Estimating the prevalence of two or more diseases using outcomes from multiplex group testing

Area of Science:

• Biostatistics and Public Health Surveillance
• Infectious Disease Screening
• Computational Biology

Background:

• Specimen pooling in infectious disease screening, known as group or pooled testing, offers significant cost savings over individual testing.
• Traditional group testing research primarily focused on single diseases, limiting applications for modern multiplex assays screening for multiple diseases simultaneously.
• Recent advancements proposed group testing protocols for multiplex assays, addressing case identification and test efficiency.

Purpose of the Study:

• To introduce Bayesian methods for estimating population-level disease probabilities using multiplex group testing protocols.
• To extend existing group testing methodologies to accommodate simultaneous screening for multiple infectious diseases.
• To incorporate potential test misclassification for each disease within the estimation framework.

Main Methods:

• Development and description of Bayesian statistical methods for analyzing multiplex group testing data.
• Application of methods to estimate disease prevalence for two or more diseases concurrently.
• Inclusion of a framework to handle imperfect test accuracy (sensitivity and specificity) for each disease.

Main Results:

• Demonstrated the utility of Bayesian methods for estimating population disease probabilities from multiplex group testing data.
• Illustrated the practical application using real-world chlamydia and gonorrhea testing data.
• Provided an accessible R resource for practitioners to implement the proposed estimation methods.

Conclusions:

• The proposed Bayesian approach provides a robust framework for analyzing multiplex group testing data in public health.
• These methods enhance the accuracy of disease prevalence estimation, especially when dealing with multiple infections and imperfect tests.
• The availability of an R package facilitates the adoption of advanced group testing strategies for cost-effective disease surveillance.