RAPID: Real-time updating of an agent-based model to inform COVID-19 mitigation strategies.
Division of Environmental Biology
2027718
Grant
Katharina Dittmar
$199,883.00
Alex Perkins
[email protected] (Principal Investigator)
University of Notre Dame
940 Grace Hall
NOTRE DAME
IN
465565708
The SARS-CoV-2 virus is responsible for the most significant pandemic in a century. With a vaccine not yet available, non-pharmaceutical interventions (NPIs) offer the only way to control the virus at this time. Those interventions, which include social distancing, school closures, and sheltering in place, may be effective if timed appropriately and adopted widely. At the same time, NPIs cause serious social and economic disruption, meaning that they must be used as sparingly as possible. To inform decisions about when to adopt NPIs, and when to relax them, it is important to understand what the consequences of those actions might be. This research will advance the capability of mathematical models to provide insight into those consequences. Looking to past data, the researchers will use statistical approaches to estimate key unknowns, such as numbers of people previously or actively infected. Looking into the future, the researchers will use simulation modeling of communities across the United States to evaluate the consequences of alternative actions. Merging these approaches will capitalize on the strengths of each, resulting in improved projections of the consequences of alternative strategies for mitigating the COVID-19 pandemic in the United States.
This project will feature a geographically realistic, agent-based model of SARS-CoV-2 transmission in the United States. Advantages of this model include its detailed portrayal of the density, demography, and movement patterns of people in specific counties across the United States, and its ability to directly implement NPIs through behavior modification of agents. These features enable locally tailored projections of SARS-CoV-2 transmission and impacts of NPIs thereon. At the same time, the computational demands of agent-based models pose a challenge to using them in the fast-paced context of a pandemic. To overcome that challenge, the researchers will use less computationally demanding statistical approaches to estimate inputs for use in the agent-based model up to a given point in the pandemic. The agent-based model will then simulate forward from that time under alternative scenarios about use of NPIs. To further safeguard against computational demands being a limiting factor for producing timely results, the researchers will make use of high-performance computing resources to perform batches of simulations of the agent-based model that account for stochasticity and parameter uncertainty. Results will be publicly disseminated on a regular basis over the course of the project, and the researchers will coordinate with stakeholders to ensure that mitigation scenarios under consideration remain relevant as the pandemic progresses.
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.