ReviewTrends in microbiology2014
Supersize me: how whole-genome sequencing and big data are transforming epidemiology.
Review in Trends in microbiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
66 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Interpreting whole genome sequencing for investigating tuberculosis transmission: a systematic review.BMC medicine · 2016Pooled it
- Phylodynamic analysis ofMicrobial genomics · 2026Article
- Molecular epidemiology of Ascaris lumbricoides following multiple rounds of community-wide treatment.Nature communications · 2025Article
- ViralFlow v1.0-a computational workflow for streamlining viral genomic surveillance.NAR genomics and bioinformatics · 2024Article
- On the ever-growing functional versatility of the CRISPR-Cas13 system.Microbial biotechnology · 2024Review
- Application of Genomic Epidemiology of Pathogens to Farmed Yellowtail Fish Mycobacteriosis in Kyushu, Japan.Microbes and environments · 2024Article
- Are rabid raccoons (Evolutionary applications · 2023Article
- The tumour is in the detail: Local phylogenetic, population and epidemiological dynamics of a transmissible cancer in Tasmanian devils.Evolutionary applications · 2023Article
- Article
- Review:Frontiers in veterinary science · 2023Review
- Unraveling the epidemiology ofFrontiers in veterinary science · 2023Article
- SARS-CoV-2 Genomic Diversity in Households Highlights the Challenges of Sequence-Based Transmission Inference.mSphere · 2022Article
- Integrating genomic and epidemiologic data to accelerate progress toward schistosomiasis elimination.eLife · 2022Review
- Target-enrichment sequencing yields valuable genomic data for challenging-to-culture bacteria of public health importance.Microbial genomics · 2022Article
- Population genomics ofMicrobial genomics · 2022Article
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- Article
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- Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations.Scientific reports · 2020Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.