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Trends Microbiol. 2014 May;22(5):282-91. doi: 10.1016/j.tim.2014.02.011. Epub 2014 Mar 22.

Supersize me: how whole-genome sequencing and big data are transforming epidemiology.

Author information

1
Boyd Orr Centre for Population and Ecosystem Health, College of Medical Veterinary and Life Sciences, University of Glasgow, G61 1QH, UK. Electronic address: rowland.kao@glasgow.ac.uk.
2
Boyd Orr Centre for Population and Ecosystem Health, College of Medical Veterinary and Life Sciences, University of Glasgow, G61 1QH, UK.
3
Medical Research Council (MRC) Centre for Virus Research, College of Medical, Veterinary and Life Sciences, University of Glasgow, G61 1QH, UK.

Abstract

In epidemiology, the identification of 'who infected whom' allows us to quantify key characteristics such as incubation periods, heterogeneity in transmission rates, duration of infectiousness, and the existence of high-risk groups. Although invaluable, the existence of many plausible infection pathways makes this difficult, and epidemiological contact tracing either uncertain, logistically prohibitive, or both. The recent advent of next-generation sequencing technology allows the identification of traceable differences in the pathogen genome that are transforming our ability to understand high-resolution disease transmission, sometimes even down to the host-to-host scale. We review recent examples of the use of pathogen whole-genome sequencing for the purpose of forensic tracing of transmission pathways, focusing on the particular problems where evolutionary dynamics must be supplemented by epidemiological information on the most likely timing of events as well as possible transmission pathways. We also discuss potential pitfalls in the over-interpretation of these data, and highlight the manner in which a confluence of this technology with sophisticated mathematical and statistical approaches has the potential to produce a paradigm shift in our understanding of infectious disease transmission and control.

KEYWORDS:

Bayesian inference; Forensic epidemiology; Mathematical modeling; Pathogen evolution; Who-infected-whom?

PMID:
24661923
DOI:
10.1016/j.tim.2014.02.011
[Indexed for MEDLINE]
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