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We present a timely and novel methodology that combines disease estimatesfrom mechanistic models with digital traces, via interpretable machine-learningmethodologies, to reliably forecast COVID-19 activity in Chinese provinces inreal-time. Specifically, our method is able to produce stable and accurateforecasts 2 days ahead of current time, and uses as inputs (a) official healthreports from Chinese Center Disease for Control and Prevention (China CDC), (b)COVID-19-related internet search activity from Baidu, (c) news media activityreported by Media Cloud, and (d) daily forecasts of COVID-19 activity fromGLEAM, an agent-based mechanistic model. Our machine-learning methodology usesa clustering technique that enables the exploitation of geo-spatialsynchronicities of COVID-19 activity across Chinese provinces, and a dataaugmentation technique to deal with the small number of historical diseaseactivity observations, characteristic of emerging outbreaks. Our model'spredictive power outperforms a collection of baseline models in 27 out of the32 Chinese provinces, and could be easily extended to other geographiescurrently affected by the COVID-19 outbreak to help decision makers.

A machine learning methodology for real-time forecasting of the  2019-2020 COVID-19 outbreak using Internet searches, news alerts, and  estimates from mechanistic models