Impact of Mobility on Epidemic Spread: Some Lessons from NYC and India
C3 Digital Transformation Institute / 2020
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.
MIT Mobility Initiative
MIT Mobility Initiative / 2020
Generalized Benders Decomposition for Resilient Electricity Networks
Georgia Tech: Energy Systems and Optimization Workshop / 2020
Bilevel mixed-integer programs arise naturally in problems concerning power systems resilience. The Benders Decomposition method attempts to solve such problems by decomposing into a master problem and inner sub-problem(s), and solving them iteratively. In each iteration, a stronger relaxation of the master problem is solved by adding a Benders cut which involves a product-sum of variables in the master problem and values of dual variables in the inner problem. However, the classical method performs poorly when the inner problem consists of binary variables and non-linear constraints. In this work, we suggest a new heuristic to address this limitation by modifying the so-called Generalized Benders Decomposition. Our approach involves modifying the right-hand side of the Benders cut to be a value determined by the sum of values of a subset of inner dual variables. We show how the size of this subset can be selected to achieve a reasonable tradeoff between solution accuracy and computational effort. We also discuss how to strengthen the modified Benders cut by using the reduced cost of inner problem’s dual linear relaxation and leveraging the properties of power system restoration. This work is joint with Devendra Shelar and Ian Hiskens.
Panel on Transportation in the Future
Operations Research Center 65th Anniversary Celebration / 2019
Analytics-Driven Operations for Critical Infrastructure Resilience
New York University: C2SMART / 2019
This talk presents a prescriptive analytics framework for designing inspection and response operations of service utilities facing risks of natural disasters and security attacks. In the first part of the talk, we introduce a stochastic orienteering and network probing problem for localizing failures in the aftermath of a major natural disaster. We develop a predictive model for failure localization using data from inspection operations of a real-world natural gas pipeline network. Next, we exploit the problem structure to design a scalable non-adaptive algorithm based on integer programming. Our results lead to practical and efficient strategies for disaster response operations. In the second part of the talk, we consider the problem of monitoring vulnerable network components with the minimum number of smart detectors against multiple random or adversarial disruptions. This network inspection problem can be formulated as a mathematical program with constraints involving Nash equilibria of a large-scale strategic game. We develop a scalable approach that computes randomized monitoring strategies based on solutions of a minimum set cover problem and a maximum set packing problem, along with optimality guarantees. We demonstrate that the proposed framework effectively utilizes the available network data to improve the resilience of critical infrastructures against a broad class of failures. The first part is joint work with Mathieu Dahan and Georgia Perakis, and the second part is joint work with Mathieu Dahan and Lina Sela.
Highway Traffic Operations: Reliability and Security Failures
UC Berkeley Institute of Transportation Studies / 2018
Building cyber-enable resilience in infrastructure networks
UC Berkeley Workshop on the Future of Cyber-Physical Systems / 2016