RAPID: Active Tracking of Disease Spread in CoVID19 via Graph Predictive Analytics

2019冠状病毒病（COVID-19）已成为全球性的公共卫生危机。截至2020年4月10日，180多个国家约有170万例新冠肺炎确诊病例，其中10万多人死亡。在美国，确诊病例超过50万例，死亡病例近2万例，而且这些数字还在继续急剧上升。随着疫情的发展，显然迫切需要确保提供基础设施和关键服务。目前控制该流行病的计划是基于对流行病预测的完善的“隔间”模型的预测。这些模型依赖于基于均匀种群、均匀混合和几个关键超参数（如基本繁殖率）的假设的微分方程。传染病和流行病管理方面的专家都知道，将观察到的数据与这种模型的参数拟合是一种描述流行病学特征的工作，而不是产生有效和可操作的预测。因此，迫切需要大幅更新这些模型，以便考虑到从多个数据源和地点在地面收集的数据。这在设计先发制人的干预措施以遏制疾病传播方面尤其重要。当前的COVID - 19疾病数据以地理空间格式组织，即按地理位置索引感染、死亡和疑似病例，其粗糙程度可以从市、县或州不等。该项目旨在开发和演示利用数据的地理空间性质、疾病统计（连同预测）的时间演变以及综合多种数据来源的技术，以帮助迅速和先发制人地将现有医疗资源分配给最需要的地区。建立COVID-19流行病模型和设计干预措施是一项重大挑战。这个项目通过图形分析的视角来看待这个问题。特别是，它力求利用有关地理空间区域之间的相似性信息来改进流行病预测并设计有效的干预措施。作为第一步，流行病预测问题被建模为从低维观测重建高维动力系统。这样学习到的模型的估计将通过利用感兴趣的位置之间的相似性信息得到增强。虽然地理空间接近图是相似性图的自然候选图，但它无法捕获基于其他因素（如人口的社会学和生物学特征）的地理区域之间的长期统计依赖性。利用图形建模技术，该项目将开发新技术，用于在正在进行的大流行期间学习具有统计意义的流行病建模图形。此外，生成的准确时间序列预测将与基于图的相似性度量相结合，以设计有效的干预措施，以遏制疫情的传播。这正在使用随机公式和新兴的方法来处理时间序列观测图上的异常检测；以这些范例为基础的最佳政策将转化为应对不断演变的大流行病的干预战略。该项目通过恢复力知识交流（KER）与马里科帕县和亚利桑那州的当地社区利益相关者建立伙伴关系，实施所开发的方法，并确保其技术进步能够产生有意义的见解，可以在全国和全球推广。该奖项反映了美国国家科学基金会的法定使命，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。

Corona Virus Disease 2019 (COVID-19) has emerged as a public health crisis of global proportions. As of April 10, 2020, there are approximately 1.7 million confirmed COVID-19 cases in more than 180 countries, with over 100,000 deaths. In the US, there are more than 500,000 confirmed cases and nearly 20,000 fatalities, and these numbers are continuing to rise sharply. There is a clear and acute need for ensuring the availability of infrastructure and critical services as the epidemic progresses. Current plans for controlling the epidemic are based on forecasts from well established “compartment” models for epidemic prediction. These models rely on differential equations based on assumptions of homogeneous populations, homogeneous mixing, and knowledge of several critical hyperparameters such as the base reproduction rate. It is well known among experts in infectious diseases and epidemic management that fitting observed data to the parameters of such models is an exercise in characterizing the epidemiology as opposed to generating valid and actionable predictions. Consequently, there is an urgent need to significantly update these models to account for the data collected on the ground from multiple data sources and locations. This is especially relevant in engineering preemptive interventions to check disease spread. Current COVID disease data are organized in a geospatial format, i.e., infected, deceased, and suspected cases indexed by geolocation, which can range from city-, county-, or state-level coarseness. This project aims to develop and demonstrate techniques that use the geospatial nature of the data, the temporal evolution of disease statistics (along with predictions), and synthesis of multiple sources of data to help rapidly and preemptively allocate available medical resources toward the areas of greatest need.  Modeling the COVID-19 epidemic and designing interventions are significant challenges. This project looks at the problem through the lens of graph analytics. In particular, it seeks to use similarity information between geospatial regions of interest to improve epidemic predictions and to design effective interventions. As a first step, the problem of epidemic prediction is being modeled as the reconstruction of a high-dimensional dynamical system from low-dimensional observations. The estimates of a model thus learned will be enhanced by leveraging similarity information between the localities of interest. While the geospatial proximity graph is a natural candidate for the graph of similarities, it fails to capture long-range statistical dependencies between geographical regions based on other factors such as the sociological and biological features of a population. Using techniques from graphical modeling, this project will develop new techniques for learning statistically meaningful graphs for epidemic modeling during an ongoing pandemic. Furthermore, the accurate time-series prediction generated will be combined with the graph-based similarity measures to design effective interventions to check the spread of the epidemic. This is being approached using a stochastic formulation and emerging methods for anomaly detection on graphs with time series observations; optimal policies based on these paradigms will be translated into interventional strategies for an evolving pandemic. The project leverages partnerships with local community stakeholders in Maricopa County and the State of Arizona through the Knowledge Exchange for Resilience (KER) to implement the methodologies developed, and to ensure its technical advances can produce meaningful insights that can generalize nationally and globally. 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.