IMPACT OF CORRUPT IMMIGRANT ON THE DYNAMICS OF CORRUPTION IN HUMAN POPULATION: A MATHEMATICAL MODELLING APPROACH

  • Hammed Olatunbosun Ibrahim Ladoke Akintola University of Technology
  • Okunloye Ayinde Adepoju Ladoke Akintola University of Technology
  • Okunloye Ayinde Adepoju Ladoke Akintola University of Technology
  • John Abayomi Olanrewaju Ladoke Akintola University of Technology
  • John Oluwabori Ayanwale Ladoke Akintola University of Technology
  • Janet Ifeade Awojugbagbe Ladoke Akintola University of Technology
  • Emmanuel Adeolu Adejumo Ladoke Akintola University of Technology
  • Wasiu Omotayo Salahu Ladoke Akintola University of Technology
Keywords: Corruption, Mathematical modelling, Immigrant, Reproduction number, Stability, Sensitivity analysis

Abstract

This study formulated and analyzed a nonlinear deterministic compartmental model of corruption dynamics that explicitly incorporates the influence of corrupt immigrants on a naive population. The population is divided into six compartments: susceptible, exposed, corrupt, prosecuted, jailed, and honest individuals. The model is shown to be well posed, with a positively invariant and attracting feasible region and non-negative solutions for all time. The corruption reproduction number, , is derived using the next-generation matrix approach. The corruption-free equilibrium is proved locally and globally asymptotically stable when , while the corruption-present equilibrium is proved globally asymptotically stable when  using quadratic Lyapunov function. Sensitivity analysis identifies the recruitment, influence, prosecution, and reform rates as the parameters to which  is most responsive, with reform exerting the strongest negative effect. Numerical simulations confirm the analytical results and show that the long-run endemic level is independent of the initial conditions. Although the sensitivity index of the corrupt-immigrant parameter is small (+0.0437), the simulations show that corrupt immigrants act as a persistent external source of corruption; neglecting this inflow would understate persistence corruption. The results support a combined strategy weighted toward reform, since rehabilitation, civic education, and moral recovery reduce both peak and endemic corruption and ease the burden on the prison system.

