Optimal Control of COVID-19 Model with Partial Comorbid Subpopulations and Two Isolation Treatments in Indonesia

Abstract

We applied sensitivity analysis and optimum control to the COVID-19 model in this research. In addition, the basic reproduction number calculated as 1.57 indicates that this illness is widespread across Indonesia. The most important factor in this model is the contact rate with infected people, with or without comorbidity. Optimal control will minimize the number of infected populations without and with comorbidity, and costs. Numerical experiments will be carried out to describe and compare the graphical models of the spread of COVID-19 with and without controls. From the numerical results and cost-effectiveness analysis on the optimal control problem, it is found that applying a combination of controls can give the best results compared to a single control

Two isolation treatments on the COVID-19 model and optimal control with public education

“…COVID-19 spread model [11] utilizing the four subpopulations of the SEIR model, namely susceptible (S), exposed (E), infected (I), and recovered (R). Next there is research [12,13] which adds subpopulations for quarantine (Q) and isolation (H), dividing seven subpopulations of the population: S, E, I, A, Q, H, and R. Research on COVID-19 [14] also added isolation (H) and quarantine (Q), so the model is built six subpopulations: S, E, I, Q, H, and R. Furthermore, there are many more studies that discuss the mathematical modeling of COVID-19 such as [15][16][17][18][19][20]. Reducing COVID-19's spread requires regulation (control) of the developed mathematical model.…”