An Epidemic Model with Pro and Anti-vaccine Groups

Abstract

Here, an epidemiological model considering pro and anti-vaccination groups is proposed and analyzed. In this model, susceptible individuals can migrate between these two groups due to the influence of false and true news about safety and efficacy of vaccines. From this model, written as a set of three ordinary differential equations, analytical expressions for the disease-free steady state, the endemic steady state, and the basic reproduction number are derived. It is analytically shown that low vaccination rate and no influx to the pro-vaccination group have similar impacts on the long-term amount of infected individuals. Numerical simulations are performed with parameter values of the COVID-19 pandemic to illustrate the analytical results. The possible relevance of this work is discussed from a public health perspective.

Investigating the social dilemma of an epidemic model with provaccination and antivaccination groups: An evolutionary approach

“…However, once infected, individuals can adopt a strategy by considering the benefits accrued throughout the infection phase and the overall number of recovered members of each group. Some analytic and numerical results of an epidemic model containing the pro- and anti-vaccine groups were presented in [12] . Group behavior towards the provaccination or antivaccination groups was analyzed in [8] .…”

“…Thus, the entire human population can be split into two groups when vaccination becomes voluntary provaccination and antivaccination groups meaning getting vaccinated and not getting vaccinated respectively. The decisions of individuals toward vaccination are influenced by the overall number of infected people in each group and the total payoff of both groups’ illnesses [8] , [9] , [10] , [11] , [12] .…”

Phase Transition in the Galam’s Majority-Rule Model with Information-Mediated Independence

“…Concerning sociologists, these methods are useful to improve forecasting by means of controlled toy models that can be run multiple times and help fine-tune field studies as well [15]. In addition to the interesting properties of opinion dynamics models, per se, such dynamics have also been applied in various fields such as finance and business [16], and epidemic dynamics with the presence of conflicting opinions [17][18][19][20][21][22][23], among others [3].…”

Be Real, Do Not Be Fake: A Pilot Study on Universiti Sains Islam Malaysia Students' Fake News Literacy

“…During the COVID-19 pandemic, this news or false information spread worldwide, including in Malaysia. The safety and efficacy of vaccines are among the hottest studies of fake news during COVID-19 (Harari & Monteiro, 2022;Al-Uqdah et al, 2022;Musi et al, 2022). Studies indicate that media exposure contributes to the rise of fake news because the Internet is likely the most common source of health information (Abd-Alrazaq et al, 2020).…”