%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 22
%N 8
%P e20285
%T Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models
%A Liu,Dianbo
%A Clemente,Leonardo
%A Poirier,Canelle
%A Ding,Xiyu
%A Chinazzi,Matteo
%A Davis,Jessica
%A Vespignani,Alessandro
%A Santillana,Mauricio
%+ Computational Health Informatics Program, Boston Children’s Hospital, 300 Longwood Avenue, Landmark 5th Floor East, Boston, MA, 02215, United States, 1 (617) 919 1795, msantill@g.harvard.edu
%K COVID-19
%K coronavirus
%K digital epidemiology
%K modeling
%K modeling disease outbreaks
%K emerging outbreak
%K machine learning
%K precision public health
%K machine learning in public health
%K forecasting
%K digital data
%K mechanistic model
%K hybrid simulation
%K hybrid model
%K simulation
%D 2020
%7 17.8.2020
%9 Original Paper
%J J Med Internet Res
%G English
%X 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. 
%M 32730217
%R 10.2196/20285
%U http://www.jmir.org/2020/8/e20285/
%U https://doi.org/10.2196/20285
%U http://www.ncbi.nlm.nih.gov/pubmed/32730217