St Andrews Research Repository

St Andrews University Home
View Item 
  •   St Andrews Research Repository
  • University of St Andrews Research
  • University of St Andrews Research
  • University of St Andrews Research
  • View Item
  •   St Andrews Research Repository
  • University of St Andrews Research
  • University of St Andrews Research
  • University of St Andrews Research
  • View Item
  •   St Andrews Research Repository
  • University of St Andrews Research
  • University of St Andrews Research
  • University of St Andrews Research
  • View Item
  • Register / Login
JavaScript is disabled for your browser. Some features of this site may not work without it.

Complex model calibration through emulation, a worked example for a stochastic epidemic model

Thumbnail
View/Open
Dunne_2022_Epidemics_Complex_model_CC.pdf (1.998Mb)
Date
01/06/2022
Author
Dunne, Michael
Mohammadi, Hossein
Challenor, Peter
Borgo, Rita
Porphyre, Thibaud
Vernon, Ian
Firat, Elif E.
Turkay, Cagatay
Torsney-Weir, Thomas
Goldstein, Michael
Reeve, Richard
Fang, Hui
Swallow, Ben
Keywords
Uncertainty quantification
History matching
Stochastic epidemic model
SEIR
Calibration
Covid-19
QA75 Electronic computers. Computer science
RA0421 Public health. Hygiene. Preventive Medicine
T-NDAS
SDG 3 - Good Health and Well-being
MCC
Metadata
Show full item record
Abstract
Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertain-ties 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.
Citation
Dunne , M , Mohammadi , H , Challenor , P , Borgo , R , Porphyre , T , Vernon , I , Firat , E E , Turkay , C , Torsney-Weir , T , Goldstein , M , Reeve , R , Fang , H & Swallow , B 2022 , ' Complex model calibration through emulation, a worked example for a stochastic epidemic model ' , Epidemics , vol. 39 , 100574 . https://doi.org/10.1016/j.epidem.2022.100574
Publication
Epidemics
Status
Peer reviewed
DOI
https://doi.org/10.1016/j.epidem.2022.100574
ISSN
1755-4365
Type
Journal article
Rights
Copyright © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Description
Funding: This work was supported by EPSRC, United Kingdom grant no. EP/R014604/1. RR was funded by STFC, United Kingdom grant no ST/V006126/1. IV gratefully acknowledges Wellcome funding (218261/Z/19/Z) and EPSRC funding (EP W011956). TP gratefully acknowledges funding from the Scottish Government Rural and Environment Science and Analytical Services Division, United Kingdom, as part of the Centre of Expertise on Animal Disease Outbreaks (EPIC). TP would also like to thank the French National Research Agency and Boehringer Ingelheim Animal Health France for support through the IDEXLYON project (ANR-16-IDEX-0005) and the Industrial Chair in Veterinary Public Health, as part of the VPH Hub in Lyon.
Collections
  • University of St Andrews Research
URI
http://hdl.handle.net/10023/26084

Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

Advanced Search

Browse

All of RepositoryCommunities & CollectionsBy Issue DateNamesTitlesSubjectsClassificationTypeFunderThis CollectionBy Issue DateNamesTitlesSubjectsClassificationTypeFunder

My Account

Login

Open Access

To find out how you can benefit from open access to research, see our library web pages and Open Access blog. For open access help contact: openaccess@st-andrews.ac.uk.

Accessibility

Read our Accessibility statement.

How to submit research papers

The full text of research papers can be submitted to the repository via Pure, the University's research information system. For help see our guide: How to deposit in Pure.

Electronic thesis deposit

Help with deposit.

Repository help

For repository help contact: Digital-Repository@st-andrews.ac.uk.

Give Feedback

Cookie policy

This site may use cookies. Please see Terms and Conditions.

Usage statistics

COUNTER-compliant statistics on downloads from the repository are available from the IRUS-UK Service. Contact us for information.

© University of St Andrews Library

University of St Andrews is a charity registered in Scotland, No SC013532.

  • Facebook
  • Twitter