Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

• Published in: Journal of medical Internet research 2020-08, Vol.22 (8), p.e20285-e20285
• Main Authors: Liu, Dianbo, Clemente, Leonardo, Poirier, Canelle, Ding, Xiyu, Chinazzi, Matteo, Davis, Jessica, Vespignani, Alessandro, Santillana, Mauricio
• Format: Article
• Language: English
• Subjects: China - epidemiology; Clustering; Cognitive style; Coronavirus Infections - epidemiology; Coronavirus Infections - transmission; Coronaviruses; COVID-19; Data Analysis; Decision makers; Disease Outbreaks; Disease transmission; Epidemics; Estimates; Exploitation; Fever; Forecasting; Forecasting - methods; Health problems; Humans; Influenza; Internet; Keywords; Machine Learning; Mass Media; Models, Biological; Models, Statistical; News media; Original Paper; Pandemics; Pneumonia, Viral - epidemiology; Pneumonia, Viral - transmission; Provinces; Public health; Public Health - methods; Research methodology; Severe acute respiratory syndrome coronavirus 2; Time series; Time use; Trends; Visualization; Zoonoses
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/20285

The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events. We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time. Our method uses the following as inputs: (a) official health reports, (b) COVID-19-related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks. Our model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces. Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention.