回答这些问题对于做出实际的公共卫生决策至关重要的实例，以及当前 方法不足：在2014-15年度决定是否以及在哪里建立额外的埃博拉治疗单位 西非埃博拉疫情，到确定优先地区应在哪些地区使用口服霍乱疫苗 2016-17年也门霍乱暴发，到挑选可能发生足够病例的地点，以选择和 优先采取干预措施，减缓新冠肺炎在全球的传播。通知这类决定的预测是 通常使用依赖于疾病传播知识的流行病模型来生成 发病机制和流行理论，或使用统计模型来预测预期病例数量 协变量和观测计数之间的关系。然而，这两种方法都受到限制， 特别是在疫情早期，观察到的病例很少。本项目是建立在拱形基础上的 将机械性流行病模型和统计学的优点结合起来的推理的科学前提 协变量模型在预测和决策方面将大大优于任何一种单独的方法 应对新出现的传染病威胁。具体地说，该项目旨在(1)开发一个框架，以 结合机械学和机器学习方法预测持续暴发的发病率； (2)使用回溯数据验证该框架，并应用该框架为决策提供信息 在新出现的流行病中；(3)将这种推理预测框架整合到因果决策理论中 优化公共卫生应对新出现的流行病的关键行动；和(4)制定 可访问和可扩展的工具，用于传染病流行的预测和决策分析。 我们将使用严格的模拟研究和应用建议的方法来验证这些方法 回顾近期重要疫情(如上文提到的埃博拉、霍乱和新冠肺炎)的数据 上图)。我们将前瞻性地应用我们的方法来为应对新出现的疾病威胁提供信息 在项目期内发生，包括正在进行的新冠肺炎大流行。以确保开发的工具 是有用的、高效的和用户友好的，我们将与国际人道主义组织合作，响应 流行病。这些目标的成功实现将为预测提供一个灵活和有效的框架 在持续流行期间进行决策，同时允许在机制和统计方面进行创新 接近了。通过这样做，它将提供工具，以优化应对措施，降低发病率和死亡率 公共卫生危机。

PROJECT SUMMARY When an outbreak of an established or emerging infectious disease occurs we ask a standard set of questions that are critical to a lifesaving public health response: Where will future incidence occur? How many cases will there be? And where can we most effectively intervene? The proposed research is motivated by real world instances where answering these questions was critical to making practical public health decisions, and current methods came up short: from deciding if and where to build additional Ebola Treatment Units in the 2014-15 West African Ebola epidemic, to identifying priority districts where oral cholera vaccine should be used in the 2016-17 cholera outbreak in Yemen, to picking locations where sufficient cases might occur to selecting and prioritizing interventions to slow the spread of COVID-19 worldwide. Forecasts informing such decisions are typically generated either using an epidemic model that relies on knowledge of the disease transmission mechanism and epidemic theory or using a statistical model to project the expected number of cases based on the relationship between covariates and observed counts. However, both approaches are subject to limitations, particularly early in an epidemic when few cases are observed. This project is based on the overarching scientific premise that inferences that combine the strengths of mechanistic epidemic models and statistical covariate models will substantially outperform either approach alone in forecasting and making decisions to confront emerging infectious disease threats. Specifically, this project aims to (1) Develop a framework to forecast incidence in ongoing outbreaks that merges mechanistic and machine learning approaches; (2) Validate the framework using retrospective data and apply the framework to inform decision making in emerging epidemics; (3) Integrate this inferential forecasting framework into causal decision theory to optimize critical actions in the public health response to emerging epidemics; and (4) Develop accessible and extensible tools for forecasting and decision analysis in infectious disease epidemics. We will validate these approaches using rigorous simulation studies and by applying the proposed approaches to retrospective data from important recent epidemics (e.g., Ebola, Cholera and COVID-19, as mentioned above). We will prospectively apply our approach to inform the response to emerging disease threats that occur during the project period, including the ongoing COVID-19 pandemic. To ensure that the tools developed are useful, efficient, and user friendly, we will work with international humanitarian organizations responding to epidemics. Successful completion of these aims will provide a flexible and validated framework for forecasting and decision making during ongoing epidemics, while allowing for innovation in mechanistic and statistical approaches. In doing so it will provide tools to optimize responses and reduce morbidity and mortality during public health crises.