  We present a timely and novel methodology that combines disease estimates
from mechanistic models with digital traces, via interpretable machine-learning
methodologies, to reliably forecast COVID-19 activity in Chinese provinces in
real-time. Specifically, our method is able to produce stable and accurate
forecasts 2 days ahead of current time, and uses as inputs (a) official health
reports from Chinese Center Disease for Control and Prevention (China CDC), (b)
COVID-19-related internet search activity from Baidu, (c) news media activity
reported by Media Cloud, and (d) daily forecasts of COVID-19 activity from
GLEAM, an agent-based mechanistic model. Our machine-learning methodology uses
a clustering technique that enables the exploitation of geo-spatial
synchronicities of COVID-19 activity across Chinese provinces, and a data
augmentation technique to deal with the small number of historical disease
activity observations, characteristic of emerging outbreaks. Our model's
predictive power outperforms a collection of baseline models in 27 out of the
32 Chinese provinces, and could be easily extended to other geographies
currently affected by the COVID-19 outbreak to help decision makers.
