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

JMIR Theme Issue: COVID-19 Special Issue (2087) Machine Learning (1004) Infectious Diseases (non-STD/STI) (1223) Outbreak and Pandemic Preparedness and Management (1453) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1330) Surveillance Systems (359) Innovative Methods in Public Health and Surveillance (241) Artificial Intelligence (665) Hygiene and Infection Prevention (240) Statistical Methods for Surveillance and Population Health (112) Epidemiology (94)

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 Yeung 1, 2 Author Orcid Image ;   Francois Roewer-Despres 1, 2 Author Orcid Image ;   Laura Rosella 3 Author Orcid Image ;   Frank Rudzicz 1, 2, 4 Author Orcid Image
Article Authors Cited by (11) 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

Books/Policy Documents

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