TY  - JOUR
AU  - Liu, Dianbo
AU  - Clemente, Leonardo
AU  - Poirier, Canelle
AU  - Ding, Xiyu
AU  - Chinazzi, Matteo
AU  - Davis, Jessica
AU  - Vespignani, Alessandro
AU  - Santillana, Mauricio
PY  - 2020
DA  - 2020/8/17
TI  - Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models
JO  - J Med Internet Res
SP  - e20285
VL  - 22
IS  - 8
KW  - COVID-19
KW  - coronavirus
KW  - digital epidemiology
KW  - modeling
KW  - modeling disease outbreaks
KW  - emerging outbreak
KW  - machine learning
KW  - precision public health
KW  - machine learning in public health
KW  - forecasting
KW  - digital data
KW  - mechanistic model
KW  - hybrid simulation
KW  - hybrid model
KW  - simulation
AB  - 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. 
SN  - 1438-8871
UR  - http://www.jmir.org/2020/8/e20285/
UR  - https://doi.org/10.2196/20285
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32730217
DO  - 10.2196/20285
ID  - info:doi/10.2196/20285
ER  -