Analysis of SIR epidemic model with information spreading of awareness. (February 2019)

• Record Type: Journal Article
• Authors: Kabir, K.M. Ariful; Kuga, Kazuki; Tanimoto, Jun
• Is Part Of: Chaos, solitons and fractals. Volume 119(2019)
• Page Start: 118
• Page End: 125
• Publication Date: 2019-02
• Subjects: Social dilemma -- Epidemic -- SIR model -- Information spreading; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2018.12.017
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: We build a new analytical framework for the information spreading called "awareness" combined with the susceptible-infected-recovered (SIR) model. Our model can evaluate the effect of awareness along with a SIR epidemic model to reduce the effect of contagious disease. We obtain the phase diagram for different parameter comparison to show the effect of awareness. Abstract: The information spreading of awareness can prompt the manners of human to ease the infectious possibility and assist to recover swiftly. A dynamic system of Susceptible-Infected-Recovered (SIR) with Unaware-Aware (UA) process (SIR-UA) is newly developed by using compartment model through analytical approach with assumption of an infinite and well-mixed population. Moreover, individuals in a population can be classified into six states as unaware susceptible( SU ), aware susceptible( SA ), unaware infected( IU ), aware infected( IA ), unaware recovered( RU ), and aware recovered( RA ). Compared with previous models, the new dynamic set of equations described the more widespread situation and incorporated all possible states of Unaware-Aware (UA) with SIR process. The effect of awareness is explored carefully to show the significance on epidemic model with time steps. Consequently, the properties of parameters on the epidemic awareness model are studied to deliberate different physical situations. Finally, full phase diagrams are explored to show the epidemic sizes of susceptible and recovered individuals for various parameters.