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
Since the outbreak of COVID-19, an astronomical number of publications on the pandemic dynamics appeared in the literature, of which many use the susceptible infected removed (SIR) and susceptible exposed infected removed (SEIR) models, or their variants, to simulate and study the spread of the coronavirus. SIR and SEIR are continuous-time models which are a class of initial value problems (IVPs) of ordinary differential equations (ODEs). Discrete-time models such as regression and machine learning have also been applied to analyze COVID-19 pandemic data (e.g. predicting infection cases), but most of these methods use simplified models involving a small number of input variables pre-selected based on a priori knowledge, or use very complicated models (e.g. deep learning), purely focusing on certain prediction purposes and paying little attention to the model interpretability. There have been relatively fewer studies focusing on the investigations of the inherent time-lagged or time-delayed relationships e.g. between the reproduction number (R number), infection cases, and deaths, analyzing the pandemic spread from a systems thinking and dynamic perspective.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
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: |
|
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): | oai:eprints.whiterose.ac.uk:179757 |