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

Sampling Bias in the Molecular Epidemiology of Tuberculosis

1 Apr 2002

Abstract excerpt

Among the goals of the molecular epidemiology of infectious disease are to quantify the extent of ongoing transmission of infectious agents and to identify host- and strain-specific risk factors for disease spread. I demonstrate the potential bias in estimates of recent transmission and the impact of risk factors for clustering by using computer simulations to reconstruct populations of tuberculosis patients and...

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 Mycobacterium research and diagnosis
Share to Tuberculosis Research and Epidemiology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 11971768
DOI: 10.3201/eid0804.000444

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Sampling Bias in the Molecular Epidemiology of TuberculosisPMID 11971768Read ↗

Influence of Sampling on Estimates of Clustering and Recent Transmission of Mycobacterium tuberculosis Derived from DNA Fingerprinting TechniquesSemantic proximity Read ↗

An updated method to estimate factors associated with tuberculosis transmission using whole genome sequencing and other additional metadataSemantic proximity Read ↗

How close is close enough? Exploring matching criteria in the estimation of recent transmission of tuberculosis.Semantic proximity Read ↗

Comparing epidemic tuberculosis in demographically distinct heterogeneous populations.Semantic proximity Read ↗

Bayesian inference of infectious disease transmission from whole genome sequence dataSemantic proximity Read ↗

Determinants of cluster distribution in the molecular epidemiology of tuberculosisSemantic proximity Read ↗

Modeling Diversity: Do Homogeneous Laboratory Strains Limit Discovery?Semantic proximity Read ↗

Using genetic data to identify transmission risk factors: statistical assessment and application to tuberculosis transmissionSemantic proximity Read ↗

Mathematical Models for the Epidemiology and Evolution of Mycobacterium tuberculosis.Semantic proximity Read ↗

Mutation of Mycobacterium tuberculosis and Implications for Using Whole-Genome Sequencing for Investigating Recent Tuberculosis Transmission.Semantic proximity Read ↗

Model-based Analysis of Tuberculosis Genotype Clusters in the United States Reveals High Degree of Heterogeneity in Transmission and State-level Differences Across California, Florida, New York, and Texas.Semantic proximity Read ↗

Mutation rates and adaptive variation among the clinically dominant clusters of Mycobacterium abscessus.Semantic proximity Read ↗

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