Investigation of COVID-19 epidemic mathematical model incorporating media coverage impact and control

Authors

  • Aqeel Ahmad Department of Mathematics, Ghazi University D G Khan 32200, Pakistan
  • Wajid Fiaz Department of Mathematics, Ghazi University D G Khan 32200, Pakistan

DOI:

https://doi.org/10.48165/jmmfc.2025.2106

Keywords:

Atangana Toukfik, Generalized incidence rate, Media coverage, Stability analysis

Abstract

 In order to estimate and control the recent corona virus disease 2019 outbreak, under me dia impact. Most countries impose stringent intervention measures to halt the spread of COVID-19. Globally, the COVID-19 epidemic has raised a lot of concerns. A non-smooth SIR system is proposed to investigate the impact of three control strategies (vaccination, therapy, and media coverage) on the spread of an infectious disease. Social media platforms like Face book, Twitter, and others have been crucial in spreading information during the COVID-19 outbreak, both factual and misleading, which has led to widespread confusion. When the susceptible population surpasses the threshold value, the traditional epidemic model comprises media coverage, linear functions that describe vaccine injection, and treatment tactics. COVID-19 model under media coverage investigated both qualitatively and quantitatively. models key proportion are also verified like boundedness, uniqueness and existence. Stability Analysis his also know development on local and global scale in order to properly manage funds and human resources and create successful disease preventive marketing efforts, organizations, institutions, enterprizes, policymakers, and educators who are in charge of infection and disease control must keep a close eye on infection rates.

Author Biography

  • Aqeel Ahmad, Department of Mathematics, Ghazi University D G Khan 32200, Pakistan

    Mathematics Research Center, Near East University, Near East Boulevard, Nicosia North Cyprus, 99138

References

Marra, C. M. (2004). Neurosyphilis. Current Neurology and Neuroscience Reports, 4, 435–440.

World Health Organization. (2009). Influenza fact sheet No. 211. April 2009.

Adepoju, K. A., & Akpan, G. E. (2017). Historical assessment of malaria hazard and mortality in Nigeria: Cases and deaths, 1955–2015. International Journal of Environmental Bioenergy, 12(1), 30–46.

Mofleh, J., & Ansari, J. (2014). Evaluation of measles surveillance systems in Afghanistan–2010. Journal of Public Health and Epidemiology, 6(11), 407–416.

Chan, N. W., Seow, T. W., & Mapjabil, J. Infectious Diseases and Pandemics.

Rollin, G., Lages, J., & Shepelyansky, D. L. (2019). World influence of infectious diseases from Wikipedia network analysis. IEEE Access, 7, 26073–26087.

Bowong, S., & Tewa, J. J. (2010). Global analysis of a dynamical model for transmission of tuberculosis with a general contact rate. Communications in Nonlinear Science and Numerical Simulation, 15(11), 3621–3631.

Jankovic, S. (2020). Current status and future perspective of coronavirus disease 2019: A review. Scripta Medica, 51(2), 101–109.

Watts, D. J. (2004). Six Degrees: The Science of a Connected Age. WW Norton & Company.

Lopez, A. D., Mathers, C. D., Ezzati, M. D., Jamison, T., & Murray, C. J. (2006). Changes in individual behavior could limit the spread of infectious diseases.

Hauer, M. K., & Sood, S. (2020). Using social media to communicate sustainable preventive measures and curtail misinformation. Frontiers in Psychology, 11, 568324.

Anwar, A., Malik, M., Raees, V., & Anwar, A. (2020). Role of mass media and public health communications in the COVID-19 pandemic. Cureus, 12(9).

Olaoye, A., & Onyenankeya, K. (2023). A systematic review of health communication strategies in Sub-Saharan Africa: 2015–2022. Health Promotion Perspectives, 13(1), 10.

Ojiso, O. M., & Nkalubo, H. The Role of Media in Disease Prevention in Uganda: A Case Study of NBS Television.

Afful-Dadzie, E., Afful-Dadzie, A., & Egala, S. B. (2023). Social media in health communication: A literature review of information quality. Health Information Management Journal, 52(1), 3–17.

Brauer, F. (2009). Mathematical epidemiology is not an oxymoron. BMC Public Health, 9, 1–11.

Neumann, G., Noda, T., & Kawaoka, Y. (2009). Emergence and pandemic potential of swine-origin H1N1 influenza virus. Nature, 459(7249), 931–939.

