Research Article
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Year 2024, Volume: 4 Issue: 1, 1 - 36, 31.03.2024
https://doi.org/10.53391/mmnsa.1373093

Abstract

References

  • [1] World Health Organization (WHO), Coronavirus (COVID-19), Events as They Happen, 31.07.2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events- as-they-happen.
  • [2] Cavallo, J.J., Donoho, D.A. and Forman, H.P. Hospital capacity and operations in the coronavirus disease 2019 (COVID-19) pandemic—planning for the Nth patient. JAMA Health Forum, 1(3), e200345, (2020).
  • [3] Ahmed, H.M., Elbarkouky, R.A., Omar, O.A.M. and Ragusa, M.A. Models for COVID-19 daily confirmed cases in different countries. Mathematics, 9(6), 659, (2021).
  • [4] Alansari, M. and Shagari, M.S. Analysis of fractional differential inclusion models for COVID-19 via fixed point results in metric space. Journal of Function Spaces, 2022, 8311587, (2022).
  • [5] Mustafa, H.I., Al-shami, T.M. and Wassef, R. Rough set paradigms via containment neighborhoods and ideals. Filomat, 37(14), 4683-4702, (2023).
  • [6] Kifle, Z.S. and Lemecha Obsu, L. Optimal control analysis of a COVID-19 model. Applied Mathematics in Science and Engineering, 31(1), 2173188, (2023).
  • [7] Akanni, J.O., Fatmawati and Chukwu, C.W. On the fractional-order modeling of COVID-19 dynamics in a population with limited resources. Communications in Mathematical Biology and Neuroscience, 2023, 12, (2023).
  • [8] Singh, H., Srivastava, H.M., Hammouch, Z. and Nisar, K.S. Numerical simulation and stability analysis for the fractional-order dynamics of COVID-19. Results in Physics, 20, 103722, (2021).
  • [9] Chen, Y., Liu, Q. and Guo, D. Emerging coronaviruses: genome structure, replication, and pathogenesis. Journal of Medical Virology, 92(4), 418-423, (2020).
  • [10] Ouaziz, S.I. and Khomssi, M.E. Dynamics and optimal control methods for the COVID-19 model. IN Mathematical Modeling and Intelligent Control for Combating Pandemics (pp. 21-38). Springer Optimization and Its Applications, 203, (2023).
  • [11] Uçar, S., Uçar, E., Özdemir, N. and Hammouch, Z. Mathematical analysis and numerical simulation for a smoking model with Atangana–Baleanu derivative. Chaos, Solitons & Fractals, 118, 300-306, (2019).
  • [12] Makinde, O.D., Akanni, J.O. and Abidemi, A. Modelling the impact of drug abuse on a nation’s education sector. Journal of Applied Nonlinear Dynamics, 12(1), 53-73, (2023).
  • [13] Tang, B., Wang, X., Li, Q., Bragazzi, N.L., Tang, S., Xiao, Y. and Wu, J. Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. Journal of Clinical Medicine, 9(2), 462, (2020).
  • [14] Srivastava, A. and Chowell, G. Understanding spatial heterogeneity of COVID-19 pandemic using shape analysis of growth rate curves. MedRxiv, (2020).
  • [15] Eikenberry, S.E., Mancuso, M., Iboi, E., Phan, T., Eikenberry, K., Kuang, Y. et al. To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infectious Disease Modelling, 5, 293-308, (2020).
  • [16] Okosun, K.O., Rachid, O. and Marcus, N. Optimal control strategies and cost-effectiveness analysis of a malaria model. BioSystems, 111(2), 83-101, (2013).
  • [17] Ghosh, J.K., Biswas, S.K., Sarkar, S. and Ghosh, U. Mathematical modelling of COVID-19: a case study of Italy. Mathematics and Computers in Simulation, 194, 1-18, (2022).
  • [18] Johansson, M.A., Quandelacy, T.M., Kada, S., Prasad, P.V., Steele, M., Brooks, J.T. et al. SARS-CoV-2 transmission from people without COVID-19 symptoms. JAMA Network Open, 4(1), e2035057, (2021).
  • [19] Asamoah, J.K.K., Okyere, E., Abidemi, A., Moore, S.E., Sun, G.Q., Jin, Z. et al. Optimal control and comprehensive cost-effectiveness analysis for COVID-19. Results in Physics, 33, 105177 (2022).
  • [20] Samui, P., Mondal, J. and Khajanchi, S. A mathematical model for COVID-19 transmission dynamics with a case study of India. Chaos, Solitons & Fractals, 140, 110173, (2020).
  • [21] Khan, M.A. and Atangana, A. Mathematical modeling and analysis of COVID-19: a study of new variant Omicron. Physica A: Statistical Mechanics and its Applications, 599, 127452, (2022).
  • [22] Yang, H., Lin, X., Li, J., Zhai, Y. and Wu, J. A review of mathematical models of COVID-19 transmission. Contemporary Mathematics, 4(1), 75-98, (2023).
