JMIR Publications

ABSTRACT

Background:

The endemic channel is a surveillance method that presents statistical indicators and a visual representation of a disease's historical dynamics. It uses an epidemic curve to define the central tendency of cases and their expected variation within upper and lower limits, providing three levels—"Safety," "Warning," and "Epidemic"—to assess the epidemiological status of a region. Parameters include the measure of central tendency used as the Epidemiological Warning Threshold (EWT), the size of the retrospective window, and the handling of previous outbreaks and zero values in data. These factors can influence the method’s reproducibility, especially when not documented in public communications.

Objective:

To review the definitions of dengue outbreaks and the guidelines used for its surveillance in Colombia, while quantitatively assessing the performance of the endemic channel method.

Methods:

We gathered an institutional baseline from epidemiological bulletins and quantitatively assessed the endemic channel in two main aspects: i) the impact on the EWT of parameter selection regarding retrospective data window, previous epidemic years handling, and zero-value handling, using a statistical framework; and ii) the endemic channel's performance based on the windows of opportunity, outbreak detection capacity, and the ratio of warnings that correspond to actual outbreaks.

Results:

Endemic channel performance is higher as transmission increases; likely due to more robust data that facilitates a timely detection of outliers, while lower transmission areas show a sharper rise in cases when outbreaks are missed by the method, indicating limited detection capacity. Reducing the retrospective data window improved metrics across all transmission profiles, while extending it decreased performance due to changes in detection capacity. There was no significant difference (p-value < 0.01) in performance when data from the 2019 epidemic year was included or excluded for municipalities with high or very high transmission levels. Instead of adding a unit, applying a minimal shift to the data prevents the endemic channel from estimating null values for the EWT or other thresholds, significantly improving performance across all transmission levels (p-value < 0.01).

Conclusions:

The endemic channel's performance varies with the outbreak definition and the municipality's transmission level. Encouraging an optimal retrospective window is challenging, as data is computed by years. Nevertheless, the improved performance with shorter retrospective windows is likely due to reduced overlap in seasonal outbreaks occurring every three years in Colombia. Shifting data by a limiting-to-zero value, instead of adding a complete unit, improves performance and can be easily integrated into existing surveillance templates. Timeliness of warnings and windows of opportunity should be considered when determining the ideal parameter combination. Finally, reassessing outbreak definitions, method parameters, and foundational assumptions underpinning surveillance tools is essential to ensure their validity and effectiveness, especially when they are used to inform early warning systems and public health policies.