- Yury Dvorkin, Assistant Professor, NYU Tandon Electrical and Computer Engineering Department and CUSP
The ongoing COVID-19 outbreak has revealed numerous vulnerabilities in urban areas, including insufficiently robust supply chains and healthcare infrastructure. While those deficiencies became apparent at the very onset, other sectors could also be negatively impacted in the long run.
Assistant Professor Yury Dvorkin recognized that with many U.S. cities enforcing social-distancing and shelter-in-place policies in order to slow the pandemic, people were being forced to remain in their primary residences for extended periods of time — a state of affairs likely to have an effect on vital physical infrastructure systems such as gas, electricity, water, and transportation. Staying at home shifts daytime gas, electricity and water consumption to residential rather than commercial neighborhoods. Hence, unusual demand profiles for infrastructure services can result, possibly leading to outages and inability (or limited ability) to serve sheltered population groups. Failure to carefully analyze and mitigate the vulnerabilities of these systems during a disease outbreak could be devastating, affecting both general and energy-dependent populations.
With an NSF RAPID Grant, Dvorkin intends to design a model that can represent infrastructure operations under various disease-outbreak scenarios and inform the development of efficient strategies to mitigate these vulnerabilities. The project will bridge the gap between computational epidemiology and infrastructure modeling, taking into account both infrastructure issues and disease spread.
The ability to mitigate infrastructure outages caused by disease outbreaks is vital to ensuring the well-being of vulnerable population groups, many of whom are already under-served, he explained, and he anticipates that the methodological outcomes of the project and data-driven analyses could also be used in the fields of climate resiliency and environmental sciences, public health and response preparedness, as well as public policy.
Dvorkin heads the Smart Energy Research (SEARCH) Group, part of the Department of Electrical and Computer Engineering’s Power Lab. He is a receiver of the NSF CAREER award, Goddard Junior Faculty Fellow as well as a member of the Center for Urban Science and Progress faculty.
In densely populated urban environments, such physical infrastructure systems as gas, electric power and water networks are critical enablers of shelter-at-home policies that help slow down the spread of the COVID-19 outbreak. However, from the infrastructure viewpoint, these policies cause a major shift of gas, electricity and water consumption to residential rather than commercial neighborhoods, which may face unusual demand profiles for infrastructure services, possibly leading to outages and inability (or limited ability) to serve sheltered population groups. Such outages take societally unacceptable death tolls, even without factoring in the effect of the COVID-19 outbreak. For example, only among medically insured Americans, the estimated number of electricity-dependent persons residing at home was 685,000 (2012), among whom roughly one fifth is vulnerable to even short 3-4 hour power outages. The main goal of this proposal is to analyze infrastructure operations under different scenarios of disease outbreaks, using real-life infrastructure and public health data from New York City, and inform infrastructure operators and urban planners on efficient strategies to mitigate public health risks. In these analyses, the project team will specially focus on needs of vulnerable population groups.
The main technical objective of this proposal is to bridge the current gap between infrastructure and epidemic models to understand how the demand of infrastructure services change under different outbreak scenarios. To this end, we will first internalize the effects of health epidemics in infrastructure models. The epidemics model will include Susceptible-Infectious-Susceptible (SIS) and Susceptible-Infectious-Recovered (SIR) models, which make it possible to reasonably simulate the spread of infectious diseases as flu and influenza (SIS) and measles, mumps, rubella (SIR). Second, using real-life data on the COVID-19 outbreak in NYC and models of gas, electricity, transportation and water systems, our research team will carry out an infrastructure vulnerability analysis under different outbreak scenarios, which will be appropriately designed to represent potential trajectories of the COVID-19 outbreak (e.g. “bell” shape, when the outbreak peaks and then gradually decays or the “W” shape, when the outbreak can have multiple peaks). We will develop outage models of critical infrastructure equipment (water pumps, gas compressors, electric power transformers) in order to adequately capture accelerated wear-and-tear of these resources due to the atypical and shifted usage during the outbreaks.