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

Mollentze, N. , Nel, L. H., Townsend, S., le Roux, K., Hampson, K. , Haydon, D. T. and Soubeyrand, S. (2014) A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data. Proceedings of the Royal Society of London Series B: Biological Sciences, 281(1782), p. 20133251. (doi:10.1098/rspb.2013.3251)

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Publisher's URL: http://dx.doi.org/10.1098/rspb.2013.3251

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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Haydon, Professor Daniel and Hampson, Dr Katie and Townsend, Dr Sunny and Mollentze, Mr Theodorus
Authors: Mollentze, N., Nel, L. H., Townsend, S., le Roux, K., Hampson, K., Haydon, D. T., and Soubeyrand, S.
College/School:College of Medical Veterinary and Life Sciences > Institute of Biodiversity Animal Health and Comparative Medicine
Journal Name:Proceedings of the Royal Society of London Series B: Biological Sciences
Publisher:The Royal Society
ISSN:0962-8452
ISSN (Online):1471-2954
Copyright Holders:Copyright © 2014 The Authors
First Published:First published in Proceedings of the Royal Society of London Series B: Biological Sciences 281(1782)20133251
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
569041Hierarchical epidemiology: the spread and persistence of infectious diseases in complex landscapesKatie HampsonWellcome Trust (WELLCOME)095787/Z/11/ZRI BIODIVERSITY ANIMAL HEALTH & COMPMED
508041Understanding how a complex intervention works: designing large-scale vaccination programsDaniel HaydonMedical Research Council (MRC)G0901135/91914RI BIODIVERSITY ANIMAL HEALTH & COMPMED