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On real-time calibrated prediction for complex model-based decision support in pandemics: Part 2
Trevelyan J McKinley1, Daniel B Williamson2, Xiaoyu Xiong2
1University of Exeter Medical School, University of Exeter, Exeter, United Kingdom.
Plos Computational Biology
|June 1, 2026
Summary
This study introduces a new framework for calibrating complex infectious disease models, like those for COVID-19 transmission. It uses emulation and particle filtering to efficiently handle large datasets and model complexities.
Area of Science:
- Epidemiology
- Computational Biology
- Statistical Modeling
Background:
- Complex stochastic infectious disease models present calibration challenges due to high-dimensional spaces and non-linear dynamics.
- Paucity of data leads to unignorable hidden states, complicating inference routines.
- Existing methods like likelihood-based approaches and direct simulation have scalability limitations.
Purpose of the Study:
- To present a novel framework for calibrating large-scale, stochastic, age-structured, spatial meta-population models.
- To address the challenges of high-dimensional data and hidden states in infectious disease modeling.
- To improve the efficiency and applicability of infectious disease model calibration.
Main Methods:
- Development of an emulation-based framework.
- Embedding a model discrepancy process within the simulation model.
- Integration with particle filtering to emulate the log-likelihood surface.
Main Results:
- Successful calibration of a COVID-19 transmission model for England and Wales.
- Demonstration of emulating the log-likelihood surface for efficient calibration.
- Alleviation of challenges related to spatial and temporal infection introduction.
Conclusions:
- The proposed framework enables calibration of complex models to high-dimensional data.
- Emulation-based methods offer a scalable solution for infectious disease model calibration.
- Further research is needed to address remaining challenges in model calibration and application.
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