Project Grant 2333494
- This $199,167 Project Grant from the National Science Foundation Division of Environmental Biology will support research to develop a data-driven model of imperfect immunity against COVID-19. The University of Southern California will receive funding to build upon data on COVID-19 reinfections and vaccine breakthroughs to better understand how imperfect protection affects SARS-CoV-2 transmission dynamics and the long-term risks of resurgence. The project directly supports the NSF's Biological...
- The National Science Foundation Division of Environmental Biology awarded a $186,835 Project Grant to the University of Southern California Department of Contracts and Grants to support the project "RAPID: FAST COVID-19 SCENARIO PROJECTIONS IN PRESENCE OF VACCINES AND COMPETING VARIANTS" from August 1, 2021 through July 31, 2022. This funding supports research under the Biological Sciences program (CFDA 47.074), which aims to promote advancement in the biological sciences and...
- This Project Grant award from the National Science Foundation's Mathematical and Physical Sciences Directorate provides $242,192 to Lawrence Technological University for the development of data-driven mathematical modeling tools to incorporate population stratification by vaccination status and virus variants in infectious disease spread models. The two-year project, titled "LEAPS-MPS: Incorporating Stratification by Vaccination Status and Virus Variants in Mathematical Models of Infectious...
- This $968,765 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) will develop a flexible modeling framework to simulate the impact of various testing-based infection control strategies for respiratory pathogens like SARS-CoV-2. The project aims to improve our ability to control existing respiratory diseases and enhance preparedness for future pandemics. Key deliverables include: Developing a...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to incorporate group-level behavioral responses into epidemic models. The award of $700,000 will support research to develop mathematical models and computational algorithms that capture how group behaviors, such as the formation of social bubbles and changes in risk-mitigating norms, can impact pandemic trajectories. The project, led by Northeastern University,...
- This $195,101 Project Grant, awarded by the National Science Foundation's (NSF) Biological Sciences program (CFDA 47.074), will use mathematical modeling to project the healthcare burden associated with COVID-19, influenza, and respiratory syncytial virus (RSV) in the United States during the 2023-2024 respiratory virus season. The research will integrate estimates of population immunity from prior infections, vaccinations, and monoclonal antibody therapy to produce four rounds of projections...
- This Project Grant, awarded by the National Science Foundation's (NSF) Division of Environmental Biology under the Biological Sciences program (CFDA 47.074), will provide $200,000 to The Johns Hopkins University to develop real-time hospitalization forecasting models for the United States. The models will leverage novel data sources like wastewater and genomic surveillance, alongside traditional epidemiological, mobility, demographic, socioeconomic, and behavioral data, to accurately assess...
- The National Science Foundation awarded a $1,000,000 Project Grant to the University of Chicago to support the Robust Epidemic Surveillance and Modeling (RESUME) project under the Engineering program of the Cross-Directorate Predictive Intelligence for Pandemic Prevention Phase I initiative. The grant period runs from August 1, 2022 to January 31, 2024. The RESUME project will develop predictive modeling capabilities and open data platforms to inform pandemic response decision making. An...
- The National Science Foundation (NSF) awarded a $200,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Virginia to develop data-driven, multimodal methods for behavior-based epidemiological modeling. The key objectives are to: Improve techniques for deriving meaningful insights from imperfect, real-world sensor data like mobile phones and search engine logs to capture complex human behaviors in real-time. Couple agent-based disease models...
- The National Science Foundation awarded a $195,381 Project Grant to Washington State University under the Biological Sciences program (CFDA 47.074) for the period of February 1, 2021 through January 31, 2024. The grant aims to fund the RAPID project "Curtailing Nosocomial Amplification of COVID-19," which seeks to develop epidemiological models to better understand and mitigate the transmission of COVID-19 in healthcare settings. As part of the grant, Research Triangle Institute will...
RAPID: RETROSPECTIVE COVID-19 SCENARIO PROJECTIONS ACCOUNTING FOR POPULATION HETEROGENEITIES -THE LONG-TERM BURDEN OF COVID-19 MAY VARY ACROSS RACES AND ETHNICITIES. TO ADDRESS THIS VARIAITON THIS PROJECT WILL EXTEND A CURRENT MODEL TO ACCOUNT FOR RACE AND ETHNICITY. THE AVAILABILITY OF OUTCOMES AND VACCINE UPTAKE DATA BY RACE/ETHNICITY IN THE US CREATES AN OPPORTUNITY TO EXPLICITLY MODEL THESE VARIABLES ACROSS THE GROUPS AND EVALUATE THE RESULTS FROM REAL-WORLD DATA. THE PROJECT WILL HELP US UNDERSTAND THE INEQUITIES OF COVID-19 OUTCOMES AND VACCINATION UPTAKE AND PREPARE THE US FOR THE FUTURE OF COVID-19 AND OTHER OUTBREAKS. THE PROJECT HAS THE POTENTIAL TO BE APPLICABLE WHEREVER RELEVANT DATA ON ETHNICITY AND RACE IS AVAILABLE, AND CAN BE EXTENDED TO OTHER TYPES OF GROUPS. THE PROJECT WILL INTEGRATE THE LESSONS LEARNED IN AN UNDERGRADUATE COURSE ON PROGRAMMING AND A GRADUATE-LEVEL CLASS ON MACHINE LEARNING FOR HEALTH. THE PROJECT WILL ALSO PROVIDE RESEARCH OPPORTUNITIES THROUGH A SENIOR CAPSTONE PROGRAM AND MINORITY-SERVING PROGRAMS SUCH AS THE USC JUMPSTART PROGRAM AND THE VITERBI SUMMER INSTITUTE. THE PROPOSED PROJECT WILL INTEGRATE DATA ON RACE AND ETHNICITY ALONG WITH VARIOUS OTHER DATASETS TO ACCOUNT FOR POPULATION HEALTH. THE KEY INNOVATION IN THE INTEGRATION IS THE ABILITY TO LEARN CONTACT MATRICES FROM DATA. THE PROJECT WILL USE A NOVEL APPROACH, WHERE THE N?N CONTACT MATRIX IS GENERATED BY N HIDDEN PARAMETERS THAT INDICATE THE LIKELIHOOD OF CONTACT OF A GROUP WITH A RANDOMLY SELECTED INDIVIDUAL. THE LEARNED CONTACT MATRIX WILL BE INTEGRATED WITH AN EPIDEMIOLOGICAL MODEL CURRENTLY BEING USED BY THE PI IN THE US SCENARIO MODELING HUB TO GENERATE LONG-TERM PROJECTIONS OF CASES, DEATHS, AND HOSPITALIZATION. THE APPOACH WILL COMPARE LEARNING CONTACT MATRICES WITH OTHER APPROACHES THAT DERIVE THOSE MATRICES FROM SURVEY DATA AND HIGH-RESOLUTION MOBILITY DATA. THE NEW APPROACH WILL ENABLE THE MODELING OF SUB-POPULATION INTERACTIONS WHEN SUCH MOBILITY DATA IS NOT AVAILABLE. THE MODEL WILL BE EVALUATED WITH GROUND TRUTH DATA OBSERVED OVER THE LAST THREE YEARS IN COLLABORATION WITH THE COVID-19 SCENARIO MODELING HUB. 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.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $0 | 5/1/25 | ||
| Not listed | $195.8k | 7/7/23 |