On real-time calibrated prediction for complex model-based decision support in pandemics: Part 1
6 affiliations
- 1Department of Economics, Land Environment Economics and Policy Institute, University of Exeter, Exeter, United Kingdom
- 2University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
- 3Department of Mathematics and Statistics, University of Exeter, Exeter, United Kingdom
- 4School of Engineering Mathematics and Technology, University of Bristol, Bristol, United Kingdom
- 5Jean Golding Institute, University of Bristol, Bristol, United Kingdom
- 6Hadley Centre for Climate Science and Services, UK Met Office, Exeter, United Kingdom
Abstract
Infectious disease models are used to predict the spread and impact of outbreaks of a disease. Like other complex models, they have parameters that need to be calibrated, and structural discrepancies from the reality that they simulate that should be accounted for in calibration and prediction. Whilst Uncertainty Quantification (UQ) techniques have been applied to infectious disease models before, they were not routinely used to inform policymakers in the UK during the COVID-19 pandemic. In this paper, we will argue that during a fast moving pandemic, models and policy are changing on timescales that make traditional UQ methods impractical, if not impossible to implement. We present an alternative formulation to the calibration problem that embeds model discrepancy within the structure of the model, and appropriately assimilates data within the simulation. We then show how UQ can be used to calibrate the model in real-time to produce disease trajectories accounting for parameter uncertainty and model discrepancy. We apply these ideas to an age-structured COVID-19 model for England and demonstrate the types of information it could have produced to feed into policy support prior to the lockdown of March 2020. Author summary: During the COVID-19 pandemic, infectious disease models were used to guide policy decisions under intense time pressure. While parameter calibration and uncertainty quantification (UQ) are essential for interpreting such models, traditional approaches are often too slow or rigid to meet real-time demands. In this paper, we introduce a method for real-time calibration of epidemic models that recognises that all models are simplifications. Our approach embeds model discrepancy (uncertainty due to missing or simplified processes in the model) directly into the simulation. This innovation allows us to handle critical challenges, such as how infection first enters a population (known as the seeding problem), without needing to fix its timing, location, or age distribution in advance. We show how policy-relevant outputs, such as projected hospitalisations or infection trajectories, can be generated quickly while still accounting for key sources of uncertainty. This work was motivated by challenges encountered during real-time policy support for the UK government in the COVID-19 pandemic and is demonstrated on age-structured models for England. It provides a foundation for applying complex models in future outbreaks by using critical but often overlooked uncertainties to improve calibration without sacrificing responsiveness, and supports extensions to models that represent disease spread across regions.
Related
- Calibrated Reliance on Large Language Models for Personalized Surgical Care: A Critical Narrative Review of Confidence, Clinical Reasoning and Decision Support
- Identifying and formulating appropriate clinical problems for clinical prediction models and AI decision support: the RIVU-AI framework.
- Injury prediction in elite women’s football: an integrative machine learning-based decision-support framework
- Antibiotic resistance prediction with an attention-based bi-LSTM clinical decision support system
- Artificial Intelligence in Eye Disease: A Systematic Review of Screening, Diagnosis, Prognosis, Treatment-Response Prediction, and Clinical Decision Support
BibTeX
@article{williamson2026realtime,
title = {{On real-time calibrated prediction for complex model-based decision support in pandemics: Part 1}},
author = {Daniel Williamson and Trevelyan J. McKinley and Xiaoyu Xiong and James M. Salter and Robert Challen and Leon Danon and Ben Youngman and Doug McNeall},
journal = {PLOS Computational Biology},
year = {2026},
month = oct,
doi = {10.1371/journal.pcbi.1014868},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014868},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - On real-time calibrated prediction for complex model-based decision support in pandemics: Part 1
AU - Daniel Williamson
AU - Trevelyan J. McKinley
AU - Xiaoyu Xiong
AU - James M. Salter
AU - Robert Challen
AU - Leon Danon
AU - Ben Youngman
AU - Doug McNeall
PY - 2026
DA - 2026/10/28
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014868
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014868
AB - Infectious disease models are used to predict the spread and impact of outbreaks of a disease. Like other complex models, they have parameters that need to be calibrated, and structural discrepancies from the reality that they simulate that should be accounted for in calibration and prediction. Whilst Uncertainty Quantification (UQ) techniques have been applied to infectious disease models before, they were not routinely used to inform policymakers in the UK during the COVID-19 pandemic. In this paper, we will argue that during a fast moving pandemic, models and policy are changing on timescales that make traditional UQ methods impractical, if not impossible to implement. We present an alternative formulation to the calibration problem that embeds model discrepancy within the structure of the model, and appropriately assimilates data within the simulation. We then show how UQ can be used to calibrate the model in real-time to produce disease trajectories accounting for parameter uncertainty and model discrepancy. We apply these ideas to an age-structured COVID-19 model for England and demonstrate the types of information it could have produced to feed into policy support prior to the lockdown of March 2020. Author summary: During the COVID-19 pandemic, infectious disease models were used to guide policy decisions under intense time pressure. While parameter calibration and uncertainty quantification (UQ) are essential for interpreting such models, traditional approaches are often too slow or rigid to meet real-time demands. In this paper, we introduce a method for real-time calibration of epidemic models that recognises that all models are simplifications. Our approach embeds model discrepancy (uncertainty due to missing or simplified processes in the model) directly into the simulation. This innovation allows us to handle critical challenges, such as how infection first enters a population (known as the seeding problem), without needing to fix its timing, location, or age distribution in advance. We show how policy-relevant outputs, such as projected hospitalisations or infection trajectories, can be generated quickly while still accounting for key sources of uncertainty. This work was motivated by challenges encountered during real-time policy support for the UK government in the COVID-19 pandemic and is demonstrated on age-structured models for England. It provides a foundation for applying complex models in future outbreaks by using critical but often overlooked uncertainties to improve calibration without sacrificing responsiveness, and supports extensions to models that represent disease spread across regions.
SN - 1553-7358
ER -