@Article{info:doi/10.2196/20285,
author="Liu, Dianbo
and Clemente, Leonardo
and Poirier, Canelle
and Ding, Xiyu
and Chinazzi, Matteo
and Davis, Jessica
and Vespignani, Alessandro
and Santillana, Mauricio",
title="Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models",
journal="J Med Internet Res",
year="2020",
month="Aug",
day="17",
volume="22",
number="8",
pages="e20285",
keywords="COVID-19; coronavirus; digital epidemiology; modeling; modeling disease outbreaks; emerging outbreak; machine learning; precision public health; machine learning in public health; forecasting; digital data; mechanistic model; hybrid simulation; hybrid model; simulation",
abstract="Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention. ",
issn="1438-8871",
doi="10.2196/20285",
url="http://www.jmir.org/2020/8/e20285/",
url="https://doi.org/10.2196/20285",
url="http://www.ncbi.nlm.nih.gov/pubmed/32730217"
}