Analysis and Control of the Bacterial Meningitis Disease Model

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Lakshmi N Sridhar

Abstract

In this study, bifurcation analysis and multiobjective nonlinear model predictive control is performed on a bacterial meningitis disease model. Bifurcation analysis is a powerful mathematical tool used to deal with the nonlinear dynamics of any process. Several factors must be considered, and multiple objectives must be met simultaneously. The MATLAB program MATCONT was used to perform the bifurcation analysis. The MNLMPC calculations were performed using the optimization language PYOMO in conjunction with the state-of-the-art global optimization solvers IPOPT and BARON. The bifurcation analysis revealed the existence of Hopf bifurcation point, limit and branchpoint. The MNLMC converged to the utopia solution. The Hopf bifurcation point, which causes an unwanted limit cycle, is eliminated using an activation factor involving the tanh function. The limit and branch points (which cause multiple steady-state solutions from a singular point) are very beneficial because they enable the Multiobjective nonlinear model predictive control calculations to converge to the Utopia point (the best possible solution) in the model.

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N Sridhar, L. (2025). Analysis and Control of the Bacterial Meningitis Disease Model. Global Journal of Medical and Clinical Case Reports, 206–213. https://doi.org/10.17352/gjmccr.000227
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Copyright (c) 2025 Sridhar LN.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Martcheva M, Crispino-O’Connell G. The transmission of meningococcal infection: a mathematical study. J Math Anal Appl. 2003;283:251–75. Available from: https://doi.org/10.1016/S0022-247X(03)00289-0

Irving T, Blyuss K, Colijn C, Trotter C. Modelling meningococcal meningitis in the African meningitis belt. Epidemiol Infect. 2012;140:897–905. Available from: https://doi.org/10.1017/s0950268811001385

Bloch KC, Tang YW. Molecular approaches to the diagnosis of meningitis and encephalitis. In: Persing DH, Tenover FC, Hayden RT, Ieven M, Miller MB, Nolte FS, et al., editors. Molecular Microbiology: Diagnostic Principles and Practice. Washington (DC): ASM Press; 2016;285–305. Available from: https://doi.org/10.1128/9781555819071.ch24

Blyuss KB. Mathematical modelling of the dynamics of meningococcal meningitis in Africa. In: Aston PJ, Mulholland AJ, Tant KMM, editors. UK Success Stories in Industrial Mathematics. Cham: Springer; 2016;221–6. Available from: https://doi.org/10.1007/978-3-319-25454-8_28

Elmojtaba IM, Adam S. A mathematical model for meningitis disease. Red Sea Univ J Basic Appl Sci. 2017;2:467–72. Available from: https://squ.elsevierpure.com/en/publications/a-mathematical-model-for-meningitis-disease

Oordt-Speets AM, Bolijn R, van Hoorn RC, Bhavsar A, Kyaw MH. Global etiology of bacterial meningitis: a systematic review and meta-analysis. PLoS One. 2018;13:e0198772. Available from: https://doi.org/10.1371/journal.pone.0198772

Asamoah JKK, Nyabadza F, Seidu B, Chand M, Dutta H. Mathematical modelling of bacterial meningitis transmission dynamics with control measures. Comput Math Methods Med. 2018;2018:2657461. Available from: https://doi.org/10.1155/2018/2657461

Yusuf T. Mathematical modelling and simulation of meningococcal meningitis transmission dynamics. FUTA J Res Sci. 2018;14:94–104. Available from: https://doi.org/10.1155/2018/2657461

Agusto F, Leite M. Optimal control and cost-effective analysis of the 2017 meningitis outbreak in Nigeria. Infect Dis Model. 2019;4:161–87. Available from: https://doi.org/10.1016/j.idm.2019.05.003

Koutangni T, Crepey P, Woringer M, Porgho S, Bicaba B, Tall H, et al. Compartmental models for seasonal hyperendemic bacterial meningitis in the African meningitis belt. Epidemiol Infect. 2019;147:e14. Available from: https://doi.org/10.1017/s0950268818002625

Asamoah JKK, Nyabadza F, Jin Z, Bonyah E, Khan MA, Li MY, et al. Backward bifurcation and sensitivity analysis for bacterial meningitis transmission dynamics with a nonlinear recovery rate. Chaos Solit Fractals. 2020;140:110237. Available from: https://doi.org/10.1016/j.chaos.2020.110237

Musa SS, Zhao S, Hussaini N, Habib AG, He D. Mathematical modeling and analysis of meningococcal meningitis transmission dynamics. Int J Biomath. 2020;13:2050006. Available from: https://doi.org/10.1142/S1793524520500060

Baba IA, Olamilekan LI, Yusuf A, Baleanu D. Analysis of meningitis model: a case study of northern Nigeria. AIMS Bioeng. 2020;7:179–93. Available from: https://www.aimspress.com/article/10.3934/bioeng.2020016

Ali SA, Taj MK, Ali SH. Antimicrobial resistance pattern of bacterial meningitis among patients in Quetta, Pakistan. Infect Drug Resist. 2021;14:5107. Available from: https://doi.org/10.2147/idr.s339231

Mazamay S, Guégan JF, Diallo N, Bompangue D, Bokabo E, Muyembe JJ, et al. An overview of bacterial meningitis epidemics in Africa from 1928 to 2018 with a focus on epidemics “outside-the-belt”. BMC Infect Dis. 2021;21:1–13. Available from: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-021-06724-1

