A statistical tool for comparing seasonal ILI surveillance data

Area of Science:

• Epidemiology
• Biostatistics
• Public Health Surveillance

Background:

• Yearly influenza epidemics pose a significant public health challenge.
• Seasonal surveillance data from the Center for Disease Control and Prevention (CDC) provides insights into influenza trends.
• Comparing influenza incidence across different geographical regions is crucial for targeted interventions.

Purpose of the Study:

• To develop and apply a novel methodology for comparing influenza epidemic features across ten Health and Human Services (HHS) regions.
• To analyze the evolution of influenza incidence data within these regions.
• To facilitate effective comparison of regional influenza surveillance data.

Main Methods:

• Utilized seasonal influenza surveillance data from the CDC.
• Developed a method comparing the relative distribution of weekly new cases.
• Employed a negative binomial regression model to predict incidence, controlling for covariates.
• Calculated predictions using a standardized set of covariate values, accounting for regional population size.

Main Results:

• Presented main findings through graphical representations for clear emphasis.
• The methodology facilitates rapid identification of relevant features in seasonal influenza data.
• Enabled effective comparison of influenza incidence patterns across the ten HHS regions.

Conclusions:

• The proposed methodology offers a robust approach for comparing regional influenza surveillance data.
• Predicted values, adjusted for population, provide a standardized basis for inter-regional comparisons.
• Graphical presentation enhances the understanding of influenza epidemic dynamics and regional differences.