Genomic Infectious Disease Epidemiology in Partially Sampled and Ongoing Outbreaks
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TLDR
The transmission tree inference methodology is uniquely suited to use in a public health environment during real-time outbreak investigations by accounting for unsampled cases and an outbreak which may not have reached its end.Abstract:
Genomic data are increasingly being used to understand infectious disease epidemiology. Isolates from a given outbreak are sequenced, and the patterns of shared variation are used to infer which isolates within the outbreak are most closely related to each other. Unfortunately, the phylogenetic trees typically used to represent this variation are not directly informative about who infected whom-a phylogenetic tree is not a transmission tree. However, a transmission tree can be inferred from a phylogeny while accounting for within-host genetic diversity by coloring the branches of a phylogeny according to which host those branches were in. Here we extend this approach and show that it can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of an observed transmission tree and we herein demonstrate how to do this for a large class of epidemiological models. We also demonstrate how the branch coloring approach can incorporate a variable number of unique colors to represent unsampled intermediates in transmission chains. The resulting algorithm is a reversible jump Monte-Carlo Markov Chain, which we apply to both simulated data and real data from an outbreak of tuberculosis. By accounting for unsampled cases and an outbreak which may not have reached its end, our method is uniquely suited to use in a public health environment during real-time outbreak investigations. We implemented this transmission tree inference methodology in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.read more
Citations
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Towards a genomics-informed, real-time, global pathogen surveillance system.
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Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues.
Conor J. Meehan,Galo A. Goig,Thomas Kohl,Lennert Verboven,Anzaan Dippenaar,Matthew Ezewudo,Maha R. Farhat,Jennifer L. Guthrie,Kris Laukens,Paolo Miotto,Boatema Ofori-Anyinam,Viola Dreyer,Philip Supply,Anita Suresh,Christian Utpatel,Dick van Soolingen,Yang Zhou,Philip Ashton,Daniela Brites,Daniela Brites,Andrea M. Cabibbe,Bouke C. de Jong,Margaretha de Vos,Fabrizio Menardo,Fabrizio Menardo,Sebastien Gagneux,Sebastien Gagneux,Qian Gao,Tim H. Heupink,Qingyun Liu,Chloé Loiseau,Chloé Loiseau,Leen Rigouts,Timothy C. Rodwell,Elisa Tagliani,Timothy M Walker,Robin M. Warren,Yanlin Zhao,Matteo Zignol,Marco Schito,Jennifer L. Gardy,Daniela Maria Cirillo,Stefan Niemann,Iñaki Comas,Annelies Van Rie +44 more
TL;DR: The current landscape of WGS pipelines and applications, and best practices for M.tuberculosis WGS, are outlined, including standards for bioinformatics pipelines, curated repositories of resistance-causing variants, phylogenetic analyses, quality control and standardized reporting.
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When are pathogen genome sequences informative of transmission events
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Bayesian inference of ancestral dates on bacterial phylogenetic trees.
TL;DR: This work proposes a new Bayesian methodology to construct dated phylogenies which is specifically designed for bacterial genomics, and considers that the phylogenetic relationships between the genomes have been previously evaluated using a standard phylogenetic method, which makes this methodology much faster and scalable.
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PHYLOSCANNER: Inferring Transmission from Within- and Between-Host Pathogen Genetic Diversity.
Chris Wymant,Chris Wymant,Matthew Hall,Matthew Hall,Oliver Ratmann,David Bonsall,Tanya Golubchik,Mariateresa de Cesare,Astrid Gall,Marion Cornelissen,Christophe Fraser,Christophe Fraser +11 more
TL;DR: A new software tool, phyloscanner, which analyses pathogen diversity from multiple infected hosts, allowing inference of the direction of transmission from sequence data alone and provides unprecedented resolution into the transmission process.
References
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