Robust optimal parameter estimation for the susceptible-unidentified infected-confirmed model

Chaeyoung Lee, Soobin Kwak, Sangkwon Kim, Youngjin Hwang, Yongho Choi, Junseok Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In this study, we consider a robust optimal parameter estimation method for the Susceptible-Unidentified infected-Confirmed (SUC) epidemic dynamics model. One of the problems in determining parameter values associated with epidemic mathematical models is that the optimal parameter values are very sensitive to the initial guess of parameter values. To resolve this problem, we fix the value of one parameter and solve an optimization problem of finding the other parameter values which best fit the confirmed population. The fixed parameter value can be obtained using data from epidemiological surveillance systems. To demonstrate the robustness and accuracy of the proposed method, we perform various numerical experiments with synthetic and real-world data from South Korea, the United States of America, India, and Brazil. The computational results confirm the potential practical application of the proposed method.

Original languageEnglish
Article number111556
JournalChaos, Solitons and Fractals
Publication statusPublished - 2021 Dec


  • COVID-19
  • Least-squares fitting
  • Optimal parameter estimation
  • SUC model

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematics(all)
  • Physics and Astronomy(all)
  • Applied Mathematics


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