ATD: Collaborative Research: Multi-task, Multi-Scale Point Processes for Modeling Infectious Disease Threats

ATD：协作研究：用于建模传染病威胁的多任务、多尺度点过程

• 批准号: 2124433
• 负责人: Frederic Schoenberg
• 金额: $ 14.99万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124433&HistoricalAwards=false
• 关键词: ATD; Collaborative; Research; Multi; task

This project develops new point-process based algorithms for modeling and forecasting event-level infectious disease data, such as when an epidemic is emerging, near elimination, or for contact tracing. The methods developed through the project have applications to source detection of super-spreader events, identification of case importation trends, and providing better risk assessments of emerging epidemics and future pandemics. The methods developed through the project also have applications beyond epidemiology where point processes are used, including social media, seismology, and criminology. The project will train two PhD students in statistics and computer science. This project will support one graduate student per year at each university for each of the three years of the grant. This project develops new point-process based algorithms for solving four important tasks that arise in modeling infectious disease threats over a range of temporal and spatial scales: 1) incorporating realistic transmission and reporting mechanisms, 2) link prediction in the transmission graph connecting separate geographic regions under surveillance, 3) source detection of the spatial-temporal and network locations of super-spreader events, and 4) modeling emerging disease epidemics over timescales of decades and spatial scales of the globe. Expectation maximization algorithms are derived to infer a probabilistic branching structure that can be used for contact tracing and source detection. Multivariate Hawkes processes are formulated to infer cross-transmission across separate geographic regions, where new theory and methods are needed to handle reproduction above the critical threshold of 1. Point process analogs to compartmental models are developed through the project that can incorporate realistic transmission and under-reporting mechanisms (e.g. exposure period, asymptomatic cases) to improve forecasts and prevalence estimation. Finally, this project develops models of emerging epidemic events for determining the separability of disease parameters vs. space and time and assessing the risk that an outbreak will become a pandemic.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.

该项目开发了新的基于点过程的算法，用于建模和预测事件级传染病数据，例如流行病何时出现，接近消除或接触者追踪。通过该项目开发的方法可应用于超级传播者事件的源头检测，确定病例输入趋势，并对新出现的流行病和未来的大流行病提供更好的风险评估。通过该项目开发的方法还具有流行病学以外的应用，其中使用点过程，包括社交媒体，地震学和犯罪学。 该项目将培训两名统计学和计算机科学博士生。该项目将在赠款的三年中每年资助每所大学的一名研究生。该项目开发了新的基于点过程的算法，用于解决在一系列时间和空间尺度上建模传染病威胁时出现的四个重要任务：1）结合现实的传输和报告机制，2）在传输图中连接受监视的单独地理区域的链路预测，3）超级传播者事件的时空和网络位置的源检测，以及4）在几十年的时间尺度和地球仪的空间尺度上对新出现的疾病流行进行建模。 期望最大化算法推导出一个概率分支结构，可用于接触跟踪和源检测。制定多变量霍克斯过程来推断跨不同地理区域的交叉传播，需要新的理论和方法来处理繁殖超过临界阈值1。 通过该项目开发了房室模型的点过程类似物，可以将现实的传播和漏报机制（例如暴露期、无症状病例）纳入其中，以改进预测和流行率估计。最后，该项目开发了新出现的流行病事件模型，用于确定疾病参数与空间和时间的可分性，并评估爆发将成为流行病的风险。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。