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

JMIR Theme Issue: COVID-19 Special Issue (2559) Machine Learning (2182) Infectious Diseases (non-STD/STI) (1739) Outbreak and Pandemic Preparedness and Management (1840) Statistical Methods for Surveillance and Population Health (193) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1544)

Published on in Vol 22, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19907, first published .
Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation

Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation

Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation

Authors of this article:

Se Young Jung1, 2 Author Orcid Image ;   Hyeontae Jo3 Author Orcid Image ;   Hwijae Son4 Author Orcid Image ;   Hyung Ju Hwang4 Author Orcid Image
Article Authors Cited by (16) Tweetations (2) Metrics

Journals

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  13. Jeong B, Lee Y, Han C. A simple yet effective approach for predicting disease spread using mathematically-inspired diffusion-informed neural networks. Scientific Reports 2025;15(1) View
  14. Luo X, Deng H, Yang J, Shen Y, Guo H, Sun Z, Liu M, Wei J, Zhao S. H2-MARL: Multi-agent reinforcement learning for Pareto optimality in hospital capacity strain and human mobility during epidemic. Expert Systems with Applications 2025;291:128432 View
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