RAPID: Data-driven Understanding of Imperfect Protection for Long-term COVID-19 Projection — NSF Award to University of Southern C
This project will use data on COVID-19 reinfections and vaccine breakthroughs to build a model of how imperfect immunity affects SARS-CoV-2 pathogen transmission dynamics and subsequent effects on numbers of cases, deaths, and hospitalizations. A key factor dictating the long-term dynamics of COVID-19 is how population
| Award title | RAPID: Data-driven Understanding of Imperfect Protection for Long-term COVID-19 Projection |
|---|---|
| Award ID | 2223933 |
| Awardee | University of Southern California |
| City | LOS ANGELES |
| State | CA |
| Amount obligated | $199,167 |
| Principal investigator | Ajitesh Srivastava |
| Program | ['01002122RB NSF RESEARCH & RELATED ACTIVIT'] |
| Start date | 05/15/2022 |
| Abstract | This project will use data on COVID-19 reinfections and vaccine breakthroughs to build a model of how imperfect immunity affects SARS-CoV-2 pathogen transmission dynamics and subsequent effects on numbers of cases, deaths, and hospitalizations. A key factor dictating the long-term dynamics of COVID-19 is how population immunity against COVID-19 changes over time and exposure. Data on vaccination breakthroughs and reinfections in various states of the US and countries around the world create a un |
| Source | NSF Awards |
$799/mo
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