Discover Community Create You

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

Tuberculosis Outbreaks Predicted by Characteristics of First Patients in a DNA Fingerprint Cluster

3 Apr 2008

Abstract excerpt

RATIONALE: Some clusters of patients who have Mycobacterium tuberculosis isolates with identical DNA fingerprint patterns grow faster than others. It is unclear what predictors determine cluster growth. OBJECTIVES: To assess whether the development of a tuberculosis (TB) outbreak can be predicted by the characteristics of its first two patients. METHODS: Demographic and clinical data of all culture-confirmed...

Read the complete abstract on PubMed

Topics

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

Share to Diagnosis and treatment of tuberculosis
Share to Infectious Diseases and Tuberculosis
Share to Tuberculosis Research and Epidemiology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 18388352
DOI: 10.1164/rccm.200708-1256oc

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Tuberculosis Outbreaks Predicted by Characteristics of First Patients in a DNA Fingerprint ClusterPMID 18388352Read ↗

Which patients' factors predict the rate of growth of Mycobacterium tuberculosis clusters in an urban community?Semantic proximity Read ↗

Long-term refined genomic analysis of tuberculosis clusters to distinguish between ongoing transmission, reactivations or diagnostic delays, Almería, Spain, 2003 to 2024.Semantic proximity Read ↗

A Mycobacterium tuberculosis cluster demonstrating the use of genotyping in urban tuberculosis control.Semantic proximity Read ↗

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

Risk factors associated with large clusters of tuberculosis patients determined by whole-genome sequencing in a high-tuberculosis-burden country.Semantic proximity Read ↗

Insights into the Complexity of a Dormant Mycobacterium tuberculosis Cluster Once Transmission Is Resumed.Semantic proximity Read ↗

Transmission classification model to determine place and time of infection of tuberculosis cases in an urban area.Semantic proximity Read ↗

The effect of age and study duration on the relationship between 'clustering' of DNA fingerprint patterns and the proportion of tuberculosis disease attributable to recent transmission.Semantic proximity Read ↗

Recent TB transmission, clustering and predictors of large clusters in London, 2010-2012: results from first 3 years of universal MIRU-VNTR strain typing.Semantic proximity Read ↗

Clusters of multidrug-resistant Mycobacterium tuberculosis cases, Europe.Semantic proximity Read ↗

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

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

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