Extended Kalman Filter with Optimal Control On Dengue Model

Aulia, Hanna Hilyati
Ayu, Regina Wahyudyah Sonata

Indonesian Journal of Applied Mathematics
Vol. 4 No. 2 (2024): Indonesian Journal of Applied Mathematics Vol. 4 No. 2 October Chapter

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.