Robust optimal parameter estimation for the susceptible-unidentified infecte d-confirme d 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 val-ues associated with epidemic mathematical models is that the optimal parameter values are very sensi-tive 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 sys-tems. To demonstrate the robustness and accuracy of the proposed method, we perform various numeri-cal 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. (c) 2021 Elsevier Ltd. All rights reserved.