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 (1728) Statistical Methods for Surveillance and Population Health (156) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517)

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 (10) Tweetations (2) Metrics

Journals

  1. Laudanski K, Shea G, DiMeglio M, Restrepo M, Solomon C. What Can COVID-19 Teach Us about Using AI in Pandemics?. Healthcare 2020;8(4):527 View
  2. Shoaib M, Raja M, Sabir M, Bukhari A, Alrabaiah H, Shah Z, Kumam P, Islam S. A stochastic numerical analysis based on hybrid NAR-RBFs networks nonlinear SITR model for novel COVID-19 dynamics. Computer Methods and Programs in Biomedicine 2021;202:105973 View
  3. Yang H, Xue Y, Pan Y, Liu Q, Hu G. Time fused coefficient SIR model with application to COVID-19 epidemic in the United States. Journal of Applied Statistics 2023;50(11-12):2373 View
  4. Lobato F, Libotte G, Platt G. Mathematical modelling of the second wave of COVID-19 infections using deterministic and stochastic SIDR models. Nonlinear Dynamics 2021;106(2):1359 View
  5. Sivaraman N, Gaur M, Baijal S, Muthiah S, Sheth A. Exo-SIR: an epidemiological model to analyze the impact of exogenous spread of infection. International Journal of Data Science and Analytics 2022 View
  6. Cho T. ST-DEVS: A Methodology Using Time-Dependent-Variable-Based Spatiotemporal Computation. Symmetry 2022;14(5):912 View
  7. Newcomb K, Bilal S, Michael E, Ndeffo Mbah M. Combining predictive models with future change scenarios can produce credible forecasts of COVID-19 futures. PLOS ONE 2022;17(11):e0277521 View
  8. Lee H, Kim S, Jeong M, Choi E, Ahn H, Lee J. Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature. Yonsei Medical Journal 2023;64(1):1 View
  9. Thakkar K, Spinardi J, Yang J, Kyaw M, Ozbilgili E, Mendoza C, Oh H. Impact of vaccination and non-pharmacological interventions on COVID-19: a review of simulation modeling studies in Asia. Frontiers in Public Health 2023;11 View
  10. Sivaraman N, Baijal S, Muthiah S. On the usage of epidemiological models for information diffusion over twitter. Social Network Analysis and Mining 2023;13(1) View