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Long-Time Analysis of a Time-Dependent SUC Epidemic Model for the COVID-19 Pandemic.

Youngjin HwangSoobin KwakJunseok Kim
Published in: Journal of healthcare engineering (2021)
In this study, we propose a time-dependent susceptible-unidentified infected-confirmed (tSUC) epidemic mathematical model for the COVID-19 pandemic, which has a time-dependent transmission parameter. Using the tSUC model with real confirmed data, we can estimate the number of unidentified infected cases. We can perform a long-time epidemic analysis from the beginning to the current pandemic of COVID-19 using the time-dependent parameter. To verify the performance of the proposed model, we present several numerical experiments. The computational test results confirm the usefulness of the proposed model in the analysis of the COVID-19 pandemic.
Keyphrases
  • coronavirus disease
  • sars cov
  • machine learning
  • deep learning
  • artificial intelligence