Tandon COVID-19 research response is certifiably RAPID

NYU Tandon professors and their students are quickly responding to the need to understand the spread, monitoring, and treatment of the COVID-19 virus, and thanks to National Science Foundation Rapid Response Research (RAPID) funding, earmarked for research with severe urgency, field and lab work is already apace for several projects, many of which take advantage of the wealth of data about New York City. All involved hope to help the world to react better to this contagion and others in the future. 

• S. Farokh Atashzar
• Enrico Bertini
• Rumi Chunara
• Yury Dvorkin
• Zhong-Ping Jiang
• Constantine Kontokosta
• Debra Laefer
• Maurizio Porfiri
• Alessandro Rizzo
• Paul Torrens
• Yao Wang
• Quanyan Zhu

Intelligent, wearable, telehealth devices

S. Farokh Atashzar
Electrical and Computer Engineering, Mechanical and Aerospace Engineering, and NYU WIRELESS

Yao Wang
Electrical and Computer Engineering, Biomedical Engineering, and NYU WIRELESS

COVID-19 has caused a severe shock to the world’s healthcare systems. Continued spikes have been occurring, accompanied by serious outbreaks of pneumonia associated with the infection. Assistant Professor S. Farokh Atashzar and Professor Yao Wang realized the pressing need for smart and scalable wearable technologies that can be produced rapidly to assist in monitoring patients.

A recent NSF RAPID Response grant is supporting their development of a wireless smart IoMT (Internet of Medical Things) necklace containing sensors that can accurately, objectively, and continuously track multiple vital symptoms of respiratory malfunction and infection, thus covering a large spectrum of COVID19 symptoms, and predicting the probability of health anomalies through machine intelligence and data modeling.

The device – which will be capable of recording and tele-reporting the sound and vibrational dynamics of breathing and coughing, together with vital signs like body oxygen saturation level, temperature, and heart rate – could be used in both home and hospital settings. By means of novel machine learning models, the abstract outcoming information will aid practitioners in detecting early health anomalies and in predicting potential adverse events, reducing the burden on the health care system and the corresponding social and economic impacts.

Atashzar and Wang expect their work to open more doors to conducting fundamental research on the temporal evolution of COVID19 symptoms and behavior. And while the NSF is now targeting many COVID-related research projects for its RAPID grants, they stress that their device will also be of use for patients with other respiratory conditions, including lung cancer, making it relevant well beyond the current pandemic.

Finding a path through the forest of pandemic information

Enrico Bertini
Computer Science and Engineering

Rumi Chunara
Computer Science and Engineering

These days, it’s hard to open a newspaper or go online without seeing mention of COVID-19. News agencies, government organizations, and many others have created visualizations depicting pandemic-related information and forecasts. While providing people with accurate information is essential, whether it’s shown as a graph, chart, and or some different format, it’s difficult to know just how much the average layperson is absorbing. 

Researchers have shown that even common visualizations can be confusing. In the case of COVID-19, the problem is exacerbated by a lack of knowledge about personal risk and the nature of pandemic uncertainty. Enrico Bertini and Rumi Chunara are now testing how people understand existing COVID-19 data visualizations, in order to create guidelines for visualizing data in the future. By studying how changes in various factors influence perceptions of risk, they hope to gain an understanding of how people conceptualize compound uncertainties from different sources (such as uncertainties associated with location, time, demographics, and behaviors). 

Concurrently, they’re developing an interactive app that will allow people to understand their personal risk factors and learn about the impact of their actions on themselves and others. Users will be able to change the parameters of a simulation to see how that affects their risk judgments. For example, users in one area can see the pandemic risk to individuals of their age in their particular zip code and then determine how that risk would change if the infection rate increased or decreased. 

The NSF-funded work is the first of its kind to test the effect of user interaction to convey uncertainty through visualization.

Are Urban Infrastructure Systems Resilient Enough to Support Stay-in-Home Orders?

Yury Dvorkin
Electrical and Computer Engineering Department and Center for Urban Science and Progress (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.

Tracking the virus through mobility data

Constantine Kontokosta
Civil and Urban Engineering Department and CUSP, Urban Science and Planning at the NYU Marron Institute

A RAPID project recently funded by the NSF will marshal large-scale, anonymized mobility data to estimate variations in exposure density, measure and monitor the effectiveness of social distancing, and predict the spread of the disease based upon mobility patterns. 

