- Maurizio Porfiri (Principal Investigator) – Affiliated Faculty at CUSP; Institute Professor, NYU Tandon School of Engineering
- Zhong-Ping Jiang (Co-Principal Investigator) – Professor, NYU Tandon School of Engineering
- Alessandro Rizzo – Visiting Professor, NYU Tandon School of Engineering
Using data from New Rochelle — the New York suburb first seriously afflicted by the virus — a team of NYU Tandon researchers will build a mathematical model specifically for the city’s unique social and transportation structures. Their goal: to help health and government leaders make smart, up-to-date testing and contact-tracing decisions.
Models from other contagions are not applicable to the novel coronavirus because they are confounded by its absence of symptoms in early stages, New York’s complex mobility patterns, and limited testing resources.
In a city where few own cars for drive-through assessment and testing is often conducted at hospitals already strained by the virus, the professors hope to provide quick, real-time scientific insights while factoring in the type and timing of testing, asymptomatic occurrence, and hospitalization stages. 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. The models will be made public at no cost.
The RAPID grant was awarded to Institute Professor Maurizio Porfiri of the Departments of Mechanical and Aerospace Engineering, Biomedical Engineering, Civil and Urban Engineering, and CUSP, along with co-principal investigator Professor Zhong-Ping Jiang of the Electrical and Computer Engineering Department. Visiting Professor Alessandro Rizzo from Politecnico di Torino, Italy, will also be part of the team; he earlier worked with Porfiri to develop contagion modeling. Other researchers on the project are Sachit Butail, formerly with Porfiri’s lab and now an assistant professor at Northern Illinois University; Agnieszka Truszkowska, a postdoctoral associate in the Mechanical and Aerospace Engineering Department; and Emanuele Caroppo, who is a leading expert on the COVID-19 spread in Italy.
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.