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

基本信息

  • 批准号:
    10709474
  • 负责人:
  • 金额:
    $ 45.9万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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.
项目概要 当已发生或新出现的传染病爆发时,我们会提出一组标准问题 对于挽救生命的公共卫生应对措施至关重要:未来的发病率将在哪里发生?会有多少个案例 有吗?我们可以在哪里进行最有效的干预?拟议的研究是由现实世界推动的 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:决定 2014-15 年是否以及在哪里建造更多埃博拉治疗单位 西非埃博拉疫情,以确定在该地区应使用口服霍乱疫苗的优先地区 2016-17 也门爆发霍乱,挑选可能发生足够病例的地点 优先采取干预措施以减缓 COVID-19 在全球范围内的传播。为此类决策提供信息的预测是 通常使用依赖于疾病传播知识的流行病模型生成 机制和流行病理论或使用统计模型来预测预期病例数 协变量和观察到的计数之间的关系。然而,这两种方法都受到限制, 特别是在流行病早期,观察到的病例很少。该项目基于总体 结合机械流行病模型和统计优势的推论的科学前提 协变量模型在预测和决策方面将大大优于单独的任何一种方法 应对新出现的传染病威胁。具体而言,该项目旨在 (1) 开发一个框架 结合机械和机器学习方法来预测持续爆发的发病率; (2) 使用回顾性数据验证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) 将这个推理预测框架融入因果决策理论 优化公共卫生应对新出现流行病的关键行动; (4) 开发 用于传染病流行预测和决策分析的可访问且可扩展的工具。 我们将通过严格的模拟研究并应用所提出的方法来验证这些方法 最近重要流行病(例如埃博拉、霍乱和 COVID-19,如上所述)的回顾性数据 多于)。我们将前瞻性地运用我们的方法来应对新出现的疾病威胁 发生在项目期间,包括正在进行的 COVID-19 大流行。确保所开发的工具 有用、高效且用户友好,我们将与国际人道主义组织合作应对 流行病。成功完成这些目标将为预测提供灵活且经过验证的框架 流行病持续期间的决策和决策,同时允许机械和统计方面的创新 接近。在此过程中,它将提供优化应对措施并降低发病率和死亡率的工具。 公共卫生危机。

项目成果

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Jessie Edwards其他文献

Jessie Edwards的其他文献

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{{ truncateString('Jessie Edwards', 18)}}的其他基金

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:
    10539401
  • 财政年份:
    2021
  • 资助金额:
    $ 45.9万
  • 项目类别:
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:
    10334519
  • 财政年份:
    2021
  • 资助金额:
    $ 45.9万
  • 项目类别:
Comparative effectiveness of tailored HIV treatment plans and mortality
定制的艾滋病毒治疗计划和死亡率的比较效果
  • 批准号:
    9270331
  • 财政年份:
    2016
  • 资助金额:
    $ 45.9万
  • 项目类别:
Comparative effectiveness of tailored HIV treatment plans and mortality
定制的艾滋病毒治疗计划和死亡率的比较效果
  • 批准号:
    10062470
  • 财政年份:
    2016
  • 资助金额:
    $ 45.9万
  • 项目类别:

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