TY - JOUR
T1 - Robust optimal parameter estimation for the susceptible-unidentified infected-confirmed model
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
KW - Optimal parameter estimation
KW - SUC model
UR - http://www.scopus.com/inward/record.url?scp=85118505349&partnerID=8YFLogxK
U2 - 10.1016/j.chaos.2021.111556
DO - 10.1016/j.chaos.2021.111556
M3 - Article
AN - SCOPUS:85118505349
SN - 0960-0779
VL - 153
JO - Chaos, Solitons and Fractals
JF - Chaos, Solitons and Fractals
M1 - 111556
ER -