| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
|
| Initial Amendment Date: | June 9, 2020 |
| Latest Amendment Date: | June 9, 2020 |
| Award Number: | 2027908 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Ann Von Lehmen
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | June 15, 2020 |
| End Date: | May 31, 2021 (Estimated) |
| Total Intended Award Amount: | $50,000.00 |
| Total Awarded Amount to Date: | $50,000.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
1 NASSAU HALL PRINCETON NJ US 08544-2001 (609)258-3090 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
Guyot Hall Princeton NJ US 08544-1003 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | COVID-19 Research |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This project will use available data sets for COVID-19 in other countries, and in NYC, Virginia, and Maryland to build compartmental and metapopulation models to quantify the events that transpired there, and what interventions at various stages may have achieved. This will permit gaining control of future situations earlier. The epidemic models developed during this project will lead to innovations in computational epidemiology and enable approaches that mitigate the negative effects of COVID-19 on public health, society, and the economy.
Based on publicly available data sets for COVID-19 in other countries, and in NYC, Virginia, and Maryland, the researchers propose to build compartmental and metapopulation models to quantify the events that transpired there, understand the impacts of interventions at various stages, and develop optimal strategies for containing the pandemic. The basic model will subdivide the population into classes according to age, gender, and infectious status; examine the impact of the quarantine that was imposed; and then consider additional strategies that could have been imposed, in particular to reduce contact rates. The project will apply and extend the approach of "transfer learning" to this problem. The research team is well positioned to conduct this research; they have a long history of experience tracking and modeling infectious disease spread (e.g., Ebola, SARS) and are already participating in the CDC forecasting challenge for COVID-19.
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
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
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 Collaborative RAPID project “Transfer Learning Techniques for Better Response to COVID-19” sought to understand how the COVID-19 pandemic evolved in various parts of the world, and to see if experiences in one region could lead to better intervention strategies in another region. We anticipate the release of open source datasets we have developed that would be of use to the broader research and academic communities. And in a more expansive way, we sought to comprehend long term COVID-19 dynamics.
In the execution of this project, we generated the following outcomes.
-
Our team has developed new AI-driven forecasting methods
-
We have developed new agent-based models to study the complicated issues arising in the study of COVID-19 dynamics
-
We have made significant progress in understanding the role of networks in reducing overall transmissions in a population
-
We have developed methods that can lead to improved forecasts for COVID-19 dynamics, and have shared these forecasts with the CDC as part of the forecasting hub initiative
The project has made the following lasting impacts on public health:
-
Our work has led to a better understanding of the role of networks in the transmission and control of epidemics, specifically herd immunity; our results shed new light on this important topic
-
We are continuing to support the CDC as they respond to the COVID-19 pandemic via participation in the Forecasting Hub and the Scenario Hub; results were briefed to the national COVID-19 task force
Last Modified: 09/26/2021
Modified by: Simon A Levin
Please report errors in award information by writing to: awardsearch@nsf.gov.
