Dewi Suhika, Roberd Saragih, Dewi Handayani, Mochamad Apri
The spread of COVID-19 poses a major challenge due to uncertain parameters, stochastic effects, and complex regional dynamics. Traditional control strategies often fail to handle mismatched disturbances and time-varying uncertainties that arise in real-world epidemic systems. To address this issue, we propose an adaptive sliding mode control (ASMC) framework designed for a stochastic epidemic model with mobility. The control design incorporates integraltype sliding surfaces and adaptive switching gains to ensure robustness against both deterministic mismatched uncertainty and stochastic fluctuations. An Extended Kalman Filter (EKF) is employed for real-time estimation of unknown model parameters based on partial observational data. The ASMC method is applied to regulate vaccination and isolation efforts in Jakarta and West Java. Simulation results indicate that the proposed control scheme significantly reduces infection levels In Jakarta, the total infections in the regular and mutant compartments are reduced by 72.90% and 62.22%, respectively. In West Java, the reductions are 71.42% and 62.89%. These results demonstrate the effectiveness and resilience of the proposed method in epidemic control under uncertainty. © 2025 ICROS.
Institut Teknologi Bandung, Faculty of Mathematics and Natural Sciences, Departement of Mathematics, Jawa Barat, Bandung, 40312, Indonesia; Institut Teknologi Sumatera, Mathematics Study Program, Faculty of Sciences, Lampung, Indonesia