title: A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data.
abstract: We describe a statistical framework for reconstructing the sequence of transmission events between observed cases of an endemic infectious disease using genetic, temporal and spatial information. Previous approaches to reconstructing transmission trees have assumed all infections in the study area originated from a single introduction and that a large fraction of cases were observed. There are as yet no approaches appropriate for endemic situations in which a disease is already well established in a host population and in which there may be multiple origins of infection, or that can enumerate unobserved infections missing from the sample. Our proposed framework addresses these shortcomings, enabling reconstruction of partially observed transmission trees and estimating the number of cases missing from the sample. Analyses of simulated datasets show the method to be accurate in identifying direct transmissions, while introductions and transmissions via one or more unsampled intermediate cases could be identified at high to moderate levels of case detection. When applied to partial genome sequences of rabies virus sampled from an endemic region of South Africa, our method reveals several distinct transmission cycles with little contact between them, and direct transmission over long distances suggesting significant anthropogenic influence in the movement of infected dogs.
authors.affiliation: Department of Microbiology and Plant Pathology, University of Pretoria, , Pretoria 0002, South Africa, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, , Glasgow G12 8QQ, UK, Directorate of Veterinary Services, KwaZulu Natal Department of Agriculture and Environmental Affairs, , Pietermaritzburg 3202, South Africa, INRA, UR546 Biostatistics and Spatial Processes, , 84914 Avignon CEDEX 9, France.
authors.forename: Nardus
authors.initials: N
authors.lastname: Mollentze
authors.forename: Louis H
authors.initials: LH
authors.lastname: Nel
authors.forename: Sunny
authors.initials: S
authors.lastname: Townsend
authors.forename: Kevin
authors.initials: K
authors.lastname: le Roux
authors.forename: Katie
authors.initials: K
authors.lastname: Hampson
authors.forename: Daniel T
authors.initials: DT
authors.lastname: Haydon
authors.forename: Samuel
authors.initials: S
authors.lastname: Soubeyrand
mesh_terms: D000818:Animals; D001483:Base Sequence; D001499:Bayes Theorem; D003141:Communicable Diseases / Q000453:epidemiology* / Q000635:transmission / Q000662:veterinary; D018562:Disease Transmission, Infectious / Q000706:statistics & numerical data*; D004285:Dogs; D004812:Epidemiologic Methods; D008954:Models, Biological; D008969:Molecular Sequence Data; D011818:Rabies / Q000453:epidemiology* / Q000635:transmission / Q000662:veterinary; D011820:Rabies virus / Q000235:genetics; D013019:South Africa; D013995:Time
keywords: endemic disease; rabies virus; spatial epidemiology; surveillance; transmission trees