Extended Kalman Filter with Optimal Control On Dengue Model - Aulia, Hanna Hilyati; Ayu, Regina Wahyudyah Sonata (Indonesian Journal of Applied Mathematics, 2024)
In real life, due to various measurement limitations, not all variables in the dengue fever epidemic model can be measured. Therefore, a tool is needed to estimate unmeasured variables with known related variables. One method for estimating variables in nonlinear systems is the extended Kalman filter (EKF). Next, using these estimated results, optimal control will be sought in the form of vaccination to reduce the number of infections. From the simulation results, it can be concluded that state estimation with EKF for the dengue fever model is good enough to estimate each variable with measurements that are disturbed by a random variable within the selected range of disturbance covariance. Then, it was found that the smaller the standard deviation of the disturbance, the smaller the optimal control required at the start. Thus, the greater the disruption, the greater the costs spent.