Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Statistical Method to Detect Tuberculosis Outbreaks among Endemic Clusters in a Low-Incidence Setting.

Emerging infectious diseases - 1 Mar 2018

Althomsons Sandy P, Hill Andrew N, Harrist Alexia V, France Anne Marie, Powell Krista M, Posey James E, Cowan Lauren S, Navin Thomas R

Abstract excerpt

We previously reported use of genotype surveillance data to predict outbreaks among incident tuberculosis clusters. We propose a method to detect possible outbreaks among endemic tuberculosis clusters. We detected 15 possible outbreaks, of which 10 had epidemiologic data or whole-genome sequencing results. Eight outbreaks were corroborated.

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Disease Outbreaks
Share to Genome, Bacterial
Share to Incidence

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Models, Statistical

Share to Molecular Epidemiology

Share to Mycobacterium tuberculosis

Share to Tuberculosis

Share to United States

Source

Open in PubMed Open full text

Identifiers

PMID: 29460749
DOI: 10.3201/eid2403.171613
PMCID: PMC5823347

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Statistical Method to Detect Tuberculosis Outbreaks among Endemic Clusters in a Low-Incidence Setting.PMID 29460749Read ↗

Using routinely reported tuberculosis genotyping and surveillance data to predict tuberculosis outbreaks.Semantic proximity Read ↗

Automated bioinformatic pipeline for unbiased detection of tuberculosis transmission clusters: Real-time impact and retrospective insightsSemantic proximity Read ↗

Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters.Semantic proximity Read ↗

Define SNP thresholds for delineation of tuberculosis transmissions using whole-genome sequencing.Semantic proximity Read ↗

An overview of tuberculosis outbreaks reported in the years 2011-2020.Semantic proximity Read ↗

Tracking a tuberculosis outbreak over 21 years: strain-specific single-nucleotide polymorphism typing combined with targeted whole-genome sequencing.Semantic proximity Read ↗

An overview of tuberculosis outbreaks reported in the years 2011-2020Semantic proximity Read ↗

Molecular surveillance for large outbreaks of tuberculosis in the United States, 2014-2018.Semantic proximity Read ↗

Whole-genome sequencing for rapid, reliable and routine investigation of Mycobacterium tuberculosis transmission in local communitiesSemantic proximity Read ↗

A long-term refined genomic analysis of tuberculosis clusters to discriminate between ongoing transmission, reactivations or diagnostic delaysSemantic proximity Read ↗

Genomic Epidemiology of a Major Mycobacterium tuberculosis Outbreak: Retrospective Cohort Study in a Low-Incidence Setting Using Sparse Time-Series Sampling.Semantic proximity Read ↗

Defining a tuberculosis cluster or outbreak.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.