Revealing the complex dynamics of monkeypox epidemics in heterogeneous networks by the evolutionary game theory.

Abstract

Gaining insight into the mechanisms of zoonotic disease transmission in both animal and human populations is essential for implementing effective measures to control the disease spread and mitigate its impact. This paper employs an evolutionary game theory framework to analyze the intricate dynamics of Monkeypox (mpox) epidemics across diverse networks, including scale-free and random regular networks with four network settings (BA-BA, ER-ER, BA-ER, and ER-BA) in both humans and animals. We investigate how individual behaviors and interactions influence the spread of diseases in different populations by combining network structures with evolutionary game dynamics. The results of our research reveal complex patterns, including the emergence of super-spreaders who transmit the disease to numerous others and the impact of the network structure on the disease's persistence and transmission. Furthermore, we demonstrate the practicality of this method in clarifying crucial elements that drive the spatial and temporal expansion of mpox, providing a valuable understanding of the efficacy of focused intervention strategies. Our work emphasizes the importance of multidisciplinary approaches in understanding the complex dynamics of infectious diseases and informing public health responses.