SIR dynamics in random networks with heterogeneous connectivity

TL;DR

This paper introduces a novel system of three nonlinear ODEs using probability generating functions to model SIR epidemic dynamics on random networks with heterogeneous degree distributions, capturing the influence of network structure.

Contribution

It develops a network-centric modeling approach for SIR epidemics that accurately incorporates degree distribution effects and provides new insights into epidemic thresholds and final sizes.

Findings

Degree distribution significantly affects epidemic spread and final size.

Power law networks exhibit rapid initial spread but smaller final outbreaks.

The model aligns well with stochastic simulations.

Abstract

Random networks with specified degree distributions have been proposed as realistic models of population structure, yet the problem of dynamically modeling SIR-type epidemics in random networks remains complex. I resolve this dilemma by showing how the SIR dynamics can be modeled with a system of three nonlinear ODE's. The method makes use of the probability generating function (PGF) formalism for representing the degree distribution of a random network and makes use of network-centric quantities such as the number of edges in a well-defined category rather than node-centric quantities such as the number of infecteds or susceptibles. The PGF provides a simple means of translating between network and node-centric variables and determining the epidemic incidence at any time. The theory also provides a simple means of tracking the evolution of the degree distribution among susceptibles or infecteds. The equations are used to demonstrate the dramatic effects that the degree distribution plays on the final size of an epidemic as well as the speed with which it spreads through the population. Power law degree distributions are observed to generate an almost immediate expansion phase yet have a smaller final size compared to homogeneous degree distributions such as the Poisson. The equations are compared to stochastic simulations, which show good agreement with the theory. Finally, the dynamic equations provide an alternative way of determining the epidemic threshold where large-scale epidemics are expected to occur, and below which epidemic behavior is limited to finite-sized outbreaks.