Informative Dorfman screening.

Area of Science:

• Biostatistics
• Epidemiology
• Infectious Disease

Background:

• Group testing (pooled testing) has historically reduced costs in screening, drug discovery, and genetics since the 1940s.
• Current decoding algorithms often overlook the heterogeneous nature of individuals, limiting efficiency.
• Accurate classification of individuals as positive or negative is crucial for effective screening.

Purpose of the Study:

• To develop novel, informative decoding algorithms for group testing that account for individual risk probabilities.
• To implement Dorfman retesting strategies tailored for heterogeneous populations.
• To minimize the expected number of tests while maintaining high screening accuracy.

Main Methods:

• Introduced "thresholding" to categorize individuals into high-risk and low-risk groups.
• Developed risk-specific decoding algorithms for each group.
• Identified optimal pool sizes to minimize expected tests.
• Applied methods to chlamydia and gonorrhea data.

Main Results:

• Achieved significant gains in testing efficiency compared to homogeneous population algorithms.
• Demonstrated virtually no loss in screening accuracy.
• Validated the ease of implementation for the new procedures.
• Successfully applied the methods to real-world infectious disease data.

Conclusions:

• Heterogeneity-aware group testing algorithms offer substantial improvements in efficiency and cost-effectiveness.
• Thresholding and risk-specific strategies enhance the performance of pooled testing.
• The developed methods provide a practical and accurate approach for infectious disease screening.