title: A Systematic Bayesian Integration of Epidemiological and Genetic Data.
abstract: Genetic sequence data on pathogens have great potential to inform inference of their transmission dynamics ultimately leading to better disease control. Where genetic change and disease transmission occur on comparable timescales additional information can be inferred via the joint analysis of such genetic sequence data and epidemiological observations based on clinical symptoms and diagnostic tests. Although recently introduced approaches represent substantial progress, for computational reasons they approximate genuine joint inference of disease dynamics and genetic change in the pathogen population, capturing partially the joint epidemiological-evolutionary dynamics. Improved methods are needed to fully integrate such genetic data with epidemiological observations, for achieving a more robust inference of the transmission tree and other key epidemiological parameters such as latent periods. Here, building on current literature, a novel Bayesian framework is proposed that infers simultaneously and explicitly the transmission tree and unobserved transmitted pathogen sequences. Our framework facilitates the use of realistic likelihood functions and enables systematic and genuine joint inference of the epidemiological-evolutionary process from partially observed outbreaks. Using simulated data it is shown that this approach is able to infer accurately joint epidemiological-evolutionary dynamics, even when pathogen sequences and epidemiological data are incomplete, and when sequences are available for only a fraction of exposures. These results also characterise and quantify the value of incomplete and partial sequence data, which has important implications for sampling design, and demonstrate the abilities of the introduced method to identify multiple clusters within an outbreak. The framework is used to analyse an outbreak of foot-and-mouth disease in the UK, enhancing current understanding of its transmission dynamics and evolutionary process.
authors.affiliation: Department of Ecology and Evolutionary Biology, Princeton, New Jersey, United States of America.
authors.forename: Max S Y
authors.initials: MS
authors.lastname: Lau
authors.affiliation: Biomathematics and Statistics Scotland, Edinburgh, United Kingdom.
authors.forename: Glenn
authors.initials: G
authors.lastname: Marion
authors.affiliation: Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh, United Kingdom.
authors.forename: George
authors.initials: G
authors.lastname: Streftaris
authors.affiliation: Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh, United Kingdom.
authors.forename: Gavin
authors.initials: G
authors.lastname: Gibson
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D005536:Foot-and-Mouth Disease / Q000453:epidemiology; D008954:Models, Biological; D017720:Molecular Epidemiology / Q000379:methods*