  • [23] Capasso, V. and Serio, G. A generalization of the Kermack-McKendrick deterministic epidemic model. Mathematical Biosciences, 42(1-2), 43–61, (1978).
  • [24] Buonomo, B., d’Onofrio, A. and Lacitignola, D. Global stability of an SIR epidemic model with information dependent vaccination. Mathematical Biosciences, 216(1), 9-16, (2008).
  • [25] Khajanchi, S., Sarkar, K. and Mondal, J. Dynamics of the COVID-19 pandemic in India. ArXiv Preprint, ArXiv:2005.06286, (2020).
  • [26] Akanni, J.O., Akinpelu, F.O., Olaniyi, S., Oladipo, A.T. and Ogunsola, A.W. Modelling financial crime population dynamics: optimal control and cost-effectiveness analysis. International Journal of Dynamics and Control, 8, 531–544, (2020).
  • [27] Diekmann, O., Heesterbeek, J.A.P. and Roberts, M.G. The construction of next-generation matrices for compartmental epidemic models. Journal of the Royal Society Interface, 7(47), 873–885, (2010).
  • [28] Iggidr, A., Mbang, J., Sallet, G. and Tewa, J.J. Multi-compartment models. Discrete and Continuous Dynamical Systems Supplement, 2007, 506-519, (2007).
  • [29] Cvetkovic, A. Stabilizing the Metzler matrices with applications to dynamical systems. Calcolo, 57, 1, (2020).
  • [30] Korobeinikov, A. and Maini, P.K. A Lyapunov function and global properties for SIR and SEIR epidemiological models with nonlinear incidence. Mathematical Biosciences & Engineering, 1(1), 57–60, (2004).
  • [31] Chitnis, N., Hyman, J.M. and Cushing, J.M. Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model. Bulletin of Mathematical Biology, 70, 1272–1296, (2008).
  • [32] Berhe, H.W. and Makinde, O.D. Computational modelling and optimal control of measles epidemic in human population. Biosystems, 190, 104102, (2020).
  • [33] Abidemi, A., Akanni, J.O. and Makinde, O.D. A non-linear mathematical model for analysing the impact of COVID-19 disease on higher education in developing countries. Healthcare Analytics, 3, 100193, (2023).
  • [34] Gaff, H.D., Schaefer, E. and Lenhart, S. Use of optimal control models to predict treatment time for managing tick-borne disease. Journal of Biological Dynamics, 5(5), 517–530, (2011).
  • [35] Fleming, W.H. and Rishel, R.W. Deterministic and Stochastic Optimal Control (Vol. 1). New York: Springer-Verlag, (1975).
  • [36] Coddington, E.A., Levinson, N. and Teichmann, T. Theory of ordinary differential equations. Physics Today, 9(2), 18, (1956).
  • [37] Aseev, S.M. and Kryazhimskii, A.V. The Pontryagin maximum principle and optimal economic growth problems. Proceedings of the Steklov Institute of Mathematics, 257, 1–255, (2007).
  • [38] World Health Organization (WHO), Coronavirus disease (COVID-19). https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
  • [39] Kouidere, A., Kada, D., Balatif, O., Rachik, M. and Naim, M. Optimal control approach of a mathematical modeling with multiple delays of the negative impact of delays in applying preventive precautions against the spread of the COVID-19 pandemic with a case study of Brazil and cost-effectiveness. Chaos, Solitons & Fractals, 142, 110438, (2021).
  • [40] Abidemi, A. and Akanni, J.O. Dynamics of illicit drug use and banditry population with optimal control strategies and cost-effectiveness analysis. Computational and Applied Mathematics, 41, 53, (2022).
  • [41] Alhassan, A., Momoh, A.A., Abdullahi, A.S. and Kadzai, M.T.Y. Optimal control strategies and cost effectiveness analysis of a malaria transmission model. Mathematical Theory and Modeling, 7(6), 123-138, (2017).
  • [42] Kada, D., Labzai, A., Balatif, O., Rachik, M. and Labriji, E. H. Spread of COVID-19 in Morocco discrete mathematical modeling: optimal control strategies and cost-effectiveness analysis. Journal of Mathematical and Computational Science, 10(5), 2070-2093, (2020).
  • [43] Marsudi, Hidayat, N. and Wibowo, R.B.E. Optimal control and cost-effectiveness analysis of HIV model with educational campaigns and therapy. Matematika, 123-138, (2019).
  • [44] Dietz, K. The estimation of the basic reproduction number for infectious diseases. Statistical Methods in Medical Research, 2(1), 23-41, (1993).

