This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2388) New Methods (901) GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (92) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1010) Statistical Methods for Surveillance and Population Health (146) Surveillance Systems (427) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1481)

Published on in Vol 22, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20285, first published .
Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

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

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

Authors of this article:

Dianbo Liu1, 2 Author Orcid Image ;   Leonardo Clemente1, 2, 3 Author Orcid Image ;   Canelle Poirier1, 2 Author Orcid Image ;   Xiyu Ding1, 4 Author Orcid Image ;   Matteo Chinazzi5 Author Orcid Image ;   Jessica Davis5 Author Orcid Image ;   Alessandro Vespignani5, 6 Author Orcid Image ;   Mauricio Santillana1, 2, 4 Author Orcid Image
Article Authors Cited by (36) Tweetations (7) Metrics

Dianbo Liu   1, 2 * , PhD ;   Leonardo Clemente   1, 2, 3 * , MSc ;   Canelle Poirier   1, 2 * , PhD ;   Xiyu Ding   1, 4 , MSc ;   Matteo Chinazzi   5 , PhD ;   Jessica Davis   5 , BSc ;   Alessandro Vespignani   5, 6 , PhD ;   Mauricio Santillana   1, 2, 4 , PhD

1 Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, United States

2 Department of Pediatrics, Harvard Medical School, Boston, MA, United States

3 Tecnologico de Monterrey, Monterrey, Mexico

4 Harvard TH Chan School of Public Health, Boston, MA, United States

5 Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, United States

6 ISI Foundation, Turin, Italy

*these authors contributed equally

Corresponding Author:

  • Mauricio Santillana, PhD
  • Computational Health Informatics Program
  • Boston Children’s Hospital
  • 300 Longwood Avenue
  • Landmark 5th Floor East
  • Boston, MA, 02215
  • United States
  • Phone: 1 (617) 919-1795
  • Email: msantill@g.harvard.edu