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Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease
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Didelot, Xavier, Helekal, David, Kendall, Michelle, Ribeca, Paolo and Schwartz, Russell (2023) Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease. Bioinformatics, 39 (1). btac761. doi:10.1093/bioinformatics/btac761 ISSN 1367-4803.
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Official URL: http://dx.doi.org/10.1093/bioinformatics/btac761
Abstract
Motivation
The ability to distinguish imported cases from locally acquired cases has important consequences for the selection of public health control strategies. Genomic data can be useful for this, for example using a phylogeographic analysis in which genomic data from multiple locations is compared to determine likely migration events between locations. However, these methods typically require good samples of genomes from all locations, which is rarely available.
Results
Here we propose an alternative approach that only uses genomic data from a location of interest. By comparing each new case with previous cases from the same location we are able to detect imported cases, as they have a different genealogical distribution than that of locally acquired cases. We show that, when variations in the size of the local population are accounted for, our method has good sensitivity and excellent specificity for the detection of imports. We applied our method to data simulated under the structured coalescent model and demonstrate relatively good performance even when the local population has the same size as the external population. Finally, we applied our method to several recent genomic datasets from both bacterial and viral pathogens, and show that it can, in a matter of seconds or minutes, deliver important insights on the number of imports to a geographically limited sample of a pathogen population.
Item Type: | Journal Article | ||||||||||||
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Subjects: | R Medicine > RA Public aspects of medicine | ||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Mathematics Faculty of Science, Engineering and Medicine > Science > Statistics |
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Library of Congress Subject Headings (LCSH): | Communicable diseases -- Epidemiology, Genomics, Communicable diseases -- Genetic aspects, Phylogeny, Medical geography, Communicable diseases -- Mathematical models | ||||||||||||
Journal or Publication Title: | Bioinformatics | ||||||||||||
Publisher: | Oxford University Press | ||||||||||||
ISSN: | 1367-4803 | ||||||||||||
Official Date: | January 2023 | ||||||||||||
Dates: |
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Volume: | 39 | ||||||||||||
Number: | 1 | ||||||||||||
Article Number: | btac761 | ||||||||||||
DOI: | 10.1093/bioinformatics/btac761 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Copyright Holders: | © The Author(s) 2022. Published by Oxford University Press. | ||||||||||||
Date of first compliant deposit: | 30 November 2022 | ||||||||||||
Date of first compliant Open Access: | 30 November 2022 | ||||||||||||
RIOXX Funder/Project Grant: |
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