Optimal Control in an SDC Mathematical Model for Diabetes Complications at a Hospital in Lamongan Regency

Abstract View: 6,
Download: 2

Authors

  • Alvina Wiliyanti Universitas Islam Darul `Ulum
  • Awawin Mustana Rohmah Universitas Islam Darul `Ulum
  • Muhammad Syaiful Pradana Universitas Islam Darul `Ulum

DOI:

https://doi.org/10.32665/james.v9i1.6401

Keywords:

Diabetes modeling, Optimal control theory, SDC compartment model, Pontryagin maximum principle, Numerical simulation

Abstract

Diabetes is a chronic disease whose prevalence continues to increase and has the potential to cause serious complications if not properly managed. This study aims to analyze the dynamics of diabetes progression and its complications, as well as to determine optimal control strategies using a mathematical model based on data from a hospital in Lamongan Regency. The model used is a compartmental SDC (Susceptible–Diabetes–Complication) model formulated as a system of ordinary differential equations with two time-dependent control variables. The optimal control is determined using Pontryagin’s Maximum Principle, while the numerical simulations are solved using the fourth-order Runge–Kutta (RK4) method. Model parameters are obtained from the literature and epidemiological data, and then calibrated to match the characteristics of real-world cases. The simulation results show that without control, the susceptible population decreases from approximately 1500 to about 200 individuals, while the complication population increases to around 1700 individuals. With the implementation of optimal control, the susceptible population increases to approximately 1250 individuals, the number of diabetic patients decreases to around 820 individuals, and the complication population is reduced to about 980 individuals. These results indicate that control strategies focused on diabetic patients are effective in suppressing disease progression and preventing complications, and contribute to the development of data-driven mathematical models for local healthcare policy planning.

References

Appadu, A. R., Lebelo, R. S., Gidey, H. H., & Das, S. (2024). Modelling and numerical simulations with differential equations in mathematical biology, medicine and the environment, volume II. Frontiers in Applied Mathematics and Statistics, 10, Article 1481224. https://doi.org/10.3389/fams.2024.1481224

Aye, P. O., Jayeola, D., Akintunlaji, I. D., & Adegbite, B. E. (2025). Application of optimal control strategies on incidence of medical complications in diabetic patients’ population. Earthline Journal of Mathematical Sciences, 15(2), 257–271.

Diveev, A., Konstantinov, S., Shmalko, E., & Dong, G. (2021). Machine learning control based on approximation of optimal trajectories. Mathematics, 9(3), 265. https://doi.org/10.3390/math9030265

Elmusharaf, K., Mairghani, M., Poix, S., Scaria, E., Phyo, P. P., Thu, W., Slama, S., Byström, M., El Berri, H., & Hammerich, A. (2025). A cost of illness study of the economic burden of diabetes in the Eastern Mediterranean Region. Eastern Mediterranean Health Journal, 31(7), 426–435.

Giacomelli, J., & Passalacqua, L. (2021). Unsustainability risk of bid bonds in public tenders. Mathematics, 9(19), 2385. https://doi.org/10.3390/math9192385

Grigorieva, E. (2021). Optimal control theory: Introduction to the special issue. Games, 12(1), 29. https://doi.org/10.3390/g12010029

Gutema, T. W., Wedajo, A. G., & Koya, P. R. (2024). A mathematical analysis of the corruption dynamics model with optimal control strategy. Frontiers in Applied Mathematics and Statistics, 10, Article 1387147. https://doi.org/10.3389/fams.2024.1387147

IDF. (2025). IDF diabetes atlas (11th ed.). International Diabetes Federation. https://idf.org/about-diabetes/diabetes-facts-figures/

K. A., D., T., A., & A. S., A. (2025). The mathematical modeling of diabetic population with the formulation of optimal control strategies. Asian Research Journal of Mathematics, 21(4), 103–125. https://doi.org/10.9734/arjom/2025/v21i4914

Kementerian Kesehatan Badan Penelitian dan Pengembangan Kesehatan. (2018). Hasil utama Riskesdas 2018.

