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Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling
Ben Swallow1, Paul Birrell2, Joshua Blake3
1School of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish COVID-19 Response Consortium, UK.
Epidemics
|February 18, 2022
Summary
Estimating infectious disease models faces challenges in uncertainty quantification, data issues, inference, and expert judgment. Addressing these is crucial for pandemic preparedness and effective policy.
Area of Science:
- Epidemiology
- Mathematical Biology
- Public Health Policy
Background:
- The COVID-19 pandemic underscored the critical need for accurate infectious disease modeling.
- Rapid and reliable data-model integration for policy decisions remains a significant challenge.
Purpose of the Study:
- To identify and discuss key challenges in infectious disease model parameter and structure estimation.
- To propose priorities for improving estimation methodologies for future pandemic preparedness.
Main Methods:
- Review and discussion of four core challenges in infectious disease estimation: Uncertainty Quantification (UQ), data challenges, model-based inference and prediction, and expert judgment.
- Identification of key areas for methodological advancement.
Main Results:
- Highlighted significant hurdles in applying UQ frameworks to infectious disease models.
- Discussed limitations in data availability, quality, and integration for real-time estimation.
- Examined complexities in model-based inference and prediction accuracy.
- Addressed the role and challenges of incorporating expert judgment into modeling.
Conclusions:
- Robust infectious disease modeling requires addressing estimation challenges in UQ, data, inference, and expert judgment.
- Prioritizing methodological advancements in these areas is essential for enhancing pandemic response and future preparedness.
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