RAPID: Time-Sensitive Human Forest and Model Forecasts for COVID-19 Vaccine and Treatment — NSF Award to American University (DC,
Accurate, time-specific predictions are important for planning and decision making during fast-moving pandemics. In particular, whether an effective COVID-19 vaccine will be available in 9, 12, or 18 months is an issue of vital national interest. The main objective of this project is to compare the accuracy of a new me
| Award title | RAPID: Time-Sensitive Human Forest and Model Forecasts for COVID-19 Vaccine and Treatment |
|---|---|
| Award ID | 2030015 |
| Awardee | American University |
| City | WASHINGTON |
| State | DC |
| Amount obligated | $200,000 |
| Principal investigator | Sauleh Siddiqui |
| Program | Decision, Risk & Mgmt Sci |
| Start date | 08/15/2020 |
| Abstract | Accurate, time-specific predictions are important for planning and decision making during fast-moving pandemics. In particular, whether an effective COVID-19 vaccine will be available in 9, 12, or 18 months is an issue of vital national interest. The main objective of this project is to compare the accuracy of a new method for crowd-based forecasting of time-specific outcomes–such as clinical trial transitions of COVID-19 treatments and vaccines–to that of new machine learning models. The resear |
| Source | NSF Awards |
$799/mo
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