RAPID: Collaborative Research: Using Phylodynamics and Line Lists for Adaptive COVID-19 Monitoring

RAPID：协作研究：使用系统动力学和线路列表进行自适应 COVID-19 监测

• 批准号: 2027848
• 负责人: Anil Kumar Vullikanti
• 金额: $ 5万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027848&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Using; Phylodynamics

It has been difficult to track and control the COVID-19 pandemic due to various factors such as asymptomatic transmission, high incubation period, human mobility, weather patterns and limited number of tests available. Especially as the number of cases rise, it will become hard to monitor, and request quarantine appropriately, as experience in other countries shows. Hence, this project aims to improve COVID-19 monitoring by designing more targeted and adaptive testing and intervention in a data-driven fashion. With both monitoring and intervention applications, this project directly attacks the problem through development of processes and actions to address this pandemic and also model and understand its spread. Apart from the immediate applications to the COVID-19 pandemic, the tools developed should be more broadly useful for other infectious disease settings (e.g. influenza). The team of Data Science, Network Science, Public Health and Phylogenetic analysis experts main approach for this question is to integrate several novel datasets via inference algorithms. The project focuses on two tasks: Task 1: Aligning phylodynamics data (PD) with line lists; and Task 2: Inferring transmission chains to new infections using aligned data. The teams prior works on interventions and monitoring have been highly successful in this regard. These inferred transmission chains naturally give guidance on whom to adaptively monitor and quarantine among the new infections. The project will release its methods as research code, which should be usable by both practitioners and modelers for faster monitoring under resource constraints.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.

由于无症状传播、高潜伏期、人员流动性、天气模式和可用测试数量有限等各种因素，COVID-19大流行难以追踪和控制。特别是随着病例数量的增加，正如其他国家的经验所表明的那样，将很难进行监测和适当的隔离。因此，该项目旨在通过以数据驱动的方式设计更具针对性和适应性的测试和干预来改善COVID-19监测。通过监测和干预应用程序，该项目通过制定处理这一流行病的程序和行动直接解决问题，并模拟和了解其传播。除了直接应用于COVID-19大流行之外，所开发的工具应更广泛地用于其他传染病环境（例如流感）。由数据科学、网络科学、公共卫生和系统发育分析专家组成的团队解决这个问题的主要方法是通过推理算法整合几个新的数据集。该项目侧重于两项任务：任务1：将病毒动态数据（PD）与线路列表对齐;任务2：使用对齐的数据推断新感染的传播链。在这方面，监测组先前的干预和监测工作非常成功。这些推断出的传播链自然会指导在新感染中对谁进行适应性监测和隔离。该项目将以研究代码的形式发布其方法，这些方法应可供实践者和建模者使用，以便在资源限制的情况下更快地进行监测。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。