RAPID/Collaborative Research: Agent-based Modeling Toward Effective Testing and Contact-tracing During the COVID-19 Pandemic

Engineering - This Rapid Response Research (RAPID) grant will support research that will improve our understanding of the spread of COVID-19 and potential mitigation strategies at the city level, promoting scientific progress and contributing to national health and prosperity. As COVID-19 continues to spread, the effectiveness of different testing strategies and predictive models are brought into question. Testing strategies include the use of drive-through facilities that have found success elsewhere but may prove impractical for elderly and low-income sections of the population, and the use of hospitals, which adds further burden to the healthcare system and may carry the risk of higher contagion. Mathematical models that forecast the spread of the disease are of paramount importance to inform local and global policy makers on the course of action that should be undertaken to mitigate the outbreak and give relief to the population. However, such models are often confounded by the absence of symptoms in early stages, complex mobility patterns, and limited testing resources. This award supports fundamental research toward a mathematical model that will overcome these confounding factors, through advancements in dynamics and control. By explicitly modeling social and mobility constraints, this research will help increase the general well-being of communities and reduce disparities across the population. The model will afford the simulation of critical what-if scenarios and will include the evaluation of different testing policies and mitigation actions, thereby constituting a valuable support to policy makers involved in the containment and eradication of the epidemic. Research outcomes will be presented to the public, including health professionals and authorities to inform public policy in the ongoing crisis.

The research will respond to COVID-19 outbreak in real time through a fine-resolution agent-based and data-driven model that aims at providing unprecedented insight in the spread and potential mitigation strategies of this virus at the city level. The approach will afford thorough what-if analysis on the effectiveness of ongoing and potential mitigation strategies. The agent-based model will include COVID-19 specific features, such as the type and timing of testing, asymptomatic occurrence, and hospitalization stages. The framework will be grounded in publicly available census and geo-referred data from New Rochelle, New York. Social behavior associated with rational and irrational factors will be included in the mobility patterns of the agent-based model at multiple spatial and temporal scales to increase the granularity of the predictions. Network-theoretic and data-driven control strategies will inform enhanced testing protocols involving active trials on the basis of available contact databases collected at testing sites.

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.