title: Network-based analysis of stochastic SIR epidemic models with random and proportionate mixing.
abstract: In this paper, we outline the theory of epidemic percolation networks and their use in the analysis of stochastic susceptible-infectious-removed (SIR) epidemic models on undirected contact networks. We then show how the same theory can be used to analyze stochastic SIR models with random and proportionate mixing. The epidemic percolation networks for these models are purely directed because undirected edges disappear in the limit of a large population. In a series of simulations, we show that epidemic percolation networks accurately predict the mean outbreak size and probability and final size of an epidemic for a variety of epidemic models in homogeneous and heterogeneous populations. Finally, we show that epidemic percolation networks can be used to re-derive classical results from several different areas of infectious disease epidemiology. In an Appendix, we show that an epidemic percolation network can be defined for any time-homogeneous stochastic SIR model in a closed population and prove that the distribution of outbreak sizes given the infection of any given node in the SIR model is identical to the distribution of its out-component sizes in the corresponding probability space of epidemic percolation networks. We conclude that the theory of percolation on semi-directed networks provides a very general framework for the analysis of stochastic SIR models in closed populations.
authors.affiliation: Departments of Epidemiology and Biostatistics, Harvard School of Public Health, 677 Huntington Ave., Boston, MA 02115, USA. ekenah@hsph.harvard.edu
authors.forename: Eben
authors.initials: E
authors.lastname: Kenah
authors.forename: James M
authors.initials: JM
authors.lastname: Robins
mesh_terms: D000818:Animals; D003141:Communicable Diseases / Q000453:epidemiology* / Q000635:transmission; D004196:Disease Outbreaks; D004198:Disease Susceptibility; D008954:Models, Biological; D011156:Population Density; D013269:Stochastic Processes