Sensitivity Analysis and Optimal Control of Covid 19 Model

Firmansyah Firmansyah, Yulita Molliq Rangkuti

Abstract


Coronavirus infection is a disease that causes death and threatens human life; for prevention, it is necessary to quarantine susceptible, exposed, and infected populations and vaccinate the entire population. This kind of quarantine and vaccination is intended to reduce the spread of coronavirus. Epidemiological models are a strategy used by public health practitioners to prevent and fight diseases. However, to be used in decision making, mathematical models must be carefully parameterized and validated using epidemiological and entomological data. Epidemiological models: susceptible, symptomatic, contagious, and recovering. In this study, sensitivity analysis and optimal control were performed to determine the relative importance of the model parameters and to minimize the number of infected populations and control measures against the spread of the disease. Sensitivity analysis was carried out using a sensitivity index to measure the relative change in the basic reproduction number for each parameter, and this control function was applied to the dynamic modeling of the spread of COVID-19 using the Pontryagin Minimum Principle. We will describe the formulation of a dynamic system for the spread of COVID-19 with optimal control and then use Pontryagin’s Minimum Principle to find optimal control solutions. In this article, COVID-19 cases in the USA and India serve as examples of the efficiency of control measures. The results obtained revealed that the parameters that became the basis for reducing the number of infected with COVID-19 for the two countries, the USA and India, are effective transmission rates from S to E, (β), transmission rates from E to I, (α), and transmission rates from S to R, (ps), which are the main parameters to watch for growth with respect to Basic Reproduction rates (R0). Finally, three controls were simulated in cases I (in the USA) and II (in India) in the interval t ∈ [0, 15]. For all controls, the effectiveness was close to 50% in India and 100% in the USA to reduce the spread of COVID 19. According to the findings, if these three controls were implemented ideally from the start of the pandemic, the number of sufferers.

Keywords


COVID-19; SEIR Model; Optimal Control; Sensitivity Analysis; Forward-Backward Sweep Method

Full Text:

PDF

References


N. R. Aida, Kasus Covid-19 di India Kembali Naik, Apa Penyebabnya). Jakarta: https://www.kompas.com/tren/read/2022/06/11/153000365/kasus-covid-19-diindia-kembali-naik-apa-penyebabnya-?page=all, 11 June 2022.

M. Kamrujjaman, P. Saha, M. S. Islam, and U. Ghosh, “Dynamics of seir model: A case study of covid-19 in italy,” Results in Control and Optimization, vol. 7, no. 100119, pp. 1–13, 2022. DOI: 10.1016/j.rico.2022.100119

M. A. Zaitri, C. J. Silva, and D. F. M. Torres, “Stability analysis of delayed covid-19 models,” Axioms, vol. 11, no. 400, pp. 1–21, 2022. DOI: 10.3390/axioms11080400

H. Al-Qadi and M. B. Yaghoub, “Incorporating global dynamics to improve the accuracy of disease models: Example of a covid-19 sir model,” Plos One, vol. 17, no. 4, pp. 1–15, 2022. DOI: 10.1371/journal.pone.0265815

A. K. Sinha, N. Namdev, and P. Shende, “Mathematical modeling of the outbreak of covid-19,” Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 11, no. 5, pp. 1–19, 2022. DOI: 10.1007/s13721-021-00350-2

O. J. Peter, H. S. Panigoro, A. Abidemi, M. M. Ojo, and F. A. Oguntolu, “Mathematical Model of COVID-19 Pandemic with Double Dose Vaccination,” Acta Biotheoretica, vol. 71, no. 2, p. 9, 2023, DOI: 10.1007/s10441-023-09460-y

I. Darti, A. Suryanto, H. S. Panigoro, and H. Susanto, “Forecasting COVID-19 Epidemic in Spain and Italy Using A Generalized Richards Model with Quantified Uncertainty,” Communication in Biomathematical Sciences, vol. 3, no. 2, pp. 90–100, 2020, DOI: 10.5614/cbms.2020.3.2.1

J. K. K. Asamoah, M. Owusu, Z. Jin, F. T. Oduro, A. Abidemi, and E. O. Gyasi, “Global stability and cost-effectiveness analysis of COVID-19 considering the impact of the environment: using data from Ghana,” Chaos, Solitons & Fractals, vol. 140, pp. 1–9, 2020. DOI: 10.1016/j.chaos.2020.110103

J. K. K. Asamoah, Z. Jina, G. Q. Suna, B. Seidu, E. Yankson, A. Abidemi, F. T. Odurog, S. E. Moore, and E. Okyere, “Sensitivity assessment and optimal economic evaluation of a new covid-19 compartmental epidemic model with control interventions,” Chaos, Solitons and Fractals, vol. 146, no. 110885, pp. 1–19, 2021. DOI: 10.1016/j.chaos.2021.110885

