DS3 Partners with NSF RAPID Grant to Win a New York State Geographic Information System COVID-19 Award

New York University has been instrumental in its research efforts against the COVID-19 pandemic. We’re excited to announce that the NSF-funded DETER project (“Developing Epidemiology Mechanisms in Three-dimensions to Enhance Response”), which looks at COVID-19 surface vector transmission, in partnership with our DS3 (Data Science and Software Services) initiative, has earned a New York State Geographic Information System COVID-19 Award.

About the Project

The project’s abstract (abbreviated):

The DETER project collected 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 provides 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 enables detailed tracking of 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), their gender, and whether or not they were wearing PPE. The project has made publicly available data that could be critical for modeling virus-based outbreaks including predicting further community transmission during the current COVID-19 pandemic… (read more)

In the US, ¾ of COVID-19 cases are due to community transmission. Despite this fact, current models do not consider localized behavior as a means to predict virus transmission or the extent of how individual settings can spread to surrounding communities. The objective of the DETER project was to provide said data and demonstrate new holistics means to comprehend the extent of community-level risk. Ultimately, the team will analyze this by tracking individuals once they leave health facilities and record touch-based behaviors on both public transportation and public accommodations. Ultimately, the project will contribute to the creation of a transferable framework and a data integration strategy that will equip researchers and local communities with better methods to predict community-based transmission.

About DS3

DS3 (Data Science & Software Services) is a joint venture of the CDS Moore-Sloan Data Science Environments (MSDSE) project, the NYU Libraries, NYU IT Research Technology, and the PRIISM Center, with connections to affiliated services such as Data Services, High Performance Computing (HPC), NYU IT Teaching and Learning with Technology (TLT), and more. The project creates a central service for faculty and research staff to access expertise in data science and statistical methodology as well as software engineering aligned with research goals. DS3 aims to enhance NYU’s research capacity by providing highly skilled labor for funded projects and increasing the competitiveness of grant proposals.

The DETER project was led by Debra Laefer, Professor of Urban Informatics & Director of Citizen Science at NYU’s Center for Urban Science & Progress and the Department of Civil and Urban Engineering, Thomas Kirchner, Assistant Professor of Social and Behavioral Sciences, at NYU School of Global Public Health, and NYU DS3 student Frank Jiang.

For more information on the DETER project, please visit the project’s NYU Spatial Data Repository page.

By Ashley C. McDonald



Official account of the Center for Data Science at NYU, home of the Undergraduate, Master’s, and Ph.D. programs in Data Science.

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NYU Center for Data Science

Official account of the Center for Data Science at NYU, home of the Undergraduate, Master’s, and Ph.D. programs in Data Science.