Analytics of Contagion in Inhomogeneous Random Social Networks

Abstract

The inhomogeneous random social network (IRSN) framework, designed to model the spread of COVID-19 and other infectious diseases, follows Einstein's dictum “that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience.'' It adopts an agent-based perspective with a sample population of size N of individuals classified into an arbitrary number of types, capturing features such as age, profession etc. An individual may become infected by their social contacts via a dose-response mechanism, whereupon they themselves can infect others. The simplicity of the framework arises because of exchangeability: the individuals of each type are modelled as agents with identically distributed random characteristics.