Mathematical approaches to controlling COVID-19: optimal control and financial benefits

Year 2024, Volume: 4 Issue: 1, 1 - 36, 31.03.2024
https://doi.org/10.53391/mmnsa.1373093

Abstract

The global population has suffered extensively as an effect of the coronavirus infection, with the loss of many lives, adverse financial consequences, and increased impoverishment. In this paper, we propose an example of the non-linear mathematical modeling of the COVID-19 phenomenon. Using the fixed point theorem, we established the solution's existence and unicity. We demonstrate how, under the framework, the basic reproduction number can be redefined. The different equilibria of the model are identified, and their stability analyses are carefully examined. According to our argument, it is illustrated that there is a single optimal control that can be used to reduce the expense of the illness load and applied processes. The determination of optimal strategies is examined with the aid of Pontryagin's maximum principle. To support the analytical results, we perform comprehensive digital simulations using the Runge-Kutta 4th-order. The data simulated suggest that the effects of the recommended controls significantly impact the incidence of the disease, in contrast to the absence of control cases. Further, we calculate the incremental cost-effectiveness ratio to assess the cost and benefits of each potential combination of the two control measures. The findings indicate that public attention, personal hygiene practices, and isolating oneself will all contribute to slowing the spread of COVID-19. Furthermore, those who are infected can readily decrease their virus to become virtually non-detectable with treatment consent.

Thanks

We appreciate the editor's and reviewers' careful and thorough remarks on our paper.

