Complex model calibration through emulation, a worked example for a stochastic epidemic model.
Epidemics
; 39: 100574, 2022 06.
Article
in En
| MEDLINE
| ID: mdl-35617882
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Epidemics
/
COVID-19
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Epidemics
Year:
2022
Document type:
Article
Affiliation country:
United kingdom