Clinical Scorecard: Analyzing Temporal Patterns of Various Pathogens and Their Variants Using Routine Surveillance Data

At a Glance

Key Highlights

• Composite surveillance indicators (e.g., influenza-like illness) combine signals from multiple pathogens, potentially obscuring individual pathogen trends.
• A general Bayesian statistical framework was developed to infer temporal trends of multiple pathogens from routine surveillance data.
• Application demonstrated across multiple pathogens, locations, epidemic scenarios, and temporal resolutions, enhancing public health surveillance insights.

Guideline-Based Recommendations

Diagnosis

• Use routine surveillance data that include both composite indicators and pathogen-specific testing results for accurate epidemic monitoring.
• Incorporate pathogen typing, subtyping, or sequencing data to distinguish contributions of individual pathogens or variants.

Management

• Apply statistical models that jointly fit composite time series and component pathogen data to infer distinct epidemic curves.
• Use inferred individual pathogen dynamics to inform targeted public health interventions and resource allocation.

Monitoring & Follow-up

• Regularly collect and integrate data on pathogen-specific testing rates to adjust for time-varying testing and observation noise.
• Visualize inferred temporal trends of individual pathogens alongside composite indicators for clearer epidemiological interpretation.

Risks

• Relying solely on composite surveillance indicators without pathogen-specific data may bias epidemic trend interpretation.
• Variable testing rates and noisy data can obscure true pathogen dynamics if not properly accounted for in analyses.

Patient & Prescribing Data

Individuals presenting with symptoms consistent with influenza-like illness, acute respiratory infections, or dengue infection under surveillance

Disentangling pathogen-specific epidemic trends can improve timing and targeting of interventions, though direct prescribing data is not addressed in this study.

Clinical Best Practices

• Incorporate pathogen-specific testing and typing data into routine surveillance to enhance epidemic trend accuracy.
• Use advanced statistical modeling frameworks to separate overlapping epidemic signals from composite surveillance data.
• Adjust for temporal changes in testing rates and data noise to avoid misinterpretation of pathogen dynamics.
• Apply findings to improve public health response planning and communication during multi-pathogen epidemics or pandemics.

References

• Influenza-like illness and respiratory pathogen surveillance
• Bayesian time-series analysis for pathogen dynamics
• SARS-CoV-2 variant growth modeling