Jeffrey Herrmann, Donald Milton, Hongjie Liu

Funding Agency

National Science Foundation




To fill in the gaps in knowledge about how to keep people safe in a university setting, three University of Maryland researchers have been awarded a two-year, $300K National Science Foundation EArly-concept Grant for Exploratory Research (EAGER) for Protecting University Communities from COVID-19 with Model-Based Risk Management.

The funding brings together a team experienced in analyzing infectious disease epidemics, formulating operational solutions, using models to predict disease spread down to the county level and evaluating social distancing policies, all with the goal of protecting public health. It includes principal investigator, Professor Jeffrey Herrmann (Mechanical Engineering and Institute for Systems Research in the A. James Clark School of Engineering); and two co-PIs, Professor Donald Milton (Applied Environmental Health) and Professor Hongjie Liu (Chair, Epidemiology and Biostatistics) in the School of Public Health.

The research team is collecting data about testing coverage and infection rates among students and faculty members and residents in neighboring areas to inform their modeling of intervention strategies. They will develop a comprehensive, data-enabled disease spread model tailored specifically to university demographics and operations. It will take student behavior and university and surrounding community demographics into account, identify strategic options for educational delivery and suggest ways to integrate empirical and conceptual models of disease spread with dynamic data in higher education environments.

Using novel mathematical and epidemic models that can estimate the trajectory of coronavirus infection, the effectiveness of mitigation strategies and predict the economic impact of different risk management options, the researchers will develop model-based decision support tools to help university administrators and public health officials evaluate their options.

These model-based decision support tools will be evaluated using an in-depth case study based at the University of Maryland.

A news story is available here.