The United States COVID-19 Forecast Hub dataset

Abstract

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident hospitalizations, incident cases, incident deaths, and cumulative deaths due to COVID-19 at national, state, and county levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting

“…There are many models that attempt to predict the evolution of the COVID-19 epidemic. The Center for Disease Control and Prevention (CDC) in the United States allows different research teams across the globe to submit their forecasts of the number of cases and deaths 1 to 8 weeks in advance [27]. More than 100 teams have submitted at least one prediction to this competition.…”

COVID-19 Hospitalizations Forecasts Using Internet Search Data

“…Retrospective out-of-sample predictions of daily national hospitalizations were made every week from January 4, 2021 to December 27, 2021 by the three models and were then aggregated into weekly new hospitalizations. To further demonstrate the prediction accuracy and robustness of ARGO, we collected hospitalizations predictions of the two benchmark methods (COVIDhub-baseline and COVIDhub-ensemble) from COVID-19 forecast hub 3 . The COVIDhub-baseline is a persistent method that uses latest daily observation as future daily predictions 3 .…”

“…To further demonstrate the prediction accuracy and robustness of ARGO, we collected hospitalizations predictions of the two benchmark methods (COVIDhub-baseline and COVIDhub-ensemble) from COVID-19 forecast hub 3 . The COVIDhub-baseline is a persistent method that uses latest daily observation as future daily predictions 3 . The COVIDhub-ensemble uses medians of hospitalizations predictions submitted to COVID-19 forecast hub as its "ensemble" forecasts.…”

“…The Centers for Disease Control and Prevention (CDC) 3 has been collecting hospitalizations predictions from various research teams and making ensemble and baseline predictions since May 2020. According to the weekly COVID-19 forecasts submissions compiled by CDC 3 , machine learning 4,5 and compartmental models 6,7 are the most popular forecasting approaches 4,5 .…”

“…The Centers for Disease Control and Prevention (CDC) 3 has been collecting hospitalizations predictions from various research teams and making ensemble and baseline predictions since May 2020. According to the weekly COVID-19 forecasts submissions compiled by CDC 3 , machine learning 4,5 and compartmental models 6,7 are the most popular forecasting approaches 4,5 . For example, Rodríguez, et al 4 use a neural network architecture incorporating COVID-19 time series, and mobility information as inputs, whereas Jin, et al 5 utilize attention and transformer models to compare and combine past COVID-19 trends for future forecasts.…”