RAPID: Developing Advanced Modeling and Analysis Tools to track Human Movement Patterns and Coronavirus / Infectious Disease Spread Dynamics in Geographical Networks

The broader impact of this RAPID proposal is to to inform policy associated with pandemic viruses, such as coronavirus disease 2019 (COVID-19), as they significantly impact the national health and economy. To safeguard society from viruses, each country/state/locality needs to dynamically adjust health policies, plan near-term health care capacity, and control population movement with little time latency. Accurate real-time prediction of virus spread is essential for making the health system respond in a fast and proactive manner to disease variations and disruption events (e.g., staffing and supply shortage). This project pursues fundamental research to develop simulation models of human movement and virus spread dynamics, prediction of real-time positions of infected population in the spato-geographic network, and development of decision support tools for the design of healthcare policies under disruptive events and processes. The proposed research is at the interface of engineering and public health to gain a better understanding of disease spread dynamics from both perspectives. Effective simulation analysis and prediction of virus positions in geographic regions will not only help optimize the design of healthcare policies to control the propagation of infectious diseases, but also help safeguard the US population and make health systems more resilient to disruptive events.

This RAPID project will leverage data analytics and simulation models to gain a better understanding of virus spreading dynamics. The objective of this research project is to develop continuous flow simulation modeling and analysis of human movement/traffic and virus spread dynamics in spatial networks. Specifically, we will investigate the derivation of health policies and infectious disease control so that the healthcare system can respond expeditiously and effectively to disruptive events. The proposed project will study three sets of policy-relevant characteristics that are central to the understanding of the impact of public interventions on virus spread, namely regional infrastructure of health care delivery, regulatory measures to slow down the virus spread, and effectiveness of information transparency. As the dynamics of infectious diseases often change over time, simulation models and analytical algorithms from this project help support decision-making in real time. The proposed methodology is generally applicable to a wide range of infectious diseases.

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.