AN OBSERVATION OF THE PRECISE RESOLUTION OF SUSCEPTIBLE-INFECTED-RECOVERED (SIR) AND SUSCEPTIBLE-INFECTED-SUSCEPTIBLE (SIS) OUTBREAK PATTERNS

Abstract

This paper uses the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected-Susceptible (SIS) models in activity-driven networks to investigate the impact of temporal memory on the propagation of epidemics. These networks, with their non-Markovian dynamics, mimic the intricate, dynamic patterns of connectedness present in real-world systems. According to our findings, memory strongly suppresses the spread of disease in SIR models by increasing the epidemic threshold and lowering the fraction of survivors at the end of the disease. On the other hand, memory increases the diseases persistence and lowers the epidemic threshold in SIS models, increasing the proportion of infected nodes in the endemic state. Strong links within closely knit local clusters frequently reoccur, acting as reservoirs for the variation in tie strengths that cause this effect. Our results imply that memory has a dual function in the dynamics of epidemics, promoting the spread in SIS processes while inhibiting it in SIR scenarios. Itverify these findings using simulations on real temporal networks, showing how memory plays a crucial role in the course of epidemics and emphasizing the importance of taking temporal dynamics into account when modeling epidemics