Integrating patient metadata and pathogen genomic data: advancing pandemic preparedness with a multi-parametric...

Area of Science:

• Public Health
• Epidemiology
• Genomics

Background:

• Effective stakeholder training is crucial for managing public health crises.
• Simulations require complex scenarios integrating pathogen genomic and patient data.
• Data sharing challenges exist between EU member states due to varying standards and regulations.

Purpose of the Study:

• To develop a multi-parametric training tool for public health crisis preparedness.
• To enhance the realism of pandemic simulation scenarios.
• To facilitate customized scenario development for training.

Main Methods:

• Developed a tool linking pathogen genomic data with metadata.
• Integrated high-throughput sequencing (HTS) data with epidemiological, clinical, and demographic information.
• Created an R package and Shiny application for scenario simulation.

Main Results:

• The tool enables enhancement and customization of training datasets and scenarios.
• A structured training procedure using the tool proved effective and user-friendly.
• Rapid scenario simulations are facilitated by the application.

Conclusions:

• The developed tool improves pandemic and public health crisis preparedness training.
• Integration of complex genomic and metadata enhances simulation realism.
• Improved emergency responder readiness is achieved for various biological incident types.