Overview:

Public health authorities face a critical challenge: accurately interpreting diagnostic test results to isolate truly infected individuals while avoiding unnecessary quarantine of healthy persons. When a test returns a positive result, can it be reliably concluded that the individual is infected? Conversely, does a negative result definitively confirm an infection-free status?

This session addresses the statistical underpinnings of these pressing questions. We will explore why the outcome of a single test can be misleading and how Bayesian inference provides a more robust framework for decision-making. Key policy questions, such as the utility and interpretation of mass population testing, will be examined through this analytical lens.

Why you should Attend:

Attend this webinar to acquire a practical understanding of how to apply Bayesian statistical methods to correctly interpret diagnostic test results, both for individual cases and at a population level. This knowledge is essential for developing effective testing strategies and public health policies.

The session will provide clear, data-driven answers to critical questions:

• What is the actual probability of infection after a single positive or negative test?

• How does repeating a test change the predictive value of the results?

• What are the implications of test sensitivity and specificity for mass testing initiatives?

• How should authorities use population testing data to guide further action?

Areas Covered in the Session:

• Bayesian interpretation of a single diagnostic test result

• Applying Bayesian updating to two or more consecutive tests

• Calculating and applying Positive Predictive Value (PPV) and Negative Predictive Value (NPV)

• Comparative analysis of COVID-19 test types: RT-PCR vs. Rapid Antigen Tests (Ag-RDTs)

• The critical concepts of diagnostic test sensitivity and specificity

• Refining probability estimates with repeated testing and updated prevalence rates (priors)

Who Will Benefit:

• Nursing Managers and Chief Nursing Officers

• Directors and Vice Presidents of Quality Improvement

• Healthcare Operations and Administration Leaders

• Public Health Officials and Policy Advisors involved in testing strategy