NYU CUSP Professors Debra Laefer, Paul Torrens, Maurizio Porfiri, and Constantine Kontokosta have begun new research to understand the spread, impact, and threats from COVID-19 facing New York City and other cities worldwide.
Recent National Science Foundation (NSF) Rapid Research Grants include:
- Debra Laefer (Principal Investigator) – Professor of Urban Informatics at CUSP
- Thomas Kirchner (Co-Principal Investigator) – NYU School of Global Public Health
The DETER project will collect data sets that can transform the study of virus transmission from two-dimensional mapping exercises into highly detailed, three-dimensional propagation models to better equip communities with the information they need for improved disease tracking, community-transmission prediction, and preventative disinfection strategies. The project will provide new types of data related to human behavior when leaving healthcare facilities that will allow more localized disease transmission models to be created. The project will track human behavior in terms of where people go (e.g. bus, coffee shop) and how they physically interact with the environment (i.e. what they touch and for how long). The project will immediately make publicly available data that could be critical for modeling virus-based outbreaks including predicting further community transmission during the current COVID-19 pandemic.
Community-transmission is responsible for over three-quarters of the COVID-19 cases in the US. Yet, current models do not consider localized behavior to predict virus transmission or the extent of propagation within individualized settings and their surrounding communities. The DETER project will provide such data and demonstrate new three-dimensional means to understand community-level risk. The DETER project investigates how generalizable human behavior is in terms of destination selection after visiting a healthcare facility and the extent and types of hand-based interaction with the built environment. These questions will be answered through tracking individuals when leaving healthcare facilities and recording touch-based behaviors on public transportation and public accommodations. The project will provide a transferable framework and a data integration strategy that can be adopted into a wide variety of three-dimensional models and schema that will help equip researchers and local communities with better methods for predicting community-based transmission.
- NYU researchers rush to capture human interactions with 3D data on surfaces likely to carry COVID-19 (Press Release via NYU Tandon School of Engineering)
- COVID-19: Researchers Capturing Geospatial Interactions (via GeoDataPoint)
- NYU Research Documenting What People Touch as They Move Through Virus-Laden World (via Campus Technology)
Paul Torrens (Principal Investigator) – Professor of Urban Informatics at CUSP
The COVID-19 pandemic has altered the moment-to-moment activities of our daily public lives. Some communities have initiated restrictions on the movement, activities, physical interaction, and socialization of large sections of the population. These actions have been borne of necessity, in a bid to reduce human contact as part of widespread efforts to mitigate the potential spread of the virus. Social distancing measures have taken on a sense of urgency in population-dense metropolitan areas, which host a large portion of the COVID-19 cases. This project will launch a rapid effort to acquire high-resolution data regarding life on streetscapes during the pandemic, with the goal of producing quick-response insight as changes in public spatial behavior unfold. This will be done by capturing and coding immersive, first-person, geolocated video- diaries of metropolitan residents going about their daily streetscape activity, as life shifts to adapt to new social distancing and curfew orders. The data will be disseminated broadly through local community partnerships. Additionally, the project will fund four graduate students in diverse STEM related fields.
This research captures how new forms of spatial behavior emerge, while testing how existing theories of spatial behavior hold under extraordinary circumstances. The central innovation is to focus on individual embodiment in day-to-day streetscape scenes, as revealed in latent and overt spatial behavior through body language in public places. This will be accomplished through first-person video footage of everyday streetscape scenes from a group of recruited volunteers as they go about daily activities during a pandemic. The data will be hand and machine coded to explore patterns of spatial behavior that can indicate relationships between individuals, the built environment, and socio-behavioral phenomena. Emergent relationships will be fine-tuned, using a series of studio-based experiments after the fact, deploying motion capture to methodically and empirically trace-out pathways between non-verbal communication such as gestures and mannerisms, and high-resolution space-time details of spatial behavior, in a controlled setting that utilizes the collected video data as ground truth. To promote broader use of the data and to foster additional research, data will be collated from both the field and from the studio experiments using high-resolution space-time Geographic Information Systems (GIS) and virtual geographical environments. These resources will be made publicly available and shared with local partners, with implications for safeguarding public health and wellbeing.
- 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
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.
- Constantine Kontokosta (Principal Investigator) – Industry Associate Professor of Urban Science and Associated Faculty at CUSP
- Arpit Gupta (Co-Principal Investigator) – NYU Stern School of Business
- Lorna Thorpe (Co-Principal Investigator) – NYU Grossman School of Medicine
The disasterous spread of COVID-19 has highlighted both the growing global risk of emerging pandemics and the urgent need for enhanced data-driven tools to identify, contain, and mitigate their effects, particularly in dense urban areas. Large-scale, high-resolution data on mobility, travel behavior, and proximate contacts can be used to enhance computational modeling of chains of transmission and future infection outbreaks and localized at-risk “hotspots”. However, current efforts using ad hoc and manual collection of contact and travel data are time and resource consuming, and subject to recollection errors and incomplete histories. Given significant diversity in local, regional, and international movement patterns, simple assumptions of homogenous mobility behaviors, like those typically used in agent-based spatial epidemiological models, can add uncertainty to predictions of transmission risk and outbreak progression. To address these limitations and advance the science and practice of COVID-19 computational modeling, the study team will use a large-scale geolocation dataset, provided by VenPath, Inc., derived from smart phone location information from over 200 applications for approximately 60 million unique users across the United States.
This project will develop computational models derived from the high-resolution mobility data to (1) estimate the contact density for individual users and localities, (2) monitor and measure the extent and effectiveness of “social (physical) distancing” efforts, and (3) predict the geographic extent of disease spread based on movement and travel patterns for individual communities. 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. Furthermore, the study team proposes to demonstrate how these data can be used in public health communication during disease outbreaks by identifying “at-risk” communities based on predicted movement patterns from specific neighborhoods and neighborhood socioeconomic characteristics influencing residents’ vulnerability. The data processing workflow and mobility models proposed can provide a new resource to epidemiologists and public health officials as they plan, implement, and evaluate the full range of policy responses to infectious disease outbreaks. Specifically, Objective 1 enables officials and researchers to understand the potential transmission rate in a given locality based on the estimated contact density derived from contact tracing using smartphone data. Objective 2 allows for both a real-time and ex post (assuming access to current data) evaluation of social distancing measures and their relative effectiveness in minimizing further spread of infection. Objective 3 creates a methodology to predict the spatio- temporal patterns of disease outbreak and identify “at-risk” locations based on the estimated contact density and mobility trajectories for areas were infections have been identified. This research is intended to provide proof-of-concept locational data analysis tools. It is focused on foundational data processing and model building to support public health decision-making. It is also designed to enable a rapid scale-up in capabilities and operational decision-making using current locational data as actual infection locations are identified. Furthermore, the study team will develop a visualization tool to support web-based analysis of the developed models and metrics. This tool will be made available to public health officials and other decision-makers. Beyond the current threat posed by COVID-19, the tools developed in this project could be applied to a full range of emergency situations, such as hurricanes, flooding, or other natural disasters.