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

JMIR Theme Issue: COVID-19 Special Issue (2461) New Methods (963) GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (113) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050) Statistical Methods for Surveillance and Population Health (158) Surveillance Systems (466) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1520)

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 (40) Tweetations (7) Metrics

Journals

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  17. Cao Z, Qiu Z, Tang F, Liang S, Wang Y, Long H, Chen C, Zhang B, Zhang C, Wang Y, Tang K, Tang J, Chen J, Yang C, Xu Y, Yang Y, Xiao S, Tian D, Jiang G, Du X. Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: A systematic analysis based on an interpretable machine learning framework. Transboundary and Emerging Diseases 2022;69(5) View
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  20. Stolerman L, Clemente L, Poirier C, Parag K, Majumder A, Masyn S, Resch B, Santillana M. Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States. Science Advances 2023;9(3) View
  21. Zhu S, Bukharin A, Xie L, Santillana M, Yang S, Xie Y. High-Resolution Spatio-Temporal Model for County-Level COVID-19 Activity in the U.S.. ACM Transactions on Management Information Systems 2021;12(4):1 View
  22. Ma S, Sun Y, Yang S. Using Internet Search Data to Forecast COVID-19 Trends: A Systematic Review. Analytics 2022;1(2):210 View
  23. Dasgupta A, Bakshi A, Mukherjee S, Das K, Talukdar S, Chatterjee P, Mondal S, Das P, Ghosh S, Som A, Roy P, Kundu R, Sarkar A, Biswas A, Paul K, Basak S, Manna K, Saha C, Mukhopadhyay S, Bhattacharyya N, De R. Epidemiological challenges in pandemic coronavirus disease (COVID‐19): Role of artificial intelligence. WIREs Data Mining and Knowledge Discovery 2022;12(4) View
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  27. Raza A, Baleanu D, Cheema T, Fadhal E, Ibrahim R, Abdelli N. Artificial intelligence computing analysis of fractional order COVID-19 epidemic model. AIP Advances 2023;13(8) View
  28. Agarwal D, Patnaik N, Harinarayanan A, Senthilkumar S, Krishnamurthy B, Srinivasan K. Forecasting Geo Location of COVID-19 Herd. Pertanika Journal of Science and Technology 2023;31(4) View
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Books/Policy Documents

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