The research will focus on understanding socioeconomic disparities in outbreak progression and the impacts of shelter-in-place mandates. Using these models, public health officials will be able to estimate the likelihood of successful containment efforts for specific localities and predict where future localized outbreaks and chains of transmission may emerge. The study team also hopes to use the data to identify at-risk communities based on predicted movement patterns and neighborhood socioeconomic characteristics influencing residents' vulnerability. 

Constantine Kontokosta leads the team; he is an associated faculty member in the Civil and Urban Engineering Department and CUSP, and is an associate professor of Urban Science and Planning at the NYU Marron Institute, as well as director of the Urban Intelligence Lab and director of Civic Analytics. Co-principal investigators are Arpit Gupta, an assistant professor of finance at the Leonard N. Stern School of Business, and Lorna E. Thorpe, a professor of population health at the NYU Grossman School of Medicine.

Don’t touch that!

Debra Laefer
Civil and Urban Engineering and CUSP

*UPDATE: In October, the New York State GIS Association, a group of Geographic Information Systems professionals, honored the project with its 2020 Fight against COVID-19 GIS Application/Dashboard Award, citing the opportunities for longitudinal investigations Laefer's work opens up.

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Researchers are already in the field recording potential hot spots outside hospitals and mass transit hubs to record what people are touching — and thus the most likely surfaces to carry the coronavirus. 

Virus mapping dates to 1854, when John Snow traced the source of a cholera epidemic in London to infected wells. NYU teams, however, will use geospatial data to pioneer a more accurate and effective tool for this virus: 3D mapping.

This study will also lay the groundwork for machine learning models to speed the analysis of how a virus spreads in urban areas. Its documentation and modeling could easily be applied to airports, grocery stores, and playgrounds — anywhere large groups of people come, touch things, and leave.

The lead investigator for the DETER project is Debra Laefer, a professor of civil and urban engineering at NYU Tandon who also serves as a professor of urban informatics and director of citizen science at CUSP; the co-leader for the project is Thomas Kirchner, director of the NYU mobile health lab and an assistant professor of social and behavioral sciences at the NYU School of Global Public Health. 

Testing in NYC: who, what, where?

Maurizio Porfiri
Mechanical and Aerospace Engineering, Biomedical Engineering, Civil and Urban Engineering, and CUSP

Zhong-Ping Jiang
Electrical and Computer Engineering Department

Alessandro Rizzo
Mechanical and Aerospace 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.

How are we adapting in public?

Paul Torrens
Computer Science and Engineering Department and CUSP

With a trove of data already in hand about how people move about on New York City streetscapes, Professor Paul Torrens’ research team was a natural to perform a compare-and-contrast study after COVID-19 hit. The NSF agreed, funding the project that could quickly inform decisions on curfews and social distancing in dense urban areas. After all, citizens’ actual reactions to each other can indicate just how strict regulations should be and potentially even help communities to forecast how the citizenry will respond once shelter-in-place rules are lifted.

This research captures how new forms of spatial behavior emerge while testing how existing theories of spatial behavior hold under extraordinary circumstances. Emergent relationships will be fine-tuned, using a series of studio-based experiments after the fact, deploying motion capture to trace pathways between non-verbal communication such as gestures and mannerisms, and high-resolution space-time details of spatial behavior. The resources will be made publicly available and shared with local partners, with implications for safeguarding public health and well-being.

Torrens is a professor in the Computer Science and Engineering Department and CUSP.

No reason to panic

Quanyan Zhu
Electrical and Computer Engineering and CUSP

Quanyan Zhu can understand why people have been emptying store shelves of milk and hand sanitizer. “Panic is a natural human behavior,” he says. Pair a propensity for panicked hoarding with the human predilection for sharing on social media, he explains, and a vicious cycle occurs: frantic buying drives Facebook and Twitter posts about shortages (real or perceived) beyond local markets, leading to further panic buying. “The subsequent shortages could include critical resources, such as medical supplies, which we have already seen,” he says. “People who need these goods in terms of vulnerability to infection, now don’t have them.”

The situation could be mitigated, Zhu believes, by reducing the panic response in social communities — an outcome that could be achieved by detecting misinformation in real-time, reinforcing the right information, and, on a broader level, allocating resources according to which regions are most likely to be subject to hoarding-related shortages. Zhu has received a National Science Foundation Rapid Response Research (RAPID) grant for his research, "Effective Resource Planning and Disbursement during the COVID-19 Pandemic," to develop models of population behavior that will make it easier to perform these tasks.