| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | April 16, 2020 |
| Latest Amendment Date: | April 16, 2020 |
| Award Number: | 2027990 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Eva Kanso
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | May 1, 2020 |
| End Date: | April 30, 2023 (Estimated) |
| Total Intended Award Amount: | $161,124.00 |
| Total Awarded Amount to Date: | $161,124.00 |
| 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: |
New York 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): | COVID-19 Research |
| 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): | 47.041 |
ABSTRACT
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.
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.
In this project, we established a modeling framework to inform the decision-making and policy about testing and active surveillance policies during the COVID-19 outbreak. Our framework comprised a new, highly granular, agent-based COVID-19 modeling platform for performing "what-if" analyses related to the spread and control of COVID-19 pandemic. The open-source agent-based modeling platform included critical features, such as hospital-like and drive-through testing centers, testing effectiveness, mobility between locations, varying capacities, vaccination campaigns, and the existence of multiple strains. The platform was initially calibrated on of the town of New Rochelle, NY, where one of the first cases of COVID-19 were detected in the state, and then it was extended to study other cities of similar size. We continuously upgraded the platform to answer urgent questions that kept arising during the course of the pandemic. For example, as vaccinations reached testing phase, we investigated the effect of different vaccination strategies and the interplay between reopening efforts and vaccination campaigns. When vaccinations had been administered to a majority of the population for a few months, and questions related to immunity were raised, we studied the impact of booster rollouts and testing practices. In parallel, as additional variants emerged, we determined the effect of multiple strains concurrently propagating in the population. The platform was additionally used to investigate the role of urban design and the population structure on the evolution of the epidemic. These studies were complemented by independent analytical and modeling efforts focusing on questions related to the possibility of exposure to COVID-19 during emergency evacuations, correlations between the COVID-19 pandemic and the 2020 U.S. Presidential Elections, understanding of the role of deniers on the spread of an epidemic disease, and in the identification of drivers of rise and fall in infections during waves of infections. Several undergraduate students, graduate students, and postdoctoral fellows contributed to this project as they received training in dynamical systems modeling, time-series analysis, statistical analysis, and epidemiology, among many other disciplines. Findings from this project were disseminated through publications in international journals and presentations in technical meetings.
Last Modified: 05/24/2023
Modified by: Maurizio Porfiri
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