Public Health Agency of Canada. (2006). Highlights from the Canadian Pandemic Influenza Plan for the Health Sector: Preparing for an Influenza Pandemic, the Canadian Health Perspective.

Centers for Disease Control and Prevention (CDC). (2010). Estimates of deaths associated with seasonal influenza – United States, 1976–2007. MMWR: Morbidity Mortality Weekly Report, 59(33).

Viswanath, K., Ramanadhan, S., Kontos, E. Z., & Galea, S. (2007). Macrosocial determinants of population health. In Mass Media and Population Health: A Macrosocial View (pp. 275–294). Springer.

Muhammad, A., Syafruddin, S., Suwardi, A., Wahyuddin, N., & Wahidah, S. (2021). An SIR epidemic model for COVID-19 spread with fuzzy parameter: The case of Indonesia. Advances in Continuous and Discrete Models, 2021(1).

Wang, L., Liu, Z., & Zhang, X. (2016). Global dynamics for an age-structured epidemic model with media impact and incomplete vaccination. Nonlinear Analysis: Real World Applications, 32, 136–158.

Olorunsaiye, C. Z., Yusuf, K. K., Reinhart, K., & Salihu, H. M. (2020). COVID-19 and child vaccination: A systematic approach to closing the immunization gap. International Journal of Maternal and Child Health and AIDS, 9(3), 381.

Anderson, R. M., & May, R. M. (1985). Vaccination and herd immunity to infectious diseases. Nature, 318(6044), 323–329.

Zhai, S., Luo, G., Huang, T., Wang, X., Tao, J., & Zhou, P. (2021). Vaccination control of an epidemic model with time delay and its application to COVID-19. Nonlinear Dynamics, 106(2), 1279–1292.

Yang, J., Zhang, Q., Cao, Z., Gao, J., Pfeiffer, D., Zhong, L., & Zeng, D. D. (2021). The impact of non-pharmaceutical interventions on the prevention and control of COVID-19 in New York City. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(2).

Agaba, G. O., Kyrychko, Y. N., & Blyuss, K. B. (2017). Dynamics of vaccination in a time-delayed epidemic model with awareness. Mathematical Biosciences, 294, 92–99.

Craven, J. S. COVID Death Spread Rate. Indian Journal of Research in Pharmacy and Biotechnology, ISSN 2321-5674.

Saxena, A., Bouvier, P. A., Shamsi-Gooshki, E., Khler, J., & Schwartz, L. J. (2021). WHO guidance on ethics in outbreaks and the COVID-19 pandemic: A critical appraisal. Journal of Medical Ethics, 47(6), 367–373.

Zhong, B. L., Luo, W., Li, H. M., Zhang, Q. Q., Liu, X. G., Li, W. T., & Li, Y. (2020). Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: A quick online cross-sectional survey. International Journal of Biological Sciences, 16(10), 1745.

Mandal, M., Jana, S., Nandi, S. K., Khatua, A., Adak, S., & Kar, T. K. (2020). A model-based study on the dynamics of COVID-19: Prediction and control. Chaos, Solitons & Fractals, 136, 109889.

Yousefpour, A., Jahanshahi, H., & Bekiros, S. (2020). Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak. Chaos, Solitons & Fractals, 136, 109883.

Buonomo, B., & Della Marca, R. (2020). Effects of information-induced behavioural changes during the COVID-19 lockdowns: The case of Italy. Royal Society Open Science, 7(10), 201635.

Mahmud, M. S., Kamrujjaman, M., Jubyrea, J., & Islam, M. S. (2020). Quarantine vs social consciousness: A prediction to control COVID-19 infection. Journal of Applied Life Sciences International, 23(3), 20–27.

Agaba, G. O., Kyrychko, Y. N., & Blyuss, K. B. (2017). Mathematical model for the impact of awareness on the dynamics of infectious diseases. Mathematical Biosciences, 286, 22–30.

Atangana, A., & Akgl, A. (2021). On solutions of fractal fractional differential equations. Discrete & Continuous Dynamical Systems - Series S, 14(10).

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Published

2025-08-01

How to Cite

Investigation of COVID-19 epidemic mathematical model incorporating media coverage impact and control . (2025). Journal of Mathematical Modeling and Fractional Calculus, 2(1), 84-106. https://doi.org/10.48165/jmmfc.2025.2106