Systematic Framework for Interpreting Laboratory-Confirmed Infectious Disease Surveillance Data: A Pragmatic Scenario-Based Approach

In infectious disease surveillance, laboratory-confirmed case counts and test positivity (proportion of tests positive for the target pathogen) are commonly used to monitor disease activity. However, these test-based data are affected not only by disease incidence but also by testing intensity, often making interpretations difficult. To help address this challenge, we present a generalizable, systematic framework that considers the number of tests performed, number of test-positive counts, and test positivity. With explicit consideration of ascertainment bias, we explain why all 3 indicators should be used to improve data interpretation, situational assessment, and communication.