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

由于无症状传播、高潜伏期、人类流动性、天气模式和可用检测数量有限等各种因素，跟踪和控制新冠肺炎大流行一直很困难。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，监测和适当要求隔离将变得困难。因此，该项目旨在通过设计更有针对性和适应性的测试和数据驱动的干预来改进新冠肺炎监测。通过监测和干预应用，该项目通过制定应对这一流行病的程序和行动直接应对问题，并对其传播进行建模和了解。除了直接应用于新冠肺炎大流行之外，所开发的工具应该更广泛地适用于其他传染病环境(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：将系统动力学数据(PD)与直线表进行比对；任务2：使用比对数据推断传播链与新感染的关系。工作队以前在干预和监测方面的工作在这方面非常成功。这些推断的传播链自然为在新感染中对谁进行适应性监测和隔离提供指导。该项目将发布其方法作为研究代码，从业人员和建模人员都应该使用这些代码，以便在资源受限的情况下进行更快的监控。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.