Saurabh Amin on Impact of Mobility on Epidemic Spread September 17

The Colloquium on Digital Transformation is a series of weekly online talks on how artificial intelligence, machine learning, and big data can lead to scientific breakthroughs with large-scale societal benefit. The summer/fall series focuses on COVID-19 mitigation research.


See details of upcoming talks here and note we have the same
Zoom Webinar registration link for all forthcoming talks

 

Impact of Mobility on Epidemic Spread: Some Lessons from NYC and India

September 17, 1 pm PT/4 pm ET

Saurabh Amin, Robert N. Noyce Career Development Associate Professor of Civil and Environmental Engineering, MIT

 

In this talk, we analyze the impact of mobility services (in particular, public transportation systems) on the spread of COVID-19. We also propose testing strategies based on mobility between areas with heterogeneous risk levels to control disease transmission. Our work focuses on two distinct regions: (1) NYC – where a significant number of people rely on the MTA for their daily commute; and (2) Odisha – an eastern state in India which saw a significant influx of migrant workers from COVID hotspots. In both regions, high levels of human mobility and limited testing capacity led to a rapid increase of COVID cases. In the study of NYC, we run panel analysis to evaluate the relative impact of MTA ridership over general mobility on the growth in cases at the zipcode level. We find strong heterogeneities across zip codes, which can be explained by socioeconomic factors and unbalanced testing resources. Importantly, we find that while a higher level of general mobility is associated with an increase in cases, the MTA usage did not lead to additional growth in cases after April. In contrast, the district-level data from Odisha exhibits strong correlation between case growth and volume of incoming migrant workers from high-risk states after May. We construct an optimization model that allows the state health authority to effectively allocate testing resources to worker populations based on the heterogeneous risk levels at their origin states and the local district population. This work is joint with Manxi Wu, Isabel Munoz, Devendra Shelar, and R. Gopalakrishnan.

Saurabh Amin is an Associate Professor in the Department of Civil and Environmental Engineering at MIT. He is affiliated with the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center at MIT. He received his PhD in Systems Engineering from UC Berkeley (2011), M.S.E. from UT Austin (2004), and B.Tech. from IIT Roorkee (2002). His fields of expertise include stochastic control theory, applied game theory, and optimization in networks. His research focuses on the design of high-confidence monitoring and control algorithms for infrastructure systems. He is a recipient of an NSF CAREER Award, the Google Faculty Research Award, the Robert N. Noyce Professorship, and the Ole Madsen Mentoring Award.