LEAPS-MPS: Enhancing Dynamic Population-Level Epidemiological Models by Incorporating Wast — NSF Award to Lawrence Technological U
Mathematical modeling has played a crucial role in assessing and forecasting the impact of the COVID-19 pandemic and informing public health policies. However, existing models often fail to consider underreported clinical cases, resulting in inaccurate estimates of epidemiological parameters and flawed forecasts. Meanw
| Award title | LEAPS-MPS: Enhancing Dynamic Population-Level Epidemiological Models by Incorporating Wast |
|---|---|
| Award ID | 2316809 |
| Awardee | Lawrence Technological University |
| City | SOUTHFIELD |
| State | MI |
| Amount obligated | $249,313 |
| Principal investigator | Bruce Pell |
| Program | LEAPS-MPS |
| Start date | 08/01/2023 |
| Abstract | Mathematical modeling has played a crucial role in assessing and forecasting the impact of the COVID-19 pandemic and informing public health policies. However, existing models often fail to consider underreported clinical cases, resulting in inaccurate estimates of epidemiological parameters and flawed forecasts. Meanwhile, wastewater surveillance has emerged as a promising tool for capturing data from a diverse population, including asymptomatic individuals and those not captured by clinical te |
| Source | NSF Awards |
$799/mo
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