Publications by authors named "Maytal Dahan"
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.
View Article and Find Full Text PDFReal-time pandemic surveillance using hospital admissions and mobility data.
Proc Natl Acad Sci U S A
February 2022
Article Synopsis
- The forecasting of COVID-19's impact has faced challenges due to biased case reporting and delays in death counts, making accurate predictions difficult.
- A new model that combines hospital admissions with mobility data has proven effective in predicting transmission rates and healthcare demand for COVID-19, guiding policies in Austin, TX.
- The model showed a high initial reproduction rate of COVID-19, a notable improvement in case detection by January 2021, and a significant decrease in transmission due to increased public safety measures, demonstrating its reliability in predicting future healthcare needs.