Dynamics of the impact of Twitter with time delay on the spread of infectious diseases

Abstract

In this paper, a mathematical model to study the impact of Twitter in controlling infectious disease is proposed. The model includes the dynamics of “tweets” which may enhance awareness of the disease and cause behavioral changes among the public, thus reducing the transmission of the disease. Furthermore, the model is improved by introducing a time delay between the outbreak of disease and the release of Twitter messages. The basic reproduction number and the conditions for the stability of the equilibria are derived. It is shown that the system undergoes Hopf bifurcation when time delay is increased. Finally, numerical simulations are given to verify the analytical results.

Trending on Social Media: Integrating Social Media into Infectious Disease Dynamics

“…Just as in the first approach where awareness programs run by media campaigns induce behavioural changes in the susceptibles, interaction with the M class will result in an "aware" class of susceptibles who may not interact with the infected class (Misra et al 2011) or if they do, with reduced contact dependent on the media compartment (Greenhalgh et al 2015). Some researchers (Liu et al 2018;Greenhalgh et al 2015) have included a time delay in the media compartment to highlight the differences in the dynamics of information and disease spread. This time delay may account for the lag between cases of disease occurring and mounting awareness programs (Greenhalgh et al 2015) or the way information is spread via social media-not always directly but also by users forwarding tweets (Liu et al 2018).…”

“…Some researchers (Liu et al 2018;Greenhalgh et al 2015) have included a time delay in the media compartment to highlight the differences in the dynamics of information and disease spread. This time delay may account for the lag between cases of disease occurring and mounting awareness programs (Greenhalgh et al 2015) or the way information is spread via social media-not always directly but also by users forwarding tweets (Liu et al 2018). Results from these modelling efforts generally confirm that media coverage can have a significant impact on the epidemic, such as delaying the peak and reducing the severity of the outbreak (Misra et al 2011) and if multiple time delays are included may produce hopf bifurcations (Greenhalgh et al 2015).…”

There exists the “smartest” movement rate to control the epidemic rather than “city lockdown”

“…Time delay has a significant effect on the epidemic model. In the mathematical model with the influence of epidemic controlled by Twitter, Hopf bifurcation occurs when the delay increases in [21] . Liu et al.…”

Susceptible-Infected-Susceptible Epidemic Discrete Dynamic System Based on Tsallis Entropy

“…A decay ago, in the year 1927, the traditional SISE was offered [ 2 ]. After that, there established an enormous number of periodicals on SISE [ 3 , 4 , 5 ]. In overall, SISEs are considered to be homogeneously combined, which indicates that susceptible persons are infected with the same information.…”