Award Number
Description
The COVID-19 pandemic has highlighted the urgent need to develop reliable tools to
forecast the trajectory of epidemics and pandemics in near real-time. We describe and
apply an ensemble n-sub-epidemic modeling framework for forecasting the trajectory of
epidemics and pandemics. We systematically assess its calibration and short-term fore-
casting performance in weekly 10–30 days ahead forecasts for the COVID-19 pandemic
in the USA from late April 2020 to late February 2022 and compare its performance with
two different statistical ARIMA models. This framework demonstrated reliable forecast-
ing performance and substantially outcompeted the ARIMA models. The forecasting per-
formance was consistently best for the ensemble sub-epidemic models incorporating a
higher number of top-ranking sub-epidemic models. The ensemble model incorporating
the top four ranking sub-epidemic models consistently yielded the best performance, par-
ticularly in terms of the coverage rate of the 95% prediction interval and the weighted
interval score. This framework can be applied to forecast other growth processes found in
nature and society, including the spread of information through social media