Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data

Abstract

Knowledge on the transmission tree of an epidemic can provide valuable insights into disease dynamics. The transmission tree can be reconstructed by analysing either detailed epidemiological data (e.g. contact tracing) or, if sufficient genetic diversity accumulates over the course of the epidemic, genetic data of the pathogen. We present a likelihood-based framework to integrate these two data types, estimating probabilities of infection by taking weighted averages over the set of possible transmission trees. We test the approach by applying it to temporal, geographical and genetic data on the 241 poultry farms infected in an epidemic of avian influenza A (H7N7) in The Netherlands in 2003. We show that the combined approach estimates the transmission tree with higher correctness and resolution than analyses based on genetic or epidemiological data alone. Furthermore, the estimated tree reveals the relative infectiousness of farms of different types and sizes.

Sharka Epidemiology and Worldwide Management Strategies: Learning Lessons to Optimize Disease Control in Perennial Plants

“…These approaches consist of fitting epidemiological and microevolutionary models to spatiotemporal data on infected hosts and to the genetic sequence data of the pathogen (19,88,149). Up to now, these approaches have been applied only to diseases infecting mammals, but they could be advantageously transferred to sharka and other viral diseases of plants, especially to improve knowledge of dispersal function and latency duration in agricultural contexts.…”

Eight challenges for network epidemic models

“…Although further work in that direction is needed, particularly exciting is the emerging world of 'big data', in the form of genetic information (Cottam et al, 2008;Ypma et al, 2012), contact diaries (Mossong et al, 2008;van Kerckhove et al, 2013) and electronic sensors Stehlé et al, 2011), and the increased power of modern statistical methods to deal with these data (Cauchemez et al, 2011). These raise the possibility of more direct observation of epidemic networks than has previously been possible, and are discussed in However, connecting observations to model structure and parameters is far from trivial.…”

A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data

“…When coupled with a model of spatial diffusion and a model of the accumulation of point mutations over time, the probability of any two cases A and B being causally related can be calculated based on the likelihood that case A was infectious and case B was infected during the same time window, the probability that the pathogen could have dispersed from the geographical location at which case A was observed to the location at which B was observed in the time between observations, and the probability that the pathogen genetic sequence from case A could have mutated to the sequence from case B in the time between observations. This approach enables inferences to be made about epidemiological processes [6], the transmission tree [6,7], the mechanism of transmission [8] and the rate of evolution 'per transmission event' [9]. More recently, the two approaches have been combined, using a coalescent model to account for the influence of intra-host population dynamics on the structure of pathogen genetic data while reconstructing the transmission tree, thus addressing an important source of inaccuracy at high sampling intensities [10].…”