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、潜伏期长、人员流动性、天气模式和可用检测数量有限等多种因素，追踪和控制 COVID-19 大流行非常困难。特别是随着病例数量的增加，监测和适当要求隔离将变得困难，正如其他国家的经验所表明的那样。因此，该项目旨在通过以数据驱动的方式设计更有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，该项目通过开发流程和行动来直接解决问题，以解决这一流行病，并建模和了解其传播。除了直接应用于 COVID-19 大流行之外，开发的工具应该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颖的数据集。该项目重点关注两项任务： 任务 1：将系统动力学数据 (PD) 与行列表对齐；任务 2：使用对齐数据推断新感染的传播链。该团队之前的干预和监测工作在这方面非常成功。这些推断的传播链自然会指导谁在新感染中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，可供从业者和建模者使用，以便在资源限制下更快地进行监控。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。