References

  • [1] World Health Organization (WHO), Coronavirus (COVID-19), Events as They Happen, 31.07.2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events- as-they-happen.
  • [2] Cavallo, J.J., Donoho, D.A. and Forman, H.P. Hospital capacity and operations in the coronavirus disease 2019 (COVID-19) pandemic—planning for the Nth patient. JAMA Health Forum, 1(3), e200345, (2020).
  • [3] Ahmed, H.M., Elbarkouky, R.A., Omar, O.A.M. and Ragusa, M.A. Models for COVID-19 daily confirmed cases in different countries. Mathematics, 9(6), 659, (2021).
  • [4] Alansari, M. and Shagari, M.S. Analysis of fractional differential inclusion models for COVID-19 via fixed point results in metric space. Journal of Function Spaces, 2022, 8311587, (2022).
  • [5] Mustafa, H.I., Al-shami, T.M. and Wassef, R. Rough set paradigms via containment neighborhoods and ideals. Filomat, 37(14), 4683-4702, (2023).
  • [6] Kifle, Z.S. and Lemecha Obsu, L. Optimal control analysis of a COVID-19 model. Applied Mathematics in Science and Engineering, 31(1), 2173188, (2023).
  • [7] Akanni, J.O., Fatmawati and Chukwu, C.W. On the fractional-order modeling of COVID-19 dynamics in a population with limited resources. Communications in Mathematical Biology and Neuroscience, 2023, 12, (2023).
  • [8] Singh, H., Srivastava, H.M., Hammouch, Z. and Nisar, K.S. Numerical simulation and stability analysis for the fractional-order dynamics of COVID-19. Results in Physics, 20, 103722, (2021).
  • [9] Chen, Y., Liu, Q. and Guo, D. Emerging coronaviruses: genome structure, replication, and pathogenesis. Journal of Medical Virology, 92(4), 418-423, (2020).
  • [10] Ouaziz, S.I. and Khomssi, M.E. Dynamics and optimal control methods for the COVID-19 model. IN Mathematical Modeling and Intelligent Control for Combating Pandemics (pp. 21-38). Springer Optimization and Its Applications, 203, (2023).
  • [11] Uçar, S., Uçar, E., Özdemir, N. and Hammouch, Z. Mathematical analysis and numerical simulation for a smoking model with Atangana–Baleanu derivative. Chaos, Solitons & Fractals, 118, 300-306, (2019).
  • [12] Makinde, O.D., Akanni, J.O. and Abidemi, A. Modelling the impact of drug abuse on a nation’s education sector. Journal of Applied Nonlinear Dynamics, 12(1), 53-73, (2023).
  • [13] Tang, B., Wang, X., Li, Q., Bragazzi, N.L., Tang, S., Xiao, Y. and Wu, J. Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. Journal of Clinical Medicine, 9(2), 462, (2020).
  • [14] Srivastava, A. and Chowell, G. Understanding spatial heterogeneity of COVID-19 pandemic using shape analysis of growth rate curves. MedRxiv, (2020).
  • [15] Eikenberry, S.E., Mancuso, M., Iboi, E., Phan, T., Eikenberry, K., Kuang, Y. et al. To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infectious Disease Modelling, 5, 293-308, (2020).
  • [16] Okosun, K.O., Rachid, O. and Marcus, N. Optimal control strategies and cost-effectiveness analysis of a malaria model. BioSystems, 111(2), 83-101, (2013).
  • [17] Ghosh, J.K., Biswas, S.K., Sarkar, S. and Ghosh, U. Mathematical modelling of COVID-19: a case study of Italy. Mathematics and Computers in Simulation, 194, 1-18, (2022).
  • [18] Johansson, M.A., Quandelacy, T.M., Kada, S., Prasad, P.V., Steele, M., Brooks, J.T. et al. SARS-CoV-2 transmission from people without COVID-19 symptoms. JAMA Network Open, 4(1), e2035057, (2021).
  • [19] Asamoah, J.K.K., Okyere, E., Abidemi, A., Moore, S.E., Sun, G.Q., Jin, Z. et al. Optimal control and comprehensive cost-effectiveness analysis for COVID-19. Results in Physics, 33, 105177 (2022).
  • [20] Samui, P., Mondal, J. and Khajanchi, S. A mathematical model for COVID-19 transmission dynamics with a case study of India. Chaos, Solitons & Fractals, 140, 110173, (2020).
  • [21] Khan, M.A. and Atangana, A. Mathematical modeling and analysis of COVID-19: a study of new variant Omicron. Physica A: Statistical Mechanics and its Applications, 599, 127452, (2022).
  • [22] Yang, H., Lin, X., Li, J., Zhai, Y. and Wu, J. A review of mathematical models of COVID-19 transmission. Contemporary Mathematics, 4(1), 75-98, (2023).
  • [23] Capasso, V. and Serio, G. A generalization of the Kermack-McKendrick deterministic epidemic model. Mathematical Biosciences, 42(1-2), 43–61, (1978).
  • [24] Buonomo, B., d’Onofrio, A. and Lacitignola, D. Global stability of an SIR epidemic model with information dependent vaccination. Mathematical Biosciences, 216(1), 9-16, (2008).
