ArticleEpidemics2022
Complex model calibration through emulation, a worked example for a stochastic epidemic model.
Article in Epidemics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- On real-time calibrated prediction for complex model-based decision support in pandemics: Part 2.PLoS computational biology · 2026Article
- Cross-validation-based sequential design for stochastic models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Emulating computer models with high-dimensional count output.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.Epidemics · 2022Article
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Authors and funding
13 authors at 10 institutions in 3 countries.
Funding
Abstract
Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology, however they have thus far not been widely used in this context. In this paper, we provide a tutorial on uncertainty quantification of stochastic epidemic models, aiming to facilitate the use of the uncertainty quantification paradigm for practitioners with other complex stochastic simulators of applied systems. We provide a formal workflow including the important decisions and considerations that need to be taken, and illustrate the methods over a simple stochastic epidemic model of UK SARS-CoV-2 transmission and patient outcome. We also present new approaches to visualisation of outputs from sensitivity analyses and uncertainty quantification more generally in high input and/or output dimensions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.