Note on the Complex Networks and Epidemiology Part I: Complex Networks

TL;DR

This paper reviews the fundamental concepts of complex networks and their application to epidemiology, highlighting how network structures influence disease spread and vaccination behaviors.

Contribution

It provides a comprehensive overview of complex network properties and their implications for epidemic modeling and public health strategies.

Findings

Scale-Free networks lack epidemic thresholds

High clustering affects disease transmission

Imitation behavior influences vaccination clusters

Abstract

Complex networks describe a wide range of systems in nature and society. Frequently cited examples include Internet, WWW, a network of chemicals linked by chemical reactions, social relationship networks, citation networks, etc. The research of complex networks has attracted many scientists' attention. Physicists have shown that these networks exhibit some surprising characters, such as high clustering coefficient, small diameter, and the absence of the thresholds of percolation. Scientists in mathematical epidemiology discovered that the threshold of infectious disease disappears on contact networks that following Scale-Free distribution. Researchers in economics and public health also find that the imitation behavior could lead to cluster phenomena of vaccination and un-vaccination. In this note, we will review the basic concepts of complex networks; Basic epidemic models; the development of complex networks and epidemiology.