Global Stability Analysis of a Malaria–Typhoid Fever Co-infection Model

Authors

  • Roseline Toyin Abah Department of Mathematics University of Abuja, Abuja, Nigeria
  • Christiana Ene Agbo Department of Mathematics University of Abuja, Abuja, Nigeria
  • Aliyu Ibrahim Department of Mathematics University of Abuja, Abuja, Nigeria

DOI:

https://doi.org/10.63561/jmns.v2i4.1125

Keywords:

Global Stability, Analysis, Malaria Typhoid Fever, Co-infection, Model

Abstract

Typhoid fever and malaria are two serious infectious diseases that are common in sub-Saharan Africa, and co-infection poses a serious threat to public health. Designing successful control measures requires an understanding of the dynamics of these illnesses. We created a deterministic compartmental model that divided the human population into seven groups: susceptible people, typhoid-only infected people, malaria-only infected people, co-infected people, recovered people, susceptible mosquitoes, and infected mosquitoes. We defined parameters for the global stability of the disease-free equilibria and determined the fundamental reproduction numbers for typhoid and malaria using the next-generation matrix technique. The global stability results were validated using Lyapunov functions and LaSalle's invariance principle. When both fundamental reproduction numbers are less than unity, the disease-free equilibrium is asymptotically stable worldwide.Numerical simulations highlight the threshold parameters that drive co-infection persistence and the combined impact of malaria–typhoid interventions. This study provides a theoretical basis for controlling malaria and typhoid co-infections through integrated interventions. The analytical thresholds derived can guide policymakers in optimizing combined control strategies in endemic regions such as Nigeria

References

Abah, R. T., Zhiri, A. B., Oshinubi, K., & Adeniji, A. (2023). Mathematical analysis and simulation of Ebola virus disease spread incorporating mitigation measures. Franklin Open, 6, 100066. https://doi.org/10.1016/j.franklinop.2023.100066

Abu-Raddad, L. J., Patnaik, P., & Kublin, J. G. (2006). Dual infection with HIV and malaria fuels the spread of both diseases in sub-Saharan Africa. Science, 314(5805), 1603–1606. https://doi.org/10.1126/science.1132337

Adewuyi, E. O., Akinyemi, J. O., & Oyeyemi, O. T. (2018). Malaria and typhoid co-infection among febrile patients in Nigeria: Implications for clinical management. PLoS One, 13(9), e0203899. https://doi.org/10.1371/journal.pone.0203899

Agbo, C., Abah, R., & Abdullahi, A. M. (2024). A mathematical modelling on the stability analysis of heart disease dynamics: Mathematical modelling on the stability analysis of heart disease. Journal of Institutional Research Big Data Analytics and Innovation, 1(1). https://universityjournals.com.ng/index.php/jirbdai/article/view/54/24

Agbo, C. E., Abah, R. T., & Ogunfiditimi, F. O. (2025). Global stability analysis of the mathematical modelling for heart disease transmission and prevention dynamics. FNAS Journal of Mathematical and Statistical Computing, 2(3), 34-43 https://doi.org/10.63561/jmsc.v2i3.855.

Akinyemi, S. T., Idisi, I. O., Rabiu, M., Okeowo, V. I., Iheonu, N., Dansu, E. J., ... & Oshinubi, K. (2023). A tale of two countries: optimal control and cost-effectiveness analysis of monkeypox disease in Germany and Nigeria. Healthcare Analytics, 4, 100258. https://doi.org/10.1016/j.health.2023.100258

Akinyi, O. C., Mugisha, J. Y. T., Manyonge, A. W., & Ouma, C. (2015). A model on the impact of treating typhoid with anti-malarial: Dynamics of malaria concurrent and co-infection with typhoid. International Journal of Mathematical Analysis, 9, 541–551. https://doi.org/10.12988/ijma.2015.412403

Bhan, K. M., Bahl, R., & Bhatnagar, S. (2005). Typhoid and paratyphoid fever. The Lancet, 366(9487), 749–762. https://doi.org/10.1016/S0140-6736(05)67181-4

Buckle, G. C., Walker, C. L., & Black, R. E. (2012). Typhoid fever and paratyphoid fever: Systematic review to estimate global morbidity and mortality for 2010. Journal of Global Health, 2(1), 010401. https://doi.org/10.7189/jogh.02.010401

Cheng, Q., Zhao, X., & Zhong, J. (2020). Global stability and optimal control of a waterborne disease model with latent period. Nonlinear Dynamics, 102, 1317–1333. https://doi.org/10.1007/s11071-020-05694-4

