Sliding mode control with stochastic modeling and mobility interaction for managing epidemic spread in high-population regions.

ABSTRACT: Managing infectious disease transmission in high-mobility regions is a critical challenge due to dynamic population interactions and elevated transmission risks. This study develops a stochastic epidemiological model to simulate disease spread between two densely populated provinces in Indonesia, Jakarta and West Java. A robust sliding mode control (SMC) framework is proposed and integrated with an Extended Kalman Filter (EKF) to estimate key epidemiological parameters in real time using limited observable data. The proposed framework functions as a theoretical and simulation-based tool to evaluate the potential effects of vaccination and isolation strategies. Although full-state variables are not directly measurable in practice, the EKF allows for the estimation of unobservable parameters

SUBMITTER: Suhika D 

PROVIDER: S-EPMC12221646 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature