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

JMIR Theme Issue: COVID-19 Special Issue (2452) Machine Learning (1465) Infectious Diseases (non-STD/STI) (1575) Outbreak and Pandemic Preparedness and Management (1727) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Surveillance Systems (462) Innovative Methods in Public Health and Surveillance (332) Artificial Intelligence (1371) Hygiene and Infection Prevention (317) Statistical Methods for Surveillance and Population Health (156) Epidemiology (159)

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 (18) Tweetations (7) Metrics

Arnold YS Yeung   1, 2 , MSc ;   Francois Roewer-Despres   1, 2 , MSc ;   Laura Rosella   3 , MHSc, PhD ;   Frank Rudzicz   1, 2, 4 , MEng, PhD

1 Department of Computer Science, University of Toronto, Toronto, ON, Canada

2 Vector Institute for Artificial Intelligence, Toronto, ON, Canada

3 Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

4 Unity Health Toronto, Toronto, ON, Canada

Corresponding Author: