Effective Resource Planning and Disbursement during the COVID-19 Pandemic
Lead PI:
Quanyan Zhu

Uncertainties during global pandemics, such as the novel Coronavirus disease (COVID-19), can generate fear and anxiety, resulting in panic-buying and overreactive consumer behavior. Information from a multitude of sources may further exacerbate the situation, leading to shortages of critical disease prevention products for emergency managers and those in dire need. The consumer response may also vary based on population demographics and community interactions. This project aims to understand the relationships between consumer panic-buying, reports on infected cases, and local population demographics in a large and densely populated urban epicenter of the virus. Fundamental understanding of community factors and the role of reports on consumer behavior in emergencies will enable effective and timely decisions on resource planning and disbursement, preventing unexpected shortages of critical supplies in large and diverse urban centers. In addition, the quantitative methodologies developed in this project bridge the disciplines of engineering, computer science, social and health science, creating a new interdisciplinary paradigm that provides a holistic view towards emergency preparedness and disaster management in urban centers.

The main focus of this RAPID project is to develop a multi-network framework that captures the linkages and inter-dependencies between networks that govern information spreading, panic spreading, and disease spreading in urban populations. Fundamental understanding of the relationships between various factors such as consumer buying behavior, socio-economic community characteristics, and the extent of available health information enables the assessment of potential outcomes such as shortages of critical disease prevention supplies. Data and crowdsourced information from the COVID-19 experience of selected NYC neighborhoods is used as a case study for validation studies. An accurate understanding of the multi-faceted consumer behavior enables decision analytics for effective planning and targeted disbursement of critical supplies for mitigating the effects of panic-buying. The identification of underlying complex and interdependent network structures provides insights into the design of equitable and effective strategies for resource planning and allocation to tackle the vicious panic cycle in emergencies, thus promoting urban resilience.

Quanyan Zhu
Dr. Quanyan Zhu earned his PhD from the University of Illinois at Urbana-Champaign in 2013. His research fields of interest are: Game Theory and Applications Resilient and Secure Socio-Cyber-Physical Systems Adversarial Machine Learning and Signal Processing Human-Robot Interactions Internet of Things Game and Decision Theory for Cyber Security Economics and Optimization of Infrastructure Systems Resource Allocations in Communication Networks
Performance Period: 06/01/2020 - 05/31/2023
Institution: New York University
Sponsor: National Science Foundation
Award Number: 2027884