Generalized linear models for identifying predictors of the evolutionary diffusion of viruses

Abstract

Bioinformatics and phylogeography models use viral sequence data to analyze spread of epidemics and pandemics. However, few of these models have included analytical methods for testing whether certain predictors such as population density, rates of disease migration, and climate are drivers of spatial spread. Understanding the specific factors that drive spatial diffusion of viruses is critical for targeting public health interventions and curbing spread. In this paper we describe the application and evaluation of a model that integrates demographic and environmental predictors with molecular sequence data. The approach parameterizes evolutionary spread of RNA viruses as a generalized linear model (GLM) within a Bayesian inference framework using Markov chain Monte Carlo (MCMC). We evaluate this approach by reconstructing the spread of H5N1 in Egypt while assessing the impact of individual predictors on evolutionary diffusion of the virus.

Figure 1.

Predictors of H5N1 diffusion in Egypt. Inclusion probability defined by indicator expectations E(δ), which reflects the likelihood of meaningful impact of the predictor on viral diffusion. Bayes Factor (BF) support values shown at the top of the figure and are indicated by vertical lines. Coefficient (β|δ=1) represents the contribution of each predictor, with the 95% credible interval represented by brackets.