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Nonlinear optimal control strategies for a mathematical model of COVID-19 and influenza co-infection

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  • Ojo, Mayowa M.
  • Benson, Temitope O.
  • Peter, Olumuyiwa James
  • Goufo, Emile Franc Doungmo

Abstract

Infectious diseases have remained one of humanity’s biggest problems for decades. Multiple disease infections, in particular, have been shown to increase the difficulty of diagnosing and treating infected people, resulting in worsening human health. For example, the presence of influenza in the population is exacerbating the ongoing COVID-19 pandemic. We formulate and analyze a deterministic mathematical model that incorporates the biological dynamics of COVID-19 and influenza to effectively investigate the co-dynamics of the two diseases in the public. The existence and stability of the disease-free equilibrium of COVID-19-only and influenza-only sub-models are established by using their respective threshold quantities. The result shows that the COVID-19 free equilibrium is locally asymptotically stable when RC<1, whereas the influenza-only model, is locally asymptotically stable when RF<1. Furthermore, the existence of the endemic equilibria of the sub-models is examined while the conditions for the phenomenon of backward bifurcation are presented. A generalized analytical result of the COVID-19-influenza co-infection model is presented. We run a numerical simulation on the model without optimal control to see how competitive outcomes between-hosts and within-hosts affect disease co-dynamics. The findings established that disease competitive dynamics in the population are determined by transmission probabilities and threshold quantities. To obtain the optimal control problem, we extend the formulated model by including three time-dependent control functions. The maximum principle of Pontryagin was used to prove the existence of the optimal control problem and to derive the necessary conditions for optimum disease control. A numerical simulation was performed to demonstrate the impact of different combinations of control strategies on the infected population. The findings show that, while single and twofold control interventions can be used to reduce disease, the threefold control intervention, which incorporates all three controls, will be the most effective in reducing COVID-19 and influenza in the population.

Suggested Citation

  • Ojo, Mayowa M. & Benson, Temitope O. & Peter, Olumuyiwa James & Goufo, Emile Franc Doungmo, 2022. "Nonlinear optimal control strategies for a mathematical model of COVID-19 and influenza co-infection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    • Handle: RePEc:eee:phsmap:v:607:y:2022:i:c:s0378437122007312
    DOI: 10.1016/j.physa.2022.128173
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    References listed on IDEAS

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    1. Ullah, Saif & Khan, Muhammad Altaf, 2020. "Modeling the impact of non-pharmaceutical interventions on the dynamics of novel coronavirus with optimal control analysis with a case study," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Ndaïrou, Faïçal & Area, Iván & Nieto, Juan J. & Torres, Delfim F.M., 2020. "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
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    Cited by:

    1. Adesoye Idowu Abioye & Olumuyiwa James Peter & Emmanuel Addai & Festus Abiodun Oguntolu & Tawakalt Abosede Ayoola, 2024. "Modeling the impact of control strategies on malaria and COVID-19 coinfection: insights and implications for integrated public health interventions," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3475-3495, August.
    2. Omame, Andrew & Abbas, Mujahid, 2023. "Modeling SARS-CoV-2 and HBV co-dynamics with optimal control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    3. Ojo, Mayowa M. & Peter, Olumuyiwa James & Goufo, Emile Franc Doungmo & Nisar, Kottakkaran Sooppy, 2023. "A mathematical model for the co-dynamics of COVID-19 and tuberculosis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 499-520.
    4. Mukhtar, Roshana & Chang, Chuan-Yu & Raja, Muhammad Asif Zahoor & Chaudhary, Naveed Ishtiaq & Shu, Chi-Min, 2024. "Novel nonlinear fractional order Parkinson's disease model for brain electrical activity rhythms: Intelligent adaptive Bayesian networks," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).

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