Modelling COVID-19 pandemic dynamics using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) machine learning models : a case study from systems thinking and system identification perspectives

Wei, H. orcid.org/0000-0002-4704-7346 and Billings, S.A. (2022) Modelling COVID-19 pandemic dynamics using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) machine learning models : a case study from systems thinking and system identification perspectives. In: Jiang, R., Zhang, L., Wei, H.L., Crookes, D. and Chazot, P., (eds.) Recent Advances in AI‑enabled Automated Medical Diagnosis. AI4MED 2021 : International Symposium on Artificial Intelligence for Medical Applications, 19-23 Aug 2021, Virtual Conference (Newcastle upon Tyne, UK). CRC Press , pp. 13-28. ISBN 9781032008431

Abstract

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Editors:
  • Jiang, R.
  • Zhang, L.
  • Wei, H.L.
  • Crookes, D.
  • Chazot, P.
Copyright, Publisher and Additional Information:

© 2022 Richard Jiang, Li Zhang, Hua-Liang Wei, Danny Crookes and Paul Chazot. This is an author-produced version of a chapter subsequently published in Recent Advances in AI-enabled Automated Medical Diagnosis. Uploaded in accordance with the publisher's self-archiving policy.

Keywords: COVID-19; SIR model; SEIR Model; NARMAX model; Machine Learning
Dates:
  • Published: 20 October 2022
  • Published (online): 20 October 2022
  • Accepted: 8 August 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:
Funder
Grant number
Engineering and Physical Sciences Research Council
EP/I011056/1; EP/H00453X/1
Natural Environment Research Council
NE/V002511/1; NE/V001787/1
Science and Technology Facilities Council
ST/V000977/1
Depositing User: Symplectic Sheffield
Date Deposited: 01 Nov 2021 07:39
Last Modified: 12 Jan 2024 12:16
Status: Published
Publisher: CRC Press
Refereed: Yes
Identification Number: 10.1201/9781003176121-2
Related URLs:
Open Archives Initiative ID (OAI ID):

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