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

Journals

  1. Hanage W, Testa C, Chen J, Davis L, Pechter E, Seminario P, Santillana M, Krieger N. COVID-19: US federal accountability for entry, spread, and inequities—lessons for the future. European Journal of Epidemiology 2020;35(11):995 View
  2. Lynch C, Gore R. Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study. Journal of Medical Internet Research 2021;23(3):e24925 View
  3. Martínez-Rodríguez D, Gonzalez-Parra G, Villanueva R. Analysis of Key Factors of a SARS-CoV-2 Vaccination Program: A Mathematical Modeling Approach. Epidemiologia 2021;2(2):140 View
  4. Yeung A, Roewer-Despres F, Rosella L, Rudzicz F. Machine Learning–Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation. Journal of Medical Internet Research 2021;23(4):e26628 View
  5. Lei S, Yang R, Huang C. Emergent neologism: A study of an emerging meaning with competing forms based on the first six months of COVID-19. Lingua 2021;258:103095 View
  6. Brüggenjürgen B, Stricker H, Krist L, Ortiz M, Reinhold T, Roll S, Rotter G, Weikert B, Wiese-Posselt M, Willich S. Impact of public health interventions to curb SARS-CoV-2 spread assessed by an evidence-educated Delphi panel and tailored SEIR model. Journal of Public Health 2023;31(4):539 View
  7. Rotter D, Doebler P, Schmitz F. Interests, Motives, and Psychological Burdens in Times of Crisis and Lockdown: Google Trends Analysis to Inform Policy Makers. Journal of Medical Internet Research 2021;23(6):e26385 View
  8. Peng Y, Li C, Rong Y, Pang C, Chen X, Chen H. Real-time Prediction of the Daily Incidence of COVID-19 in 215 Countries and Territories Using Machine Learning: Model Development and Validation. Journal of Medical Internet Research 2021;23(6):e24285 View
  9. Li J, Sia C, Chen Z, Huang W. Enhancing Influenza Epidemics Forecasting Accuracy in China with Both Official and Unofficial Online News Articles, 2019–2020. International Journal of Environmental Research and Public Health 2021;18(12):6591 View
  10. Miliou I, Xiong X, Rinzivillo S, Zhang Q, Rossetti G, Giannotti F, Pedreschi D, Vespignani A, Viboud C. Predicting seasonal influenza using supermarket retail records. PLOS Computational Biology 2021;17(7):e1009087 View
  11. Sunthornwat R, Areepong Y. Reproduction number, discrete forecasting model, and chaos analytics for Coronavirus Disease 2019 outbreak in India, Bangladesh, and Myanmar. Biostatistics & Epidemiology 2022;6(1):31 View
  12. Ferdousi T, Cohnstaedt L, Scoglio C. A Windowed Correlation-Based Feature Selection Method to Improve Time Series Prediction of Dengue Fever Cases. IEEE Access 2021;9:141210 View
  13. Gunasekeran D, Chew A, Chandrasekar E, Rajendram P, Kandarpa V, Rajendram M, Chia A, Smith H, Leong C. The Impact and Applications of Social Media Platforms for Public Health Responses Before and During the COVID-19 Pandemic: Systematic Literature Review. Journal of Medical Internet Research 2022;24(4):e33680 View
  14. Li J, Huang W, Sia C, Chen Z, Wu T, Wang Q. Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data, Online News Articles, and Search Queries. JMIR Public Health and Surveillance 2022;8(6):e35266 View
  15. Zakharov V, Balykina Y, Ilin I, Tick A. Forecasting a New Type of Virus Spread: A Case Study of COVID-19 with Stochastic Parameters. Mathematics 2022;10(20):3725 View
  16. González-Parra G, Díaz-Rodríguez M, Arenas A. Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela. Spatial and Spatio-temporal Epidemiology 2022;43:100532 View
  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
  18. Ma S, Yang S. COVID-19 forecasts using Internet search information in the United States. Scientific Reports 2022;12(1) View
  19. Galetsi P, Katsaliaki K, Kumar S. The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19. Social Science & Medicine 2022;301:114973 View
  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
  24. Kamalov F, Rajab K, Cherukuri A, Elnagar A, Safaraliev M. Deep learning for Covid-19 forecasting: State-of-the-art review.. Neurocomputing 2022;511:142 View
  25. Drazen J, Kohane I, Leong T, Brownstein J, Rader B, Astley C, Tian H. Advances in Artificial Intelligence for Infectious-Disease Surveillance. New England Journal of Medicine 2023;388(17):1597 View
  26. Díaz-Lozano M, Guijo-Rubio D, Gutiérrez P, Hervás-Martínez C. Cluster analysis and forecasting of viruses incidence growth curves: Application to SARS-CoV-2. Expert Systems with Applications 2023;225:120103 View
  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
  29. Zhu S, Bukharin A, Xie L, Yamin K, Yang S, Keskinocak P, Xie Y. Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data. IEEE Journal of Selected Topics in Signal Processing 2022;16(2):250 View
  30. Zhan X, Zhang K, Ge L, Huang J, Zhang Z, Wei L, Sun G, Liu C, Zhang Z. Exploring the Effect of Social Media and Spatial Characteristics During the COVID-19 Pandemic in China. IEEE Transactions on Network Science and Engineering 2023;10(1):553 View

Books/Policy Documents

  1. Sanchez-Daza A, Medina-Ortiz D, Olivera-Nappa A, Contreras S. Modeling, Control and Drug Development for COVID-19 Outbreak Prevention. View
  2. Aquino Y, Shih P, Bosward R. Reference Module in Biomedical Sciences. View
  3. Stylianides C, Malialis K, Kolios P. Artificial Neural Networks and Machine Learning – ICANN 2023. View