How does media coverage affect a COVID-19 pandemic model with direct and indirect transmission?

Volume 35, Issue 2, pp 169--181 https://dx.doi.org/10.22436/jmcs.035.02.04

Publication Date: May 08, 2024 Submission Date: December 13, 2023 Revision Date: March 15, 2024 Accteptance Date: March 30, 2024

Download PDF

Download XML

• Export citations
  • CITATIONS & REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • ARTICLE CITATION
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)

• 37 Downloads
• 115 Views

Authors

A. A. Thirthar - Department of Studies and Planning, University of Fallujah, Anbar, Iraq. Sh. Jawad - Department of Mathematics, College of Science, University of Baghdad, Baghdad, Iraq. K. Shah - Department of Mathematics and Sciences, Prince Sultan University, P.O. Box 66833, 11586 Riyadh, Saudi Arabia. Th. Abdeljawad - Department of Mathematics and Sciences, Prince Sultan University, P.O. Box 66833, 11586 Riyadh, Saudi Arabia.

Abstract

In this paper, a compartmental differential epidemic model of COVID-19 pandemic transmission is constructed and analyzed that accounts for the effects of media coverage. The model can be categorized into eight distinct divisions: susceptible individuals, exposed individuals, quarantine class, infected individuals, isolated class, infectious material in the environment, media coverage, and recovered individuals. The qualitative analysis of the model indicates that the disease-free equilibrium point is asymptotically stable when the basic reproduction number \(R_0\) is less than one. Conversely, the endemic equilibrium is globally asymptotically stable when \(R_0\) is bigger than one. In addition, a sensitivity analysis is conducted to determine which model parameters impact the fundamental reproduction number most. Finally, some numerical simulations are implemented to reinforce the theoretical part. The results of this study indicate that media coverage may serve as a viable strategy to impede the transmission of Covid-19.

Share and Cite

Keywords

• Covid-19
• dynamical systems
• stability
• numerical simulation

MSC

•  37Dxx
•  34Dxx
•  76Fxx

References

• [1] M. S. Abdo, A. S. Alghonaim, B. A. Essam, Public perception of COVID-19’s global health crisis on Twitter until 14 weeks after the outbreak, Digit. Scholarsh. Humanit., 36 (2021), 509–524
  • View Article
  • Google Scholar


• [2] M. Ahmed, S. Jawad, The role of antibiotics and probiotics supplements on the stability of gut flora bacteria interactions, Commun. Math. Biol. Neurosci., 2023 (2023), 1–20
  • View Article
  • Google Scholar


• [3] H. Ahmad, T. A. Khan, I. Ahmad, P. S. Stanimirovi´c, Y.-M. Chu, A new analyzing technique for nonlinear time fractional Cauchy reaction diffusion model equations, Results Phys., 19 (2020),
  • View Article
  • Google Scholar


• [4] H. Ahmad, A. R. Seadawy, T. A. Khan, Study on numerical solution of dispersive water wave phenomena by using a reliable modification of variational iteration algorithm, Math. Comput. Simulation, 177 (2020), 13–23
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [5] M. Al Nuaimi, S. Jawad, Persistence and bifurcation analysis among four species interactions with the influence of competition, predation and harvesting, Iraqi J. Sci., 64 (2023), 1369–1390
  • View Article
  • Google Scholar


• [6] O. Bazighifan, H. Ahmad, S.-W. Yao, New oscillation criteria for advanced differential equations of fourth order, Mathematics, 8 (2020), 1–10
  • View Article
  • Google Scholar


• [7] C. Castillo-Chavez, On the computation of R, and its role on global stability Carlos Castillo-Chavez, Zhilan Feng, and Wenzhang Huang, Springer, New York (2002)
  • View Article
  • Google Scholar


• [8] A. Chafekar, B. C. Fielding, MERS-CoV: understanding the latest human coronavirus threat, Viruses, 10 (2018), 1–22
  • View Article
  • Google Scholar