R. Resmawan and L. Yahya, “Sensitivity analysis of mathematical model of coronavirus disease (covid-19) transmission,” CAUCHY –Jurnal Matematika Murni dan Aplikasi, vol. 6, no. 2, pp. 91–99, 2020. DOI: 10.18860/ca.v6i2.9165

A. I. Abioye, O. J. Peter, H. A. Ogunseye, F. A. Oguntolu, K. Oshinubi, A. A. Ibrahim, and I. Khan, “Mathematical model of covid-19 in Nigeria with optimal control,” Results in Physics, vol. 28, no. 104598, pp. 1–10, 2021. DOI: 10.1016/j.rinp.2021.104598

D. Aldila, M. Shahzad, S. H. A. Khoshnaw, M. Ali, F. Sultan, A. Islamilova, Y. R. Anwar, and B. M. Samiadji, “Optimal control problem arising from covid-19 transmission model with rapid-test,” Results in Physics, vol. 37, no. 105501, pp. 1–16, 2022. DOI: 10.1016/j.rinp.2022.105501

H. T. Alemneh and N. Y. Alemu, “Mathematical modeling with optimal control analysis of social media addiction,” Infectious Disease Modelling, vol. 6, pp. 405–419, 2021. DOI: 10.1016/j.idm.2021.01.011

M. S. Goudiaby, L. D. Gning, M. L. Diagne, B. M. Dia, H. Rwezaura, and J. M. Tchuenche, “Optimal control analysis of a covid-19 and tuberculosis co-dynamics model,” Informatics in Medicine Unlocked, vol. 28, no. 100849, pp. 1–13, 2022. DOI: 10.1016/j.imu.2022.100849

T. Hussain, M. Ozair, F. Ali, S. U. Rehman, T. A. Assiri, and E. E. Mahmoud, “Sensitivity analysis and optimal control of covid-19 dynamics based on seiqr model,” Results in Physics, vol. 22, no. 103956, pp. 1–10, 2021. DOI: 10.1016/j.rinp.2021.103956

D. Kada, A. Kouidere, O. Balatif, M. Rachik, and E. H. Labriji, “Mathematical modelling of the spread of covid-19 among different age groups in Morocco : Optimal control approach for intervention strategies,” Chaos, Solitons And Fractals, vol. 141, no. 110437, pp. 1–13, 2020. DOI: 10.1016/j.chaos.2020.110437

A. Kouidere, D. Kada, O. Balatif, M. Rachik, and M. Naim, “Optimal control approach of a mathematical modelling 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 And Fractals, vol. 142, no. 110438, pp. 1–13, 2021. DOI: 10.1016/j.chaos.2020.110438

R. P. Kumar, S. Basu, P. K. Santra, D. Ghosh, and G. S. Mahapatra, “Optimal control design incorporating vaccination and treatment on six compartment pandemic dynamical system,” Results in Control and Optimization, vol. 7, no. 100115, pp. 1–20, 2022. DOI: 10.1016/j.rico.2022.100115

M. M. Ojo, T. O. Benson, O. J. Peter, and E. F. D. Goufo, “Nonlinear optimal control strategies for a mathematical model of covid-19 and influenza coinfection,” Physica A: Statistical Mechanics and its Applications, vol. 607, no. 128173, pp. 1–27, 2022. DOI: 10.1016/j.physa.2022.128173

L. Zhang, S. Ullah, B. Al Alwan, A. Alshehri, and W. Sumelka, “Mathematical assessment of constant and time-dependent control measures on the dynamics of the novel coronavirus: An application of optimal control theory,” Results in Physics, vol. 31, no. 104971, pp. 1–9, 2021. DOI: 10.1016/j.rinp.2021.104971

M. A. Zaitria, M. O. Bibi, and D. F. M. Torres, “Transport and optimal control of vaccination dynamics for covid-19,” Alexandria Engineering Journal, vol. 60, pp. 2875–2884, 2021. DOI: 10.1016/B978-0-32-390504-6.00007-3

Z. H. Shen, Y. M. Chu, M. A. Khan, S. Muhammad, O. A. Al-Hartomy, and M. Higazy, “Mathematical modeling and optimal control of the covid-19 dynamics,” Results in Physics, vol. 31, no. 105028, pp. 1–9, 2022. DOI: 10.1016/j.rinp.2021.105028

N. H. Sweilam, S. M. AL-Mekhlafi, and T. M. Al-Ajami, “Optimal control of hybrid variable-order fractional coronavirus (2019-ncov) mathematical model; numerical treatments,” Ecological Complexity, vol. 49, no. 100983, pp. 1–12, 2022. DOI: 10.1016/j.ecocom.2022.100983

