RAPID: Dynamic Identification of SARS-COV-2 Transmission Epicenters in Presence of Spatial Heterogeneity (COV-DYNAMITE)

严重急性呼吸系统综合征冠状病毒2型（SARS-CoV-2）的迅速传播导致全球爆发，世界卫生组织宣布其为大流行病。与这种病毒（COVID-19）相关的疾病在老年人和患有合并症的人中具有较高的并发症和死亡率;然而，传播发生在所有年龄段的个体中，包括症状前或患有轻度疾病的个体。旨在遏制病毒传播的干预措施依赖于追溯病毒检测呈阳性的个人的接触史。由于SARS-CoV-2继续传播，病例数量增加，彻底追踪接触者可能不切实际。作为补充，从患者样本中获得的分子数据，如病毒基因序列，可用于重建流行病历史。通过系统发育和病毒动态分析，病毒基因组可以排列成类似于家谱树的结构，重建传播历史，即使大部分历史是未知或未报告的。因此，当接触者追踪存在问题时，这种方法对于立即了解快速传播的病毒（如SARS-CoV-2）的行为非常宝贵。此外，可以将数学建模应用于传输树，以预测在不久的将来的增长率和传播率。该项目的目的是克服SARS-CoV-2分子流行病学分析中确定为有问题的局限性-即抽样偏差-并推断假定的传播网络，这些网络涉及临界数量的相关病例，预计需要立即确定公共卫生优先次序。该项目将扩大现有的分子分析框架-传播中心动态识别，纳入一种改进的数据采样战略，以更可靠地重建历史传播，使用增长模型，以及一个基本的可视化组件，以便于用户在真实的时间内对数据进行解释。此外，该项目将朝着与其他软件工具集成的代码的可互操作实现方向发展。该项目的开发将由焦点小组指导，涉及遗传学，流行病学和公共卫生领域的领导人。在长期目标中，COV-EQUIPITE旨在通过提供对关键亚流行热点的预测来帮助公共卫生官员优先分配资源。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。

The rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a global outbreak, declared a pandemic by the World Health Organization. The disease associated with this virus (COVID-19) has higher complication and fatality rates for the elderly and those with comorbidities; however, transmission occurs among individuals of all ages, including individuals who are pre-symptomatic or have mild illness. Interventions aimed at containing the spread of the virus rely on retracing the contact history of individuals who have tested positive for the virus. As SARS-CoV-2 continues to spread and the numbers of cases rise, thorough contact tracing may be impractical. As a supplement, molecular data obtained from patient samples, such as viral genetic sequences, can be used to recreate the epidemic history. Through phylogenetic and phylodynamic analyses, virus genomes can be arranged in structures similar to a family genealogy tree, reconstructing transmission histories, even when much of the history is unknown or unreported. Such an approach is, therefore, invaluable in immediately understanding the behavior of rapidly spreading viruses, such as SARS-CoV-2, when contact tracing is problematic. Further, mathematical modeling can be applied to the transmission trees to predict rates of growth and spread in the near future. The purpose of this project is to overcome limitations identified as problematic in the molecular epidemiological analysis of SARS-CoV-2 – namely sampling bias – and to infer putative transmission networks that involve a critical mass of linked cases and that are predicted to require immediate public heath prioritization. The project will expand on an existing molecular analysis framework, the Dynamic Identification of Transmission Epicenters (DYNAMITE), incorporating a modified data sampling strategy for more reliable reconstruction of historical spread, the use of growth modeling, and a basic visualization component for user-friendly data interpretation in real time. Additionally, this project will move toward an interoperable implementation of code to be integrated with other software tools. The project development will be guided by focus groups, involving leaders in the field of phylogenetics, epidemiology, and public health. Among the long-term goals, COV-DYNAMITE aims to assist public health officials in prioritizing resources by providing projections on critical sub-epidemic hotspots.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.