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

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

doi:10.1016/j.chaos.2021.111556

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 journal › Article › peer-review

4 Citations (Scopus)

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.

Original language	English
Article number	111556
Journal	Chaos, Solitons and Fractals
Volume	153
DOIs	https://doi.org/10.1016/j.chaos.2021.111556
Publication status	Published - 2021 Dec

Bibliographical note

Funding Information:
The first author (C. Lee) was supported by the National Research Foundation (NRF), Korea , under project BK21 FOUR. The author (Y. Choi) was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF2020R1C1C1A0101153712 ). The corresponding author (J.S. Kim) was supported by Korea University Research Grant. The authors appreciate the reviewers for their constructive comments, which have improved the quality of this paper.

Publisher Copyright:
© 2021

Keywords

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

ASJC Scopus subject areas

Statistical and Nonlinear Physics
General Mathematics
General Physics and Astronomy
Applied Mathematics

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10.1016/j.chaos.2021.111556

Cite this

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title = "Robust optimal parameter estimation for the susceptible-unidentified infected-confirmed model",

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.",

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author = "Chaeyoung Lee and Soobin Kwak and Sangkwon Kim and Youngjin Hwang and Yongho Choi and Junseok Kim",

note = "Funding Information: The first author (C. Lee) was supported by the National Research Foundation (NRF), Korea , under project BK21 FOUR. The author (Y. Choi) was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF2020R1C1C1A0101153712 ). The corresponding author (J.S. Kim) was supported by Korea University Research Grant. The authors appreciate the reviewers for their constructive comments, which have improved the quality of this paper. Publisher Copyright: {\textcopyright} 2021",

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AU - Lee, Chaeyoung

AU - Kwak, Soobin

AU - Kim, Sangkwon

AU - Hwang, Youngjin

AU - Choi, Yongho

AU - Kim, Junseok

N1 - Funding Information: The first author (C. Lee) was supported by the National Research Foundation (NRF), Korea , under project BK21 FOUR. The author (Y. Choi) was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF2020R1C1C1A0101153712 ). The corresponding author (J.S. Kim) was supported by Korea University Research Grant. The authors appreciate the reviewers for their constructive comments, which have improved the quality of this paper. Publisher Copyright: © 2021

PY - 2021/12

Y1 - 2021/12

N2 - 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.

AB - 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.

KW - COVID-19

KW - Least-squares fitting

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KW - SUC model

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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