Clinical Report: Inferring Pathogen-Specific Temporal Trends from Composite Surveillance Data

Overview

A novel Bayesian statistical framework enables disentangling temporal trends of multiple pathogens and their variants from composite surveillance indicators. Applied to influenza subtypes, SARS-CoV-2 variants, and dengue serotypes across diverse countries and epidemic contexts, this approach reveals distinct epidemic dynamics obscured in aggregate data.

Background

Surveillance indicators such as influenza-like illness (ILI) often represent composite signals from multiple pathogens and their variants, complicating accurate epidemic monitoring. Traditional analyses assuming a single pathogen can misrepresent trends, especially during variant replacement or co-circulation. Although some testing data exist to identify component pathogens, variable testing rates and noise limit their utility. A generalizable statistical method to integrate composite and component data is needed to improve epidemic inference and public health responses.

Data Highlights

The framework was applied to three surveillance systems covering: (1) influenza subtypes in Australia, Singapore, and the USA (2012–2024); (2) SARS-CoV-2 variants in the UK (2020–2023); and (3) dengue serotypes in Taiwan (2006–2016 and 2023 outbreak). Data included weekly and daily reporting resolutions, with pathogen-specific testing and typing information. The model jointly fit individual pathogen epidemic curves to component data and the composite time series, accounting for noise and variable testing rates.

Key Findings

• Composite surveillance indicators often mask distinct epidemic trajectories of individual pathogens and variants.
• The Bayesian framework successfully inferred separate temporal trends for multiple pathogens from noisy, time-varying surveillance data.
• During SARS-CoV-2 variant replacement, the model distinguished increasing incidence of emerging variants from declining ones, clarifying epidemic risk.
• Influenza subtype dynamics varied across countries and seasons, highlighting the value of subtype-specific surveillance.
• Dengue serotype contributions to outbreaks were quantified, improving understanding of serotype-specific epidemic patterns.
• The methodology is broadly applicable across pathogens, locations, and surveillance systems with varying temporal resolutions.

Clinical Implications

This modeling approach enhances the interpretation of routine surveillance data by providing pathogen- and variant-specific epidemic trends, enabling more precise public health decision-making. Improved detection of emerging variants or serotypes can inform timely interventions and resource allocation. Integrating such methods into surveillance reporting could augment outbreak prediction and control strategies.

Conclusion

The presented statistical framework offers a robust, generalizable tool to disentangle complex pathogen dynamics from composite surveillance data, advancing epidemiological insight and public health response capabilities across diverse infectious diseases.

References

1. Article Source 2024 -- Analyzing Temporal Patterns of Various Pathogens and Their Variants Using Routine Surveillance Data