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

Computer and Information Science and Engineering - 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.