  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 from
neighboring counties, and a demographics-based shared exponential predictor. We
use prediction errors from the past five days to assess the uncertainty of our
death predictions, resulting in generally-applicable prediction intervals,
Maximum (absolute) Error Prediction Intervals (MEPI). MEPI achieves a coverage
rate of more than 94% when averaged across counties for predicting cumulative
recorded death counts two weeks in the future. Our forecasts are currently
being used by the non-profit organization, Response4Life, to determine the
medical supply need for individual hospitals and have directly contributed to
the distribution of medical supplies across the country. We hope that our
forecasts and data repository at https://covidseverity.com can help guide
necessary county-specific decision-making and help counties prepare for their
continued fight against COVID-19.
