Managing Infectious Diseases Under Quiescence

Quiescence is added to the Susceptible-Infectious-Recovered (SIR) model with demography in this work. To investigate the consequences of Quiescence in the infection process in more depth, we use stochastic simulations on the stochastic version of the model we built. This method provides a more accurate picture of the dynamics of infectious diseases by considering the inherent randomness in the disease processes. We examine the effects of Quiescence on the number of infected people using simulations. The results, presented in histograms depicting the distribution of infected individuals, reveal a notable trend: the mean number of infected individuals is higher when Quiescence is incorporated into the dynamics. These findings emphasize the dynamic influence of Quiescence on infectious disease spread. The higher mean number of infections during periods of Quiescence highlights the need for public health strategies that are flexible enough to focus interventions during these times to reduce the possibility of an increase in infections.