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Using a hybrid of machine learning and epidemiological approaches, we proposea novel data-driven approach in predicting US COVID-19 deaths at a countylevel. The model gives a more complete description of the daily deathdistribution, outputting quantile-estimates instead of mean deaths, where themodel's objective is to minimize the pinball loss on deaths reported by the NewYork Times coronavirus county dataset. The resulting quantile estimatesaccurately forecast deaths at an individual-county level for a variable-lengthforecast period, and the approach generalizes well across different forecastperiod lengths. We won the Caltech-run modeling competition out of 50+ teams,and our aggregate is competitive with the best COVID-19 modeling systems (onroot mean squared error).

Ensemble Machine Learning Methods for Modeling COVID19 Deaths