N. Ringa, M. L. Diagne, H. Rwezaura, A. Omamee, S. Y. Tchoumi, and J. M. Tchuenche, “Hiv and covid-19 co-infection: A mathematical model and optimal control,” Informatics in Medicine Unlocked, vol. 31, no. 100978, pp. 1–17, 2022. DOI: 10.1016/j.imu.2022.100978

T. Li and Y. Guo, “Modeling and optimal control of mutated covid-19 (delta strain) with imperfect vaccination,” Chaos, Solitons and Fractals, vol. 156, no. 111825, pp. 1–19, 2022. DOI: 10.1016/j.chaos.2022.111825

J. M. Awel, E. Numfor, R. Zhao, and S. Lenhart, “A new mathematical model studying imperfect vaccination: Optimal control analysis,” Journal of Mathematical Analysis and Applications, vol. 500, no. 125132, pp. 1–32, 2021. DOI: 10.1016/j.jmaa.2021.125132

I. A. Baba, B. A. Nasidi, D. Baleanu, and S. H. Saadi, “A mathematical model to optimize the available control measures of covid–19,” Ecological Complexity, vol. 46, no. 100930, pp. 1–10, 2022. DOI: 10.1016/j.ecocom.2021.100930

P. Wintachi and K. Prathom, “Stability analysis of seir model related to efficiency of vaccines for covid-19 situation,” Heliyon, vol. 7, no. e06812, pp. 1–7, 2021. DOI: 10.1016/j.heliyon.2021.e06812

Y. Deressa, C. T.and Mussa and G. F. Duressa, “Optimal control and sensitivity analysis for transmission dynamics of coronavirus,” Results in Physics, vol. 19, no. 103642, pp. 1–14, 2020. DOI: 10.1016/j.rinp.2020.103642

S. Djaoue, G. G. Kolaye, H. Abboubakar, A. A. A. Ari, and I. Damakoa, “Mathematical modeling, analysis and numerical simulation of the covid-19 transmission with mitigation of control strategies used in Cameroon,” Chaos, Solitons and Fractals, vol. 139, no. 110281, pp. 1–15, 2020. DOI: 10.1016/j.chaos.2020.110281

S. P. Gatyeni, C. W. Chukwu, F. Chirove, Fatmawati, and F. Nyabadza, “Application of optimal control to the dynamics of covid-19 disease in south Africa,” Scientific African, vol. 16, no. e01268, pp. 1–15, 2022. DOI: 10.1016/j.sciaf.2022.e01268

M. A. Sanchez and S. M. Blower, “Uncertainty and sensitivity analysis of the basic reproductive rate: tuberculosis as an example,” American Journal of Epidemiology, vol. 145, no. 12, pp. 1127–1137, 1997. DOI: 10.1093/oxfordjournals.aje.a009076

T. Khan, Z. Ullah, A. N., and G. Zaman, “Modeling and control of the hepatitis b virus spreading using an epidemic model,” Chaos, Solitons Fractals, vol. 124, pp. 1–9, 2019. DOI: 10.1016/j.chaos.2019.04.033

L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze, and M. E. F., Mathematical theory of optimal processes. New York, NY: USA: Gordon and Breach Science Publishers, 1986, vol. 4.

S. Lenhart and J. T. Workman, Optimal control applied to biological models, ser. Mathematical and Computational Biology Series. London: London/Boca Raton: Chapman and Hall/CRC Press, 2007. DOI: 10.1201/9781420011418

P. Van den Driessche and J. Watmough, “Reproduction numbers and subthreshold endemic equilibria for compartmental models of disease transmission,” Mathematical Biosciences, vol. 180, no. 1–2, pp. 29–48, 2002. DOI: 10.1016/S0025-5564(02)00108-6

B. I. Omede, U. B. Odionyenma, A. A. Ibrahim, and B. Bolaji, “Third wave of covid-19: mathematical model with optimal control strategy for reducing the disease burden in Nigeria,” International Journal of Dynamics and Control, vol. 11, pp. 411–427, 2023. DOI: 10.1007/s40435-022-00982-w




DOI: https://doi.org/10.34312/jjbm.v4i1.19025

Copyright (c) 2023 Firmansyah Firmansyah, Yulita Molliq Rangkuti

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


Jambura Journal of Biomathematics (JJBM) has been indexed by:


                          EDITORIAL OFFICE OF JAMBURA JOURNAL OF BIOMATHEMATICS

 Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Negeri Gorontalo
Jl. Prof. Dr. Ing. B. J. Habibie, Moutong, Tilongkabila, Kabupaten Bone Bolango 96554, Gorontalo, Indonesia
 Email: editorial.jjbm@ung.ac.id
 +6281356190818 (Call/SMS/WA)
 Jambura Journal of Biomathematics (JJBM) by Department of Mathematics Universitas Negeri Gorontalo is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.  Powered by Public Knowledge Project OJS.