Logistic Regression Model for Bacterial Transmission Cluster Analysis

A multivariable logistic regression model assesses factors associated with bacterial transmission clusters. Each observation is a unique introduction event, classified as a transmission cluster or a singleton case. The model, created by Erkison Ewomazino Odih and last updated in May 2026, uses consensus values for predictors like sequence type, site, and antimicrobial resistance gene carriage.

Use Cases

• Identify risk factors for bacterial transmission based on sequence type and site.
• Model the association between antimicrobial resistance gene carriage and cluster formation.
• Compare transmission likelihood of common K loci against a reference category.

Strengths

• Model uses a clear binary outcome (transmission vs. no transmission) for analysis.
• Predictors include detailed categorical encodings for sequence types, K loci, and O types.
• License is permissive (CC-BY-4.0) for reuse.

Limitations

• Dataset size is very small (15.5 KB), indicating limited scope.
• Row count and specific column definitions are unknown, requiring inspection after download.
• Data may reflect bias inherent to the source studies on figshare.

Provenance

Source

figshare

Collection Method

Derived from analysis of bacterial transmission clusters, likely from genomic epidemiology studies.

Freshness

Last updated 2026-05-13 18:00:16