Kouidere, A., Khajji, B., Balatif, O., & Rachik, M. (2021). A multi-age mathematical modeling of the dynamics of population diabetics with effect of lifestyle using optimal control. Journal of Applied Mathematics and Computing, 67(1–2), 375–403. https://doi.org/10.1007/s12190-020-01474-w

Kouidere, A., Labzai, A., Ferjouchia, H., Balatif, O., & Rachik, M. (2020). A new mathematical modeling with optimal control strategy for the dynamics of population of diabetics and its complications with effect of behavioral factors. Journal of Applied Mathematics, 2020, Article 1943410. https://doi.org/10.1155/2020/1943410

Leiva, H., & Valero, M. (2025). Pontryagin’s maximum principle for optimal control problems governed by integral equations with state and control constraints. Symmetry, 17(12), 2088. https://doi.org/10.3390/sym17122088

Mollah, S., & Biswas, S. (2023). Optimal control for the complication of Type 2 diabetes: The role of awareness programs by media and treatment. International Journal of Dynamics and Control, 11(2), 877–891. https://doi.org/10.1007/s40435-022-01013-4

Ndaïrou, F., & Torres, D. F. M. (2023). Pontryagin maximum principle for incommensurate fractional-orders optimal control problems. Mathematics, 11(19), 4218. https://doi.org/10.3390/math11194218

Pacôme, B., Francis, K. A., & Ghislain, P. K. (2025). Runge-Kutta’s 4th-order differential equation system (RK4) based on the SIR (susceptible-infected-removed) epidemiological model to predict the dynamic spread of cocoa swollen shoot virus. Far East Journal of Dynamical Systems, 38(2), 159–176. https://doi.org/10.17654/0972111825007

Raţiu, A., & Minculete, N. (2022). On several bounds for types of angular distances. Mathematics, 10(18), 3303. https://doi.org/10.3390/math10183303

Rohmah, A. M., & Rahmalia, D. (2021). SEIR model simulation on the spreading of Ebola virus between two regions. Journal of Physics: Conference Series, 1882(1), Article 012038. https://doi.org/10.1088/1742-6596/1882/1/012038

Rohmah, A. M., Rohmaniah, S. A., & Saputra, R. A. K. (2022). Model kontrol optimal SIR pada penyakit campak. UJMC (Unisda Journal of Mathematics and Computer Science), 8(1), 67–74. https://doi.org/10.52166/ujmc.v8i1.3226

Rohmah, A. M., Wiliyanti, A., Pradana, M. S., Rohmaniah, S. A., & Saputra, R. A. K. (2025). Analisis dan simulasi model matematika SIRC pada dinamika penyakit diabetes mellitus dengan komplikasi. UJMC (Unisda Journal of Mathematics and Computer Science), 11(2), 83–91.

Shao, Y., Meng, Y., & Xu, X. (2022). Turing instability and spatiotemporal pattern formation induced by nonlinear reaction cross-diffusion in a predator–prey system with Allee effect. Mathematics, 10(9), 1500. https://doi.org/10.3390/math10091500

Trihono. (2023). Survei kesehatan Indonesia 2023 (SKI). Kementerian Kesehatan Republik Indonesia.

Wang, D., Chen, Y., Wang, H., & Huang, M. (2020). Formulation of the non-parametric value at risk portfolio selection problem considering symmetry. Symmetry, 12(10), 1639. https://doi.org/10.3390/sym12101639

Weston, J., Tolić, D., & Palunko, I. (2024). Application of Hamilton–Jacobi–Bellman equation/Pontryagin’s principle for constrained optimal control. Journal of Optimization Theory and Applications, 200(2), 437–462. https://doi.org/10.1007/s10957-023-02364-4

WHO. (2023). WHO discussion group for people living with diabetes. https://iris.who.int/bitstream/handle/10665/374810/9789240081451-eng.pdf?sequence=1

You, W., & Zhang, F. (2025). Pontryagin’s principle-based algorithms for optimal control problems of parabolic equations. Mathematics, 13(7), 1143. https://doi.org/10.3390/math13071143

Downloads

Published

2026-04-30
Abstract View: 6, PDF Download: 2