Objective:

To develop a statistical framework for inferring temporal trends of multiple pathogens from composite surveillance data, ultimately enhancing public health outcomes.

Key Findings:

• The composite time series often obscures the distinct dynamics of individual pathogens, leading to potential misinterpretations.
• The new framework allows for the disentangling of epidemic dynamics of multiple pathogens from composite data, providing clearer insights.
• Case studies demonstrated improved insights into the epidemiological trends of influenza, SARS-CoV-2, and dengue, with specific examples of trends observed.

Interpretation:

The methodology enhances public health surveillance by providing clearer insights into the dynamics of individual pathogens, which can improve disease prediction and response strategies, ultimately leading to better health outcomes.

Limitations:

• The model's effectiveness may vary based on the quality and completeness of the underlying surveillance data; improving data collection methods could enhance results.
• Temporal variations in testing rates can introduce noise, complicating the analysis; strategies to standardize testing rates may be necessary.

Conclusion:

This framework can significantly augment public health surveillance reporting by providing a clearer understanding of the dynamics of multiple pathogens, which is crucial for effective public health interventions.