Indicator of county-level compounded vulnerability and risk to COVID-19 exposure and spread, hurricane impact, and flooding
Interactive map produced using kepler.gl
Deep learning model considering heterogeneous features, such as census data, intra-county mobility, inter-county mobility, social distancing data, and past growth of infection
Detecting Early-warning signals in Time Series of Visits to Points of Interests to Examine Population Response to COVID-19 Pandemic
Disparate Patterns of Movements and Visits to Points of Interests Located in Urban Hotspots across U.S. Metropolitan Cities during COVID-19
Effects of Population Co-location Reduction on Cross-county Transmission Risk of COVID-19 in the United States
Principal Investigator
Ph.D Student, Civil Engineering
Ph.D Student, Civil Engineering
Ph.D Student, Civil Engineering
Ph.D Student, Civil Engineering
Ph.D Student, Civil Engineering
Ph.D Student, Civil Engineering
MS Student, Computer Engineering
MS Student, Computer Science
MS Student, Computer Engineering
MS Student, Computer Engineering
Undergraduate Researcher,
Computer Science
Undergraduate Researcher,
Computer Science
Undergraduate Researcher,
Computer Science
Undergraduate Researcher,
Computer Science
Undergraduate Researcher,
Computer Engineering
Undergraduate Researcher,
Computer Science
Undergraduate Researcher,
Civil Engineering
Undergraduate Researcher,
Computer Engineering
We acknowledge funding support from the National Science Foundation (Award #: 2026814). Any opinions, findings, and conclusion or recommendations expressed in this research are those of the authors and do not necessarily reflect the view of the funding agency.
This work was supported in part by the Microsoft AI for Health COVID-19 Grant for cloud computing resources.