• [9] R. Chen, W. Liang, M. Jiang, W. Guan, C. Zhan, T. Wang, C. Tang, L. Sang, J. Liu, Z. Ni, Y. Hu, L. Liu, H. Shan, C. Lei, Y. Peng, L. Wei, Y. Liu, Y. Hu, P. Peng, J. Wang, N. Zhong, Risk factors of fatal outcome in hospitalized subjects with coronavirus disease 2019 from a nationwide analysis in China, Chest, 158 (2020), 97–105
  • View Article
  • Google Scholar


• [10] M. Cinelli, W. Quattrociocchi, A. Galeazzi, C. M. Valensise, E. Brugnoli, A. L. Schmidt, P. Zola, F. Zollo, A. Scala, The COVID-19 social media infodemic, Sci. Rep., 10 (2020), 1–10
  • View Article
  • Google Scholar


• [11] D. Cucinotta, M. Vanelli, WHO declares COVID-19 a pandemic, Acta Biomed., 91 (2020), 157–160
  • View Article
  • Google Scholar


• [12] A. Devi, A. Kumar, D. Baleanu, A. Khan, On stability analysis and existence of positive solutions for a general non-linear fractional differential equations, Adv. Difference Equ., 2020 (2020), 16 pages
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [13] A. Goel, L. Gupta, Social media in the times of COVID-19, J. Clin. Rheumatol., 26 (2020), 220–223
  • View Article
  • Google Scholar


• [14] Z.-D. Guo, Z.-Y. Wang, S.-F. Zhang, X. Li, L. Li, C. Li, Y. Cui, R.-B. Fu, Y.-Z. Dong, X.-Y. Chi, M.-Y. Zhang, K. Liu, C. Cao, B. Liu, K. Zhang, Y.-W. Gao, B. Lu, W. Chen, Aerosol and surface distribution of severe acute respiratory syndrome coronavirus 2 in hospital wards, Wuhan, China, 2020, Emerg. Infect. Dis., 26 (2020), 1586–1591
  • View Article
  • Google Scholar


• [15] S. K. Hassan, S. R. Jawad, The Effect of Mutual Interaction and Harvesting on Food Chain Model, Iraqi J. Sci., (2022), 2641–2649
  • View Article
  • Google Scholar


• [16] S. Jawad, S. K. Hassan, Bifurcation analysis of commensalism intraction and harvisting on food chain model, Brazilian J. Biometrics, 41 (2023), 218–233
  • View Article
  • Google Scholar


• [17] S. Jawad, M. Winter, Z.-A. S. A. Rahman, Y. I. A. Al-Yasir, A. Zeb, Dynamical behavior of a cancer growth model with chemotherapy and boosting of the immune system, Mathematics, 11 (2023), 1–16
  • View Article
  • Google Scholar


• [18] H. Khan, F. Ahmad, O. Tunc¸, M. Idrees, On fractal-fractional Covid-19 mathematical model, Chaos Solitons Fractals, 157 (2022), 11 pages
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [19] H. Khan, J. F. G´omez-Aguilar, A. Alkhazzan, A. Khan, A fractional order HIV-TB coinfection model with nonsingular Mittag-Leffler law, Math. Methods Appl. Sci., 43 (2020), 3786–3806
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [20] F. Krammer, SARS-CoV-2 vaccines in development, Nature, 586 (2020), 516–527
  • View Article
  • Google Scholar


• [21] Q.-X. Long, X.-J. Tang, Q.-L. Shi, Q. Li, H.-J. Deng, J. Yuan, J.-L. Hu, W. Xu, Y. Zhang, F.-J. Lv, K. Su, F. Zhang, J. Gong, B.Wu, X.-M. Liu, J.-J. Li, J.-F. Qiu, J. Chen, A.-L. Huang, Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections, Nat. Med., 26 (2020), 1200–1204
  • View Article
  • Google Scholar


• [22] I. Masti, K. Sayevand, H. Jafari, On epidemiological transition model of the Ebola virus in fractional sense, J. Appl. Anal. Comput., 14 (2024), 1625–1647
  • View Article
  • MathSciNet
  • Google Scholar


• [23] I. Masti, K. Sayevand, H. Jafari, On analyzing two dimensional fractional order brain tumor model based on orthonormal Bernoulli polynomials and Newton’s method, Int. J. Optim. Control. Theor. Appl. IJOCTA, 14 (2024), 12–19
  • View Article
  • MathSciNet
  • Google Scholar


