Effects of network temporality on coevolution spread epidemics in higher-order network

Abstract

Interactions between people, including pairwise and higher-order interactions, can be approximated as temporal higher-order networks, where the connections are constantly created and broken over time. Meanwhile, in nature, multiple contagion processes, such as epidemics spreading, are co-evolutionary and exhibit synergistic, competitive, and asymmetric interactions. Traditional research focused on pairwise temporal effects on the single spreading dynamics. How the network temporality affects the coevolution epidemic spread on the higher-order network remains to be investigated. This paper presents a coevolution epidemics spread model on a temporal higher-order social network, which considers synergistic, competitive, and asymmetric interactions. The temporality of the network can facilitate and inhibit the transmission dynamics of different interaction patterns are drawn through a microscopic Markov Chain approach. The intensity of epidemic infection refers to the combined effect of the epidemic’s infection rate and the promoting (or suppressing) effect of another epidemic. The network temporality promotes spread when the intensity of epidemic infection is at its maximum. In the study of synergistic spread, the network temporality is found to weaken the effect of initial infection density on outbreak thresholds. As the strength of network temporality diminishes, experimental results show that the higher the initial density, the smaller the outbreak threshold.