Epidemiological Modeling Highlights

Created the ability to forecast new case counts at state, county, and metropolis scales using data-driven statistical models, enabling short-term planning of contact tracing staffing and testing capacity needs. For long-term forecasts, existing epidemiological modeling capabilities were combined to perform scenario-based analysis and mitigation planning to support decision makers with information on the effects of interventions before they are implemented.

Produced a COVID-19 data and visualization platform that includes comprehensive, data access and visualization capabilities to process near real-time, multi-modal and multi-source data to support informed decision making and monitor potential recovery efforts. Collected and curated disease data (600,000 data records per week over 12 weeks) with far more attributes than other public sites, creating a foundation for a unique national data resource to support future epidemiological and pandemic modeling.  In addition to assessing current spread of infection, this tool also assesses the impact of human dynamics on the infection spread, location, and availability of critical infrastructure, prediction, and high-performance computing driven simulation.
Modeling for transmission and related stress on public health infrastructure

Developed an approach to assess mobility behavior changes (personal and freight movements) in response to COVID-19. Cellular phone and vehicle derived mobility data were analyzed to reveal travel patterns for commercial activity by type and across industries including bars and restaurants as well as passenger, fleet, and heavy-duty vehicles.

Established a novel epidemiological modeling approach to quantify contact tracing, testing, and vaccination strategies in resource constrained environments and help identify optimal vaccination strategies for states and large metropolitan areas.

Created a unique map to illustrate, for every US county, both the rate at which the number of new COVID-19 cases is increasing over a week and the how this rate is changing over that same period, providing a unique perspective on the “transmission dynamics” and the criticality of the situation on a daily basis.

Calculated the demand for practitioner types, committed, and consumable resources for each U.S. county in multiple scenarios using output from epidemiological models. Models were also used to forecast economic impact of the COVID-19 event with multiple scenarios for virus progression and estimate the cumulative economic impacts of shutdowns and recovery strategies.

Developed and assessed scenarios for the greater Chicago and the New York City regions with emphasis on public transit impact and provided ridership forecasts for various degrees of economic reopening with aversion to shared mobility such as public transit.