• [24] R. K. Naji, A. A. Thirthar, Stability and bifurcation of an SIS epidemic model with saturated incidence rate and treatment function, Iran. J. Math. Sci. Inform., 15 (2020), 129–146
  • View Article
  • Google Scholar
  • MATH


• [25] K. Sayevand, V. Moradi, A robust computational framework for analyzing fractional dynamical systems, Discrete Contin. Dyn. Syst. Ser. S, 14 (2021), 3763–3783
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [26] A. G. M. Selvam, J. Alzabut, D. A. Vianny, M. Jacintha, F. B. Yousef, Modeling and stability analysis of the spread of novel coronavirus disease COVID-19, Int. J. Biomath., 14 (2021), 34 pages
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [27] Z. Shahid, R. Kalayanamitra, B. McClafferty, D. Kepko, D. Ramgobin, R. Patel, C. S. Aggarwal, R. R. Vunnam, N. Sahu, D. Bhatt, K. Jones, R. Golamari, R. Jain, COVID-19 and older adults: what we know, J. Am. Geriatr. Soc., 68 (2020), 926–929
  • View Article
  • Google Scholar


• [28] R. N. Shalan, R. Shireen, A. H. Lafta, Discrete an SIS model with immigrants and treatment, J. Interdiscip. Math., 24 (2021), 1201–1206
  • View Article
  • Google Scholar


• [29] H. Tajadodi, A. Khan, J. F. G´omez-Aguilar, H. Khan, Optimal control problems with Atangana-Baleanu fractional derivative, Optimal Control Appl. Methods, 42 (2021), 96–109
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [30] A. A. Thirthar, A mathematical modelling of a plant-herbivore community with additional effects of food on the environment, Iraqi J. Sci., 64 (2023), 3551–3566
  • View Article
  • Google Scholar


• [31] A. A. Thirthar, H. Abboubakar, A. Khan, T. Abdeljawad, Mathematical modeling of the COVID-19 epidemic with fear impact, AIMS Math., 8 (2023), 6447–6465
  • View Article
  • MathSciNet
  • Google Scholar


• [32] A. A. Thirthar, S. J. Majeed, K. Shah, T. Abdeljawad, The dynamics of an aquatic ecological model with aggregation, Fear and Harvesting Effects, AIMS Math., 7 (2022), 18532–18552
  • View Article
  • MathSciNet
  • Google Scholar


• [33] A. A. Thirthar, R. K. Naji, F. Bozkurt, A. Yousef, Modeling and analysis of an SI1I2R epidemic model with nonlinear incidence and general recovery functions of I1, Chaos Solitons Fractals, 145 (2021), 1–9
  • View Article
  • Google Scholar


• [34] A. A. Thirthar, P. Panja, A. Khan, M. A. Alqudah, T. Abdeljawad, An ecosystem model with memory effect considering global warming phenomena and an exponential fear function, Fractals, 31 (2023), 1–29
  • View Article
  • Google Scholar


• [35] A. A. Thirthar, N. Sk, B. Mondal, M. A. Alqudah, T. Abdeljawad, Utilizing memory effects to enhance resilience in disease-driven prey-predator systems under the influence of global warming, J. Appl. Math. Comput., 69 (2023), 4617–4643
  • View Article
  • MathSciNet
  • Google Scholar


• [36] S.-F. Tsao, H. Chen, T. Tisseverasinghe, Y. Yang, L. Li, Z. A. Butt, What social media told us in the time of COVID-19: a scoping review, The Lancet Digital Health, 3 (2021), e175–e194
  • View Article
  • Google Scholar


• [37] World Health Organization, Coronavirus disease (COVID-19) Virtual Press conference transcript, 12 October, ((2020)),
  • View Article


• [38] A. Yousef, A. A. Thirthar, A. L. Alaoui, P. Panja, T. Abdeljawad, The hunting cooperation of a predator under two prey’s competition and fear-effect in the prey-predator fractional-order model, AIMS Math., 7 (2022), 5463–5479
  • View Article
  • MathSciNet
  • Google Scholar