a bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data贝叶斯推理框架重建使用流行病学和遗传数据传输树.pdf

A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data 1¤ ¨ ´ 2 ¨ 3 4 1 Marco J. Morelli , Gael Thebaud , Joel Chadœuf , Donald P. King , Daniel T. Haydon *, Samuel Soubeyrand3 1 Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom, 2 INRA, UMR BGPI, Cirad TA A-54/K, Montpellier, France, 3 INRA, UR546 Biostatistics and Spatial Processes, Avignon, France, 4 Institute for Animal Health, Pirbright, United Kingdom Abstract The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth