Impacts of TV and radio advertisements on the dynamics of an infectious disease: A modeling study. (17th December 2018)

• Record Type: Journal Article
• Authors: Misra, Arvind Kumar; Rai, Rajanish Kumar
• Is Part Of: Mathematical methods in the applied sciences. Volume 42:Number 4(2019)
• Page Start: 1262
• Page End: 1282
• Publication Date: 2018-12-17
• Subjects: forward bifurcation -- global stability -- mathematical model -- radio advertisements -- TV advertisements; Mathematics -- Periodicals; Technology -- Mathematics -- Periodicals; 519
• DOI: 10.1002/mma.5438
• Languages: English
• ISSNs: 0170-4214

Abstract

Abstract : TV and radio advertisements are widely acknowledged as important interventions in raising issues of public health care and play promising role to control the infection through propagating awareness among the individuals. In this paper, a nonlinear susceptible‐infected‐susceptible (SIS) model is proposed and analyzed to see the impacts of TV and radio advertisements on the spread of influenza epidemic. In the model formulation, it is assumed that the susceptible individuals contract infection through the direct contact with infected individuals. The information regarding the protection against the disease is propagated via TV and radio advertisements, and their growth rates are assumed to be proportional to the fraction of infected individuals. However, the growth rate of TV advertisements decreases with the increase in number of aware individuals. The information broadcasted through TV and radio advertisements induces behavioral changes among the susceptible individuals, and they form an isolated aware class. The epidemiological feasible equilibria, their stability properties, and direction of bifurcation are discussed. The expression for modified basic reproduction number is obtained. The model analysis shows that the dissemination rate of awareness among susceptible individuals due to TV and radio advertisements and baseline number of TV and radio advertisements have potential to reduce the epidemic peak and, thus, control the spread of infection. Further, the analytical findings are well supported through numerical simulation.