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

融合机器学习和机械模型以改进对新兴流行病的预测和推理

• 批准号: 10709474
• 负责人: Jessie Edwards
• 金额: $ 45.9万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10709474
• 关键词: African; Algorithms; Area; COVID-19; COVID-19 pandemic; Cholera; Cholera Vaccine; Communicable Diseases; Community Health; Data; Data Set; Decision Analysis; Decision Making; Decision Theory; Disease; Disease Outbreaks; Ebola; Emerging Communicable Diseases; Ensure; Epidemic; Evaluation; Fogs; Future; Geographic Locations; Incidence; International; Intervention; Knowledge; Liberia; Life; Link; Location; Machine Learning; Methods; Modeling; Morbidity - disease rate; Online Systems; Oral; Policies; Public Health; Research; Research Personnel; Series; Shapes; Statistical Algorithm; Statistical Methods; Statistical Models; System; Time; Translating; Update; War; Work; Yemen; case-based; cost; curve fitting; dashboard; disease transmission; epidemic response; experience; flexibility; improved; innovation; mortality; multidimensional data; new epidemic; outbreak response; programs; prospective; response; simulation; sound; surveillance data; theories; tool; transmission process; user-friendly

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.

项目摘要 当发生已建立或新兴的传染病的爆发时，我们提出了一组标准的问题 对于救生的公共卫生反应至关重要的：未来会发生在哪里？多少个案件 有？我们在哪里可以最有效地干预？拟议的研究是由现实世界的动机 回答这些问题对于做出实际公共卫生决策至关重要的情况和当前 方法很短：从决定是否以及在2014 - 15年度建造其他埃博拉治疗单元 西非埃博拉病毒的流行，以确定应使用口腔霍乱疫苗的优先区 2016-17也门霍乱疫情，选择可能发生足够案例的地点 优先考虑干预措施，以减缓世界各地的Covid-19的蔓延。告知此类决定的预测是 通常使用依赖疾病传播知识的流行病模型生成 机制和流行论或使用统计模型来投射基于的预期案例数 协变量与观察到的计数之间的关系。但是，两种方法都受到限制， 尤其是在几例病例时在流行病的早期。该项目基于总体 科学前提是将机械流行模型和统计学强度相结合的推论 协变量模型将在预测中单独胜过任何方法，并做出决定 面对新兴的传染病威胁。具体而言，该项目旨在（1）开发一个框架 预测在持续的爆发中，将机械和机器学习方法融合在一起； （2）使用回顾性数据验证框架并应用框架为决策提供信息 在新兴的流行病中； （3）将这个推论预测框架整合到因果决策理论中 在公共卫生对新兴流行病的反应中优化关键行动； （4）发展 可访问且可扩展的工具，用于传染病流行病的预测和决策分析。 我们将使用严格的模拟研究验证这些方法，并应用所提出的方法 从重要的最近流行病（例如埃博拉，霍乱和库维德19号）回顾性数据，如前所述 多于）。我们将前瞻性地采用我们的方法来告知对新兴疾病威胁的反应 在项目期间发生，包括正在进行的Covid-19-大流行。确保工具开发 我们将与国际人道主义组织合作，有用，高效且用户友好 流行病。这些目标的成功完成将为预测提供灵活且经过验证的框架 以及在持续流行期间的决策，同时允许在机械和统计上进行创新 方法。这样一来，它将提供优化反应并降低发病率和死亡率的工具 公共卫生危机。