Abstract
Phylodynamic models are widely used in infectious disease epidemiology to infer the dynamics and structure of pathogen populations. However, these models generally assume that individual hosts contact one another at random, ignoring the fact that many pathogens spread through highly structured contact networks. We present a new framework for phylodynamics on local contact networks based on pairwise epidemiological models that track the status of pairs of nodes in the network rather than just individuals. Shifting our focus from individuals to pairs leads naturally to coalescent models that describe how lineages move through networks and the rate at which lineages coalesce. These pairwise coalescent models not only consider how network structure directly shapes pathogen phylogenies, but also how the relationship between phylogenies and contact networks changes depending on epidemic dynamics and the fraction of infected hosts sampled. By considering pathogen phylogenies in a probabilistic framework, these coalescent models can also be used to estimate the statistical properties of contact networks directly from phylogenies using likelihood-based inference. We use this framework to explore how much information phylogenies retain about the underlying structure of contact networks and to infer the structure of a sexual contact network underlying a large HIV-1 sub-epidemic in Switzerland.
Highlights
From the viewpoint of an infectious pathogen, host populations are highly structured by the physical contacts necessary for disease transmission to occur
Our models provide a statistical framework that can be used to infer network structure directly from phylogenies, which we use to estimate the properties of a sexual contact network in Switzerland from a HIV phylogeny
Our approach uses a random graph model to describe the statistical properties of a network including its degree distribution
Summary
From the viewpoint of an infectious pathogen, host populations are highly structured by the physical contacts necessary for disease transmission to occur. For pathogens whose transmission does not require intimate or sustained physical contact, random mixing models assuming contacts form instantaneously between individuals may offer a reasonable approximation to the true dynamics of person-to-person contact [1,2,3]. For pathogens like sexually-transmitted infections (STIs), transmission requires contacts that are generally more limited in number, less transient in nature, and form non-randomly based on individual behavior—resulting in host populations that are highly structured locally at the level of individuals [4,5,6,7]. Studying the structure of contact networks empirically through methods such as contact tracing is difficult and costly, meaning we often know little about the structure of contact networks underlying real-world epidemics [15, 16]
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