Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Abstract:

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 pbkp_redictions 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.org/) collected, disseminated, and synthesized tens of millions of specific pbkp_redictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined pbkp_redictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made pbkp_redictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when pbkp_redicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Año de publicación:

2022

Keywords:

• Model Evaluation
• covid-19
• Ensemble forecast
• forecasting

Fuente:

scopus