Article,
SIR dynamics in random networks with heterogeneous connectivity
Journal of Mathematical Biology, 56 (3): 293--310 (March 2008)
Abstract
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 howthe SIR dynamics can be modeled with a system of three nonlinear ODE’s. The method makes use of the probability generatingfunction (PGF) formalism for representing the degree distribution of a random network and makes use of network-centric quantitiessuch as the number of edges in a well-defined category rather than node-centric quantities such as the number of infectedsor susceptibles. The PGF provides a simple means of translating between network and node-centric variables and determiningthe epidemic incidence at any time. The theory also provides a simple means of tracking the evolution of the degree distributionamong susceptibles or infecteds. The equations are used to demonstrate the dramatic effects that the degree distribution playson the final size of an epidemic as well as the speed with which it spreads through the population. Power law degree distributionsare observed to generate an almost immediate expansion phase yet have a smaller final size compared to homogeneous degreedistributions such as the Poisson. The equations are compared to stochastic simulations, which show good agreement with thetheory. Finally, the dynamic equations provide an alternative way of determining the epidemic threshold where large-scaleepidemics are expected to occur, and below which epidemic behavior is limited to finite-sized outbreaks.