  • [25] Khajanchi, S., Sarkar, K. and Mondal, J. Dynamics of the COVID-19 pandemic in India. ArXiv Preprint, ArXiv:2005.06286, (2020).
  • [26] Akanni, J.O., Akinpelu, F.O., Olaniyi, S., Oladipo, A.T. and Ogunsola, A.W. Modelling financial crime population dynamics: optimal control and cost-effectiveness analysis. International Journal of Dynamics and Control, 8, 531–544, (2020).
  • [27] Diekmann, O., Heesterbeek, J.A.P. and Roberts, M.G. The construction of next-generation matrices for compartmental epidemic models. Journal of the Royal Society Interface, 7(47), 873–885, (2010).
  • [28] Iggidr, A., Mbang, J., Sallet, G. and Tewa, J.J. Multi-compartment models. Discrete and Continuous Dynamical Systems Supplement, 2007, 506-519, (2007).
  • [29] Cvetkovic, A. Stabilizing the Metzler matrices with applications to dynamical systems. Calcolo, 57, 1, (2020).
  • [30] Korobeinikov, A. and Maini, P.K. A Lyapunov function and global properties for SIR and SEIR epidemiological models with nonlinear incidence. Mathematical Biosciences & Engineering, 1(1), 57–60, (2004).
  • [31] Chitnis, N., Hyman, J.M. and Cushing, J.M. Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model. Bulletin of Mathematical Biology, 70, 1272–1296, (2008).
  • [32] Berhe, H.W. and Makinde, O.D. Computational modelling and optimal control of measles epidemic in human population. Biosystems, 190, 104102, (2020).
  • [33] Abidemi, A., Akanni, J.O. and Makinde, O.D. A non-linear mathematical model for analysing the impact of COVID-19 disease on higher education in developing countries. Healthcare Analytics, 3, 100193, (2023).
  • [34] Gaff, H.D., Schaefer, E. and Lenhart, S. Use of optimal control models to predict treatment time for managing tick-borne disease. Journal of Biological Dynamics, 5(5), 517–530, (2011).
  • [35] Fleming, W.H. and Rishel, R.W. Deterministic and Stochastic Optimal Control (Vol. 1). New York: Springer-Verlag, (1975).
  • [36] Coddington, E.A., Levinson, N. and Teichmann, T. Theory of ordinary differential equations. Physics Today, 9(2), 18, (1956).
  • [37] Aseev, S.M. and Kryazhimskii, A.V. The Pontryagin maximum principle and optimal economic growth problems. Proceedings of the Steklov Institute of Mathematics, 257, 1–255, (2007).
  • [38] World Health Organization (WHO), Coronavirus disease (COVID-19). https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
  • [39] Kouidere, A., Kada, D., Balatif, O., Rachik, M. and Naim, M. Optimal control approach of a mathematical modeling with multiple delays of the negative impact of delays in applying preventive precautions against the spread of the COVID-19 pandemic with a case study of Brazil and cost-effectiveness. Chaos, Solitons & Fractals, 142, 110438, (2021).
  • [40] Abidemi, A. and Akanni, J.O. Dynamics of illicit drug use and banditry population with optimal control strategies and cost-effectiveness analysis. Computational and Applied Mathematics, 41, 53, (2022).
  • [41] Alhassan, A., Momoh, A.A., Abdullahi, A.S. and Kadzai, M.T.Y. Optimal control strategies and cost effectiveness analysis of a malaria transmission model. Mathematical Theory and Modeling, 7(6), 123-138, (2017).
  • [42] Kada, D., Labzai, A., Balatif, O., Rachik, M. and Labriji, E. H. Spread of COVID-19 in Morocco discrete mathematical modeling: optimal control strategies and cost-effectiveness analysis. Journal of Mathematical and Computational Science, 10(5), 2070-2093, (2020).
  • [43] Marsudi, Hidayat, N. and Wibowo, R.B.E. Optimal control and cost-effectiveness analysis of HIV model with educational campaigns and therapy. Matematika, 123-138, (2019).
  • [44] Dietz, K. The estimation of the basic reproduction number for infectious diseases. Statistical Methods in Medical Research, 2(1), 23-41, (1993).
There are 44 citations in total.

Details

Primary Language English
Subjects Dynamical Systems in Applications
Journal Section Research Articles
Authors

Saida Id Ouaziz 0009-0005-3776-4412

Mohammed El Khomssi 0009-0007-7250-811X

Publication Date March 31, 2024
Submission Date October 9, 2023
Published in Issue Year 2024 Volume: 4 Issue: 1

Cite

APA Id Ouaziz, S., & El Khomssi, M. (2024). Mathematical approaches to controlling COVID-19: optimal control and financial benefits. Mathematical Modelling and Numerical Simulation With Applications, 4(1), 1-36. https://doi.org/10.53391/mmnsa.1373093


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