Interoperability of statistical models in pandemic preparedness: principles and reality

Area of Science:

• Epidemiology
• Statistical Modeling
• Public Health Informatics

Background:

• Pandemic responses require timely, accurate data analysis to inform policy decisions.
• Diverse datasets and evolving situations challenge traditional statistical modeling approaches.
• A need exists for adaptable, integrated systems for disease surveillance.

Purpose of the Study:

• To present interoperability as a guiding framework for statistical modeling in pandemic response.
• To assist policymakers in addressing multiple questions using diverse datasets.
• To promote principles for future pandemic preparedness through adaptable systems.

Main Methods:

• Utilizing interoperability principles for the joint design and deployment of statistical models.
• Employing probabilistic reasoning for disease surveillance.
• Developing case studies for spatial-temporal inference of SARS-CoV-2 infections.

Main Results:

• Demonstrated the application of interoperability in statistical modeling for pandemic response.
• Successfully inferred and characterized spatial-temporal prevalence of SARS-CoV-2.
• Characterized spatial-temporal reproduction numbers for SARS-CoV-2 infections in England.

Conclusions:

• Interoperability offers a robust framework for statistical modeling in public health emergencies.
• Adaptable, interoperable systems are crucial for effective disease surveillance and pandemic preparedness.
• The framework supports evidence-based policymaking through integrated data analysis.