| NSF Org: |
BCS Division Of Behavioral and Cognitive Sci |
| Recipient: |
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| Initial Amendment Date: | March 24, 2020 |
| Latest Amendment Date: | March 24, 2020 |
| Award Number: | 2027293 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Scott Freundschuh
BCS Division Of Behavioral and Cognitive Sci SBE Direct For Social, Behav & Economic Scie |
| Start Date: | May 1, 2020 |
| End Date: | April 30, 2021 (Estimated) |
| Total Intended Award Amount: | $98,856.00 |
| Total Awarded Amount to Date: | $98,856.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
70 WASHINGTON SQ S NEW YORK NY US 10012-1019 (212)998-2121 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
NY US 10012-1019 |
| Primary Place of Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Geography and Spatial Sciences |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.075 |
ABSTRACT
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.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The funded award RAPID: DETER: Developing Epidemiology mechanisms in Three-dimensions to Enhance Response undertook a short, longitudinal observational study related to COVID-19. The project united faculty experts in public health and geo-spatial technology to advance the conceptualization and application of three-dimensional epidemiology by documenting the behaviors of individuals in dense urban environments. Specifically, the project employed 16 student researchers to document the touch, personal protective equipment usage, and destination and transportation choices when egressing 16 New York City (NYC) hospital and urgent care facilities. This was done at 19 sites over the two months representing the initial peak of COVID-19 in NYC in the spring of 2020. The project generated over 5,000 records documenting the behaviors of over 6,100 individuals and reported the subjects’ genders, path of travel, time of day, day of week, and weather conditions, as well as the socio-economic characteristics of each neighborhood. The project had three main aims: (1) document perishable data that if not captured at the moment in time would be lost and wholly unrecoverable; (2) forge workflows that could systematically collect such data without generating privacy concerns, while still providing high detailed information about individual responses within the context of highly distinctive three-dimensional scenes; (3) enable and promote use of that data so that its value and the value of this new approach to public health outbreaks could be explored.
The intellectual of the merit of the work centered around the creation of new data collection protocols and in the three-dimensional representation of these behaviors. This enabled a clear understanding of the effectiveness of the New York City mask mandate, the psychological impact of the rising infection, hospitalization and fatality levels, and the impact of the state’s PAUSE order of non-essential businesses. The data enabled insights as to factors such as gender, the weather, and the local amenities of a neighborhood (e.g. access to different types of public transportation) over a two-month period.
The broader impacts of the work involved the employment and training of 16 students from a broad range of neighborhoods, backgrounds and academic disciplines, the engagement with a major software firm to modify their capabilities to better support the DETER data capture, and provision of a large, unique dataset to the public and to more than two dozen supervised research students including 8 high school students; 1 of which published a peer-reviewed paper in a ranked, international journal and 2 others received a state-level award in the area of geographic information systems (GIS) application. The project itself won a separate COVID-19 GIS award and has been the subject of dozens of on-line and in-print news articles and invited talks. The award also directly lead to a follow up project in the winter of 2020-2021 that expanded the study to 7 other US cities and 4 overseas sites, as well as revisiting 8 of the original sites. Finally the data are now the basis of an interdisciplinary, guided research class open to undergraduate and master’s level students.
Last Modified: 09/08/2021
Modified by: Debra F Laefer
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