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Investigation of Vaccination Game Approach in Spreading Covid-19 Epidemic Model With Considering the Birth and Death Rates Publisher



Vivekanandhan G1 ; Nourian Zavareh M2 ; Natiq H3 ; Nazarimehr F4 ; Rajagopal K5, 6 ; Svetec M7
Authors
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Authors Affiliations
  1. 1. Centre for Artificial Intelligence, Chennai Institute of Technology, Chennai, India
  2. 2. Department of Biomedical Engineering, Faculty of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Information Technology Collage, Imam Ja'afar Al-Sadiq University, Baghdad, 10001, Iraq
  4. 4. Department of Biomedical Engineering, Amirkabir University of Technology (Tehran polytechnic), Iran
  5. 5. Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai, India
  6. 6. Department of Electronics and Communications Engineering and University Centre for Research & Development, Chandigarh University, -140413, Mohali, Punjab, India
  7. 7. Faculty of Natural Sciences and Mathematics, University of Maribor, Koroska cesta 160, Maribor, 2000, Slovenia

Source: Chaos, Solitons and Fractals Published:2022


Abstract

In this study, an epidemic model for spreading COVID-19 is presented. This model considers the birth and death rates in the dynamics of spreading COVID-19. The birth and death rates are assumed to be the same, so the population remains constant. The dynamics of the model are explained in two phases. The first is the epidemic phase, which spreads during a season based on the proposed SIR/V model and reaches a stable state at the end of the season. The other one is the “vaccination campaign”, which takes place between two seasons based on the rules of the vaccination game. In this stage, each individual in the population decides whether to be vaccinated or not. Investigating the dynamics of the studied model during a single epidemic season without consideration of the vaccination game shows waves in the model as experimental knowledge. In addition, the impact of the parameters is studied via the rules of the vaccination game using three update strategies. The result shows that the pandemic speeding can be changed by varying parameters such as efficiency and cost of vaccination, defense against contagious, and birth and death rates. The final epidemic size decreases when the vaccination coverage increases and the average social payoff is modified. © 2022 Elsevier Ltd
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