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

JMIR Theme Issue: COVID-19 Special Issue (2554) Machine Learning (2076) Infectious Diseases (non-STD/STI) (1726) Outbreak and Pandemic Preparedness and Management (1826) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1542) Surveillance Systems (514) Innovative Methods in Public Health and Surveillance (383) Artificial Intelligence (2129) Hygiene and Infection Prevention (374) Statistical Methods for Surveillance and Population Health (188) Epidemiology (196)

Published on in Vol 23, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26628, first published .
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

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

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

Authors of this article:

Arnold YS Yeung1, 2 Author Orcid Image ;   Francois Roewer-Despres1, 2 Author Orcid Image ;   Laura Rosella3 Author Orcid Image ;   Frank Rudzicz1, 2, 4 Author Orcid Image
Article Authors Cited by (23) Tweetations (7) Metrics

Journals

  1. Yu C, Chang S, Chang T, Wu J, Lin Y, Chien H, Chen R. A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study. Journal of Medical Internet Research 2021;23(5):e27806 View
  2. Nguyen H, Turk P, McWilliams A. Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence. JMIR Public Health and Surveillance 2021;7(8):e28195 View
  3. Eum N, Kim S. The Role of Information and Communications Technology Policies and Infrastructure in Curbing the Spread of the Novel Coronavirus: Cross-country Comparative Study. JMIR Public Health and Surveillance 2022;8(1):e31066 View
  4. Vega R, Flores L, Greiner R. SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting. Forecasting 2022;4(1):72 View
  5. Winston L, McCann M, Onofrei G. Exploring Socioeconomic Status as a Global Determinant of COVID-19 Prevalence, Using Exploratory Data Analytic and Supervised Machine Learning Techniques: Algorithm Development and Validation Study. JMIR Formative Research 2022;6(9):e35114 View
  6. Luo W, Liu Z, Zhou Y, Zhao Y, Li Y, Masrur A, Yu M. Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method. JMIR Public Health and Surveillance 2022;8(8):e35840 View
  7. Botz J, Wang D, Lambert N, Wagner N, Génin M, Thommes E, Madan S, Coudeville L, Fröhlich H. Modeling approaches for early warning and monitoring of pandemic situations as well as decision support. Frontiers in Public Health 2022;10 View
  8. Ahmadi I, Habel J, Jia M, Lee N, Wei S. Consumer Stockpiling Across Cultures During the COVID-19 Pandemic. Journal of International Marketing 2022;30(2):28 View
  9. Rajkumar R, Arafat S. Model Driven Causal Factors of Panic Buying and Their Implications for Prevention: A Systematic Review. Psychiatry International 2021;2(3):325 View
  10. Núñez M, Barreiro N, Barrio R, Rackauckas C. Forecasting virus outbreaks with social media data via neural ordinary differential equations. Scientific Reports 2023;13(1) View
  11. Tran V, Ivanov V, Kim J. Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes. Advances in Water Resources 2023;182:104569 View
  12. Tu K, Chen S, Mesler R. Policy stringency and the spread of COVID-19: The moderating role of culture and its implications on first responses. Health Policy 2023;137:104896 View
  13. Gökler S. Prediction of Covid-19 confirmed cases and deaths using hybrid support vector machine-Taguchi method. Computers & Industrial Engineering 2024;191:110103 View
  14. Hu J, Zhou Y, Li H, Liang P. An interval forecast model for infectious diseases using fuzzy information granulation and spatial-temporal graph neural network. Journal of Intelligent & Fuzzy Systems 2024;47(1-2):83 View
  15. Kamlesh K. A Review Study on Outbreak Prediction of Covid19 By using Machine Learning. International Journal of Inventive Engineering and Sciences 2024;11(6):1 View
  16. Cheng C, Aruchunan E, Noor Aziz M. Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach. Scientific Reports 2025;15(1) View
  17. Duarte M, Ferreira da Silva C, Moro S. Machine learning models to predict the COVID-19 reproduction rate: combining non-pharmaceutical interventions with sociodemographic and cultural characteristics. Informatics for Health and Social Care 2025;50(2):81 View
  18. Cheng Y, Cheng R, Xu T, Tan X, Bai Y. Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review. Bioengineering 2025;12(5):514 View
  19. Liu C, Huang J, Chen S, He J, Du S, Yin N, Zhang C, Wang D. RF-KDE-QSR Model for Estimating the Scale of Epidemics. IEEE Transactions on Computational Social Systems 2025;12(3):1193 View
  20. Cheon S, Ahn I. Survice-BERT: A BERT Model for Named Entity Recognition in Infectious Disease Surveillance Reports. Journal of Bacteriology and Virology 2025;55(3):235 View

Books/Policy Documents

  1. Zehtabian S, Khodadadeh S, Turgut D, Bölöni L. Multimodal AI in Healthcare. View

Conference Proceedings

  1. Nastiti F, Musa S, Yafi E, Chauhan R. 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS). Systematic Literature Review: Machine Learning Prediction Model for Covid-19 Spreading View
  2. Maniamfu P, Kameyama K. 2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA). LSTM-based Forecasting using Policy Stringency and Time-varying Parameters of the SIR Model for COVID-19 View