Curating a COVID-19 data repository and forecasting county-level death counts in the United States
Nick Altieri, Rebecca L. Barter, James Duncan, Raaz Dwivedi, Karl, Kumbier, Xiao Li, Robert Netzorg, Briton Park, Chandan Singh, Yan Shuo Tan,, Tiffany Tang, Yu Wang, Chao Zhang, Bin Yu

TL;DR
This paper presents a continuously curated COVID-19 data repository and develops county-level death forecasts in the US using ensemble methods, achieving over 94% coverage in prediction intervals and aiding medical supply distribution.
Contribution
It introduces a comprehensive data repository and a novel ensemble forecasting method for county-level COVID-19 death counts with reliable uncertainty quantification.
Findings
Ensemble method achieves >94% coverage in prediction intervals.
Forecasts assist medical supply planning and distribution.
Data repository supports county-specific COVID-19 decision-making.
Abstract
As the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely important role in informing policy decisions. In this paper, we present our continuous curation of a large data repository containing COVID-19 information from a range of sources. We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the county-level in the United States up to two weeks ahead. Using data from January 22 to June 20, 2020, we develop and combine multiple forecasts using ensembling techniques, resulting in an ensemble we refer to as Combined Linear and Exponential Predictors (CLEP). Our individual predictors include county-specific exponential and linear predictors, a shared exponential predictor that pools data together across counties, an expanded shared exponential predictor that uses data…
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