References

[1] Grass D, Caulkins JP, Feichtinger G, Tragler G, Behrens DA. Optimal Control of Nonlinear Processes. Berlin, Heidelberg: Springer (2008). 2. World Bank. Helping Countries Combat Corruption: The Role of the World
[2] Akanni, J. O., Akinpelu, F. O., Olaniyi, S., Oladipo, A. T., & Ogunsola, A. W. (2020). Modelling financial crime population dynamics: optimal control and cost-effectiveness analysis: JO Akanni et al. International Journal of Dynamics and Control, 8(2), 531-544. l https://doi.org/10.1007/s40435-019-00572-3
[3] Gottschalk P (2010) Categories of financial crime. J Financ Crime 17(4):441–4587. https://doi.org/10.1108/13590791011082797
[4]. Jung J, Lee J (2017) Contemporary financial crime. J Public Adm Gov 7(2):88–97. https://doi.org/10.5296/jpag.v7i2.11219
[5] Pickett KS, Pickett JM (2002) Financial crime investigation and control. Wiley, New York
[6] Danford, O., Kimathi, M., & Mirau, S. (2020). Mathematical modelling and analysis of corruption dynamics with control measures in Tanzania.
[7] A. Cuervo-Cazurra, “Corruption in international business,” Journal of World Business, vol. 51, no. 1, pp. 35–49, 2016.
[8] P. Milner-Smyth, “Consequences of corruption,” HR Future, vol. 2017, no. Jul 2017, pp. 30-31, 2017.
[9] Gutema TW, Wedajo AG and Koya PR (2024) A mathematical analysis of the corruption dynamics model with optimal control strategy. Front. Appl. Math. Stat. 10:1387147. doi: 10.3389/fams.2024.1387147
[10] Alemneh, H. T. (2020). Mathematical modeling, analysis, and optimal control of corruption dynamics. Journal of Applied Mathematics, 2020(1), 5109841. https://doi.org/10.1155/2020/5109841
[11] Fantaye, A. K., & Birhanu, Z. K. (2022). Mathematical model and analysis of corruption dynamics with optimal control. Journal of Applied Mathematics, 2022(1), 8073877. https://doi.org/10.1155/2022/8073877
[12] Jose, S.A., Raja, R., Alzabut, J. et al. Mathematical modeling on transmission and optimal control strategies of corruption dynamics. Nonlinear Dyn 109, 3169–3187 (2022). https://doi.org/10.1007/s11071-022-07581-6
[13] Assenga, N. R., Nyerere, N., & Matofali, A. X. (2026). Mathematical modeling of corruption dynamics: Exploring the role of rehabilitation of corrupt individuals. Scientific African, e03326. https://doi.org/10.1016/j.sciaf.2026.e03326
[14] Nathan, O. M., & Jackob, K. O. (2019). Stability analysis in a mathematical model of corruption in Kenya. DOI: 10.9734/ARJOM/2019/v15i430164
[15]. Isa, R. S., Emmanuel, S., Danat, N. T., & Tsok, S. H. (2023). Mathematical Modeling of Corruption Dynamics: Examining the Reintegration of Formerly Corrupt Individuals. FUDMA Journal of Sciences, 7(4), 1-13. https://doi.org/10.33003/fjs-2023-0704-1888
[16] Idisi, O. I., Akande, K. B., Suleiman, L. A., Samson, O. O., & Adeniji, A. (2026). Mathematical modeling of crime and corruption dynamics: a cross-national analysis of their interdependencies. Journal of Computational Social Science, 9(2), 26. https://doi.org/10.1007/s42001-025-00458-9
[17] Ademosu J.A., Olaniyi S, Abimbade S.F., Chuma F.M, Ogbonna R.C., Lebelo RS, Okosun K.O. Modelling population dynamics of substance abuse in the presence of addicted immigrant with real data of rehabilitation cases. J Appl Math. 2025;2025(1):2241780.
[18] Wangari, I. M., Olaniyi, S., Lebelo, R. S., and Okosun, K. O. (2023). Transmission of COVID-19 in the presence of single-dose and double-dose vaccines with hesitancy: mathematical modeling and optimal control analysis. Frontiers in Applied Mathematics and Statistics, 9, 1292443. https//doi.org/10.3389/famus.2023.1292443.
[19] Adepoju O. A., Ibrahim H. O., Awojugbagbe J. I., Olanrewaju S. O., Salahu W. O., Adejumo A. E. (2023). Moonkeypox transmission dynamics in a population on men who have sex with men: A mathematical modelling approach.
[20] Driessche, P. V. and Watmough, J. (2002). Reproduction Numbers and Sub-Threshold Endemic Equilibria for Computational Models of Disease Transmission. Mathematical Biosciences, 180, 2948. https://doi.org/10.1016/S0025-5564(02)00108-6
[21] Diekmann, O., Heesterbeek, J.A.P and Metz, J.A.J., (1990). On the definition and the computation of the basic reproduction ratio in models for infectious diseases in heterogenous populations. Journal of Mathematics Biology, 28,365-382. https://doi.org/10.1007/BF00178324
[22] Adepoju, O. A., Olatunji, T. M., Olanrewaju, S. O., & Ibrahim, H. O. (2024). Stability analysis of HIV/AIDS epidemic model with vertical transmission. https://doi.org/10.37418/amsj.13.3.10
[23] Castillo-Chavez, C., Blower, S., van den Driessche, P., Kirschner, D., & Yakubu, A. A. (Eds.). (2002). Mathematical approaches for emerging and reemerging infectious diseases: models, methods, and theory (Vol. 126). Springer Science & Business Media. https://doi.org/10.1007/978-1-4757-3667-013
[24] Adepoju, O. A., Ibrahim, H., & Salahu, W. O. (2024). Mathematical Assessment and Stability Analysis of HIV/AIDS epidemic model with vertical transmission and treatment. Transpublika International Research in Exact Sciences, 3(4), 1-20.
[25] Olanrewaju, O. A., & Abimbade, S. F. (2026). Optimal Control Analysis of Vertically Transmitted Infectious Diseases With Saturated Incidence and Nonlinear Treatment. Optimal Control Applications and Methods, 1–18 1 https://doi.org/10.1002/oca.7011
[26] Oluyo, T. O., Olanrewaju, S. O., Akinsola, V. O., Adeyemi, M. O., Oladejo, J. K., Odebiyi, O. A., ... and Taiwo, A. J. (2025). Mathematical modelling and analysis of lassa fever dynamics with environmental transmission and reinfection. Franklin Open, 11, 100254. https://doi.org/10.1016/j.fraope.2025.100254
[27]. J. P. LaSalle, “The Stability of Dynamical Systems,” in Regional Con ference Series in Applied Mathematics (SIAM, 1976)
[28] N. Chitnis, J.M. Hyman, J.M. Cushing, Determining important parameters in the spread of malaria through the sensitivity analysis of mathematical model, Bull. Math. Biol. 70 (2008) 1272–1296. http://dx.doi.org/10.1108/174103809-10953720.
[29] M.A. Rois, Trisilowati, U. Habibah, Local sensitivity analysis of COVID-19 epidemic with quarantine and isolation using normalized index, Telematika 14 (2021) 13–24, http://dx.doi.org/10.35671/telematika.v14i1.1191.
[30] Olaniyi, S., Ogunniyi, T. D., Okosun, K. O., Lebelo, R. S., Chuma, F. M., & Abimbade, S. F. (2026). Modelling Cholera Transmission Dynamics with Logistic Growth Through Optimal Vaccination, Treatment and Sanitation. International Journal of Analysis and Applications, 24, 318-318. https://doi.org/10.28924/2291-8639-24-2026-318
Published
2026-11-05
How to Cite
Ibrahim, H., Adepoju, O., Adepoju, O., Olanrewaju, J., Ayanwale, J., Awojugbagbe, J., Adejumo, E., & Salahu, W. (2026). IMPACT OF CORRUPT IMMIGRANT ON THE DYNAMICS OF CORRUPTION IN HUMAN POPULATION: A MATHEMATICAL MODELLING APPROACH. IJO - International Journal of Mathematics (ISSN: 2992-4421 ), 9(10), 01-22. Retrieved from https://ijojournals.com/index.php/m/article/view/1369