Group Testing Complexity Analysis with R Functions and Shiny App

Laboratories use group testing to test high volumes of clinical specimens for pathogens like SARS-CoV-2, West Nile, and Chlamydia trachomatis. Christopher R. Bilder proposes a new 'complexity' measure to compare algorithms beyond just expected test counts, illustrated with a SARS-CoV-2 implementation. The dataset includes PDF documentation, R functions, and a Shiny app for calculations.

Use Cases

• Comparing algorithmic complexity of group testing methods based on the proposed measure
• Evaluating trade-offs between test efficiency and implementation difficulty for SARS-CoV-2 testing
• Applying provided R functions to calculate expected tests and complexity for specific prevalence scenarios

Strengths

• Includes R functions and a Shiny app for practical application
• Focuses on a novel complexity measure for group testing algorithms
• Illustrates concepts with a SARS-CoV-2 testing implementation

Limitations

• Dataset size is only 484.4 KB, indicating limited scope
• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment

Provenance

Source

Christopher R. Bilder

Collection Method

Likely contains derived statistical analysis and software tools.

Freshness

Last updated 2026-04-22 13:45:16