Generalized external synchronization of networks based on clustered pandemic systems—The approach of Covid-19 towards influenza

Abstract

Real-world models, like those used in social studies, epidemiology, energy transport, engineering, and finance, are often called “multi-layer networks.” In this work, we have described a controller that connects the paths of synchronized models that are grouped together in clusters. We did this using Lyapunov theory and a variety of coupled matrices to look into the link between the groups of chaotic systems based on influenza and covid-19. Our work also includes the use of external synchrony in biological systems. For example, we have explained in detail how the pandemic disease covid-19 will get weaker over time and become more like influenza. The analytical way to get these answers is to prove a theorem, and the numerical way is to use MATLAB to run numerical simulations.

Criticality and partial synchronization analysis in Wilson-Cowan and Jansen-Rit neural mass models

“…Additionally, some analyses of market connectivity and risk spillover in stock prices by Chen et al [ 32 – 34 ] may provide insight into the interconnectedness of neural networks and their synchronization. The management of epidemics is another situation that requires an understanding of the motivations and behaviors that lead to the dynamics and synchronization of complex networks [ 35 – 38 ]. Some practical example on how the mean field applications in different fields can be explored by analyzing their synchronization are presented in [ 2 , 39 – 41 ].…”