Workineh YH, Kassa SM. Optimal control of the spread of meningitis: in the presence of behaviour change of the society and information-dependent vaccination. Commun Math Biol Neurosci. 2021;2021:29. Available from: https://scik.org/index.php/cmbn/article/view/5575

Veronica CM, Olusegun O, Newton A, Sunday AA. Mathematical modeling and stability analyses on the transmission dynamics of bacterial meningitis. J Math Comput Sci. 2021;11:7384–413. Available from: https://scik.org/index.php/jmcs/article/view/6513

Afolabi M, Adewoye KS, Folorunso AI, Omoloye MA. A mathematical model on transmission dynamics of meningococcal meningitis. IRE J. 2021;4:59–66. Available from: https://www.researchgate.net/publication/352090634_A_Mathematical_Model_on_Transmission_Dynamics_of_Meningococcal_Meningitis

Crankson MV. Mathematical modeling and optimal control of the transmission dynamics of bacterial meningitis population in Ghana. Tarkwa (GH): University of Mines and Technology; 2021.

Yano TK, Bitok J, Jerop R. Optimal control analysis of meningococcal meningitis disease with varying population size. Appl Comput Math. 2022;11:140–9. Available from: https://www.sciencepublishinggroup.com/article/10.11648/j.acm.20221105.14

Belay MA, Abonyo OJ, Theuri DM. Mathematical model analysis for the transmission dynamics of bacterial meningitis disease incorporating drug-resistance class. Commun Math Biol Neurosci. 2022;2022:121. Available from: https://scik.org/index.php/cmbn/article/view/7774

Belay MA, Abonyo JO, Alemneh HT, Engida HA, Ferede MM, Delnessaw SA. Optimal control and cost-effectiveness analysis for bacterial meningitis disease. Front Appl Math Stat. 2024;10:1460481. Available from: https://doi.org/10.3389/fams.2024.1460481

Dhooge A, Govaerts W, Kuznetsov AY. MATCONT: A Matlab package for numerical bifurcation analysis of ODEs. ACM Trans Math Softw. 2003;29(2):141–164. Available from: https://doi.org/10.1145/980175.980184

Dhooge A, Govaerts W, Kuznetsov YA, Mestrom W, Rie AM. CL_MATCONT: A continuation toolbox in Matlab. 2004. Available from: https://doi.org/10.1145/952532.952567

Kuznetsov YA. Elements of applied bifurcation theory. New York: Springer; 1998. Available from: https://www.ma.imperial.ac.uk/~dturaev/kuznetsov.pdf

Kuznetsov YA. Five lectures on numerical bifurcation analysis. Utrecht University, NL; 2009.

Govaerts WJF. Numerical methods for bifurcations of dynamical equilibria. Philadelphia (PA): SIAM; 2000. Available from: https://dl.acm.org/doi/10.5555/346850

Dubey SR, Singh SK, Chaudhuri BB. Activation functions in deep learning: A comprehensive survey and benchmark. Neurocomputing. 2022;503:92–108. Available from: https://doi.org/10.1016/j.neucom.2022.06.111

Kamalov AF, Nazir M, Safaraliev AK, Cherukuri R, Zgheib R. Comparative analysis of activation functions in neural networks. In: 2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS); Dubai, United Arab Emirates. 2021;1–6. Available from: https://doi.org/10.1109/ICECS53924.2021.9665646

Szandała T. Review and comparison of commonly used activation functions for deep neural networks. ArXiv. 2020. Available from: https://doi.org/10.1007/978-981-15-5495-7

Sridhar LN. Bifurcation analysis and optimal control of the tumor macrophage interactions. Biomed J Sci Tech Res. 2023;53(5):8470. Available from: https://doi.org/10.26717/BJSTR.2023.53.008470

Sridhar LN. Elimination of oscillation causing Hopf bifurcations in engineering problems. J Appl Math. 2024;2(4):1826. Available from: https://doi.org/10.59400/jam1826

Flores-Tlacuahuac A, Morales P, Rivera Toledo M. Multiobjective nonlinear model predictive control of a class of chemical reactors. Ind Eng Chem Res. 2012;51(16):5891–5899. Available from: https://doi.org/10.1021/ie201742e

Hart WE, Laird CD, Watson JP, Woodruff DL, Hackebeil GA, Nicholson BL, et al. Pyomo – Optimization modeling in Python. 2nd ed. 2021;67. Available from: https://link.springer.com/book/10.1007/978-3-030-68928-5

Wächter A, Biegler LT. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math Program. 2006;106:25–57. Available from: https://doi.org/10.1007/s10107-004-0559-y

Tawarmalani M, Sahinidis NV. A polyhedral branch-and-cut approach to global optimization. Math Program. 2005;103(2):225–249. Available from: https://link.springer.com/article/10.1007/s10107-005-0581-8

Sridhar LN. Coupling bifurcation analysis and multiobjective nonlinear model predictive control. Austin Chem Eng. 2024;10(3):1107. Available from: https://doi.org/10.59400/se1751

Upreti SR. Optimal control for chemical engineers. Boca Raton (FL): Taylor and Francis; 2013. Available from: https://doi.org/10.1201/b13045