Chitnis, N., Cushing, J. M., & Hyman, J. M. (2006). Bifurcation analysis of a mathematical model for malaria transmission. Journal of Applied Mathematics, 67(1), 24–45. https://doi.org/10.1090/S0025-5718-06-01807-9

Crump, J. A., & Mintz, E. D. (2010). Global trends in typhoid and paratyphoid fever. Clinical Infectious Diseases, 50(2), 241–246. https://doi.org/10.1086/649541

Diekmann, O., Heesterbeek, J. A. P., & Britton, T. (2020). Mathematical tools for understanding infectious disease dynamics. Princeton University Press. https://doi.org/10.1515/9781400845620

Gutiérrez-Jara, J. P., Córdova-Lepe, F., & Muñoz-Quezada, M. T. (2019). Dynamics between infectious diseases with two susceptibility conditions: A mathematical model. Mathematical Biosciences, 309, 66–77. https://doi.org/10.1016/j.mbs.2019.01.005

Kang, G., Rajalakshmi, B., & Shenoy, S. (2020). Clinical features and prevalence of malaria–typhoid co-infection in endemic areas: A cross-sectional study. Tropical Medicine & International Health, 25(2), 238–244. https://doi.org/10.1111/tmi.13345

Korobeinikov, A. (2006). Lyapunov functions and global stability for SIR and SEIR epidemiological models with nonlinear incidence rate. Bulletin of Mathematical Biology, 68(3), 615–626. https://doi.org/10.1007/s11538-005-9049-6

Liu, Q., Wang, J., & Wang, Y. (2017). Global stability analysis of a vector-borne disease model with vaccination and vector control. Mathematical Biosciences and Engineering, 14(2), 325–344. https://doi.org/10.3934/mbe.2017014

Mutua, J. M., Wang, F. B., & Vaidya, N. K. (2015). Modeling malaria and typhoid fever co-infection dynamics. Mathematical Biosciences, 264, 128–144. https://doi.org/10.1016/j.mbs.2015.03.014

Mushayabasa, S., Bhunu, C. P., & Mhlanga, N. A. (2014). Modeling the transmission dynamics of typhoid in malaria endemic settings. Applications and Applied Mathematics: An International Journal (AAM), 9(1), 1–18. https://digitalcommons.pvamu.edu/aam/vol9/iss1/9

Nguyen, T. V., Nguyen, T. D., Phan, H. T., et al. (2021). Malaria and typhoid fever co-infection among febrile patients in Vietnam: Diagnostic challenges and clinical implications. BMC Infectious Diseases, 21, 736. https://doi.org/10.1186/s12879-021-06425-1

Okolo, P. N., Makama, C. G., & Abah, R. (2023). A mathematical model for tuberculosis transmission with testing and therapy [Preprint]. https://doi.org/10.33003/fjs-2023-0706-2108

Okosun, O. K., & Makinde, O. D. (2011). Modelling the impact of drug resistance in malaria transmission and its optimal control analysis. International Journal of Physical Sciences, 6(26), 6479–6487 https://doi.org/10.5897/IJPS2024.5086 .

Prüss-Ustün, A., Wolf, J., Bartram, J., Clasen, T., Cumming, O., Freeman, M. C., ... & Johnston, R. (2019). Burden of disease from inadequate water, sanitation and hygiene for selected adverse health outcomes: an updated analysis with a focus on low-and middle-income countries. International journal of hygiene and environmental health, 222(5), 765-777.https://doi.org/10.1016/j.ijheh.2019.05.004

Snow, R. W., Guerra, C. A., Noor, A. M., Myint, H. Y., & Hay, S. I. (2005). The global distribution of clinical episodes of Plasmodium falciparum malaria. Nature, 434(7030), 214–217. https://doi.org/10.1038/nature03342

Takem, E. N., Roca, A., & Cunnington, A. (2014). The association between malaria and non-typhoid Salmonella bacteraemia in children in sub-Saharan Africa: a literature review. Malaria journal, 13(1), 400 https://doi.org/10.1186/1475-2875-13-400.

World Health Organization. (2021). World malaria report 2021. WHO. https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2021 .

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Published

2025-12-30

How to Cite

Abah , R. T., Agbo, C. E., & Ibrahim, A. (2025). Global Stability Analysis of a Malaria–Typhoid Fever Co-infection Model. Faculty of Natural and Applied Sciences Journal of Mathematical Modeling and Numerical Simulation, 2(4), 69–84. https://doi.org/10.63561/jmns.v2i4.1125