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

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

Abstract

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.

Suggested Citation

  • Lee, Chaeyoung & Kwak, Soobin & Kim, Sangkwon & Hwang, Youngjin & Choi, Yongho & Kim, Junseok, 2021. "Robust optimal parameter estimation for the susceptible-unidentified infected-confirmed model," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    • Handle: RePEc:eee:chsofr:v:153:y:2021:i:p1:s0960077921009103
    DOI: 10.1016/j.chaos.2021.111556
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    References listed on IDEAS

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    1. Memon, Zaibunnisa & Qureshi, Sania & Memon, Bisharat Rasool, 2021. "Assessing the role of quarantine and isolation as control strategies for COVID-19 outbreak: A case study," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
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    4. Lee, Chaeyoung & Li, Yibao & Kim, Junseok, 2020. "The susceptible-unidentified infected-confirmed (SUC) epidemic model for estimating unidentified infected population for COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    5. Gabriel G Katul & Assaad Mrad & Sara Bonetti & Gabriele Manoli & Anthony J Parolari, 2020. "Global convergence of COVID-19 basic reproduction number and estimation from early-time SIR dynamics," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-22, September.
    6. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
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