Identifying the Effective Restriction and Vaccination Policies During the COVID-19 Crisis in Sydney: A Machine Learning Approach

Lee, S; Chen, F

Identifying the Effective Restriction and Vaccination Policies During the COVID-19 Crisis in Sydney: A Machine Learning Approach

Lee, S

Chen, F

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Publisher:: SPRINGER INTERNATIONAL PUBLISHING AG
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13151 LNAI, pp. 356-367
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Lee, S https://orcid.org/0000-0003-2356-3677
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.editor	Long, G
dc.contributor.editor	Yu, X
dc.contributor.editor	Wang, S
dc.date	2022-02-02
dc.date.accessioned	2023-03-29T02:32:43Z
dc.date.available	2023-03-29T02:32:43Z
dc.date.issued	2022-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13151 LNAI, pp. 356-367
dc.identifier.isbn	9783030975456
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/168775
dc.description.abstract	This study identified effective COVID-19 restriction policies and the best times to deploy them to minimise locally acquired COVID-19 cases in Sydney. We normalised stringency levels of individual COVID-19 policies, usage levels of urban mobility, and vaccination rates to establish unbiased multivariate time-series features. We introduced the time-lag from 1 day to 15 d before when the governments have officially announced the number of locally acquired COVID-19 cases to the multivariate features. This time-lag dimension allows us to decide critical timings for announcing various COVID-19 related policies and vaccinations to control rapidly increasing infections. We used principal component analysis (PCA) to reduce the dimensions of the multivariate features. A Gaussian process regression (GPR) estimated the daily number of locally acquired COVID-19 cases based on the reduced dimensional features. The model outperformed diverse parametric and non-parametric models in estimating the daily number of infections. We successfully identified effective restriction policies and the best times to implement them to minimise the rate of confirmed COVID-19 cases by analysing PCA coefficients and kernel functions in GPR.
dc.language	en
dc.publisher	SPRINGER INTERNATIONAL PUBLISHING AG
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartof	34th Australasian Joint Conference on Artificial Intelligence (AI)
dc.relation.ispartofseries	Lecture Notes in Artificial Intelligence
dc.relation.isbasedon	10.1007/978-3-030-97546-3_29
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Identifying the Effective Restriction and Vaccination Policies During the COVID-19 Crisis in Sydney: A Machine Learning Approach
dc.type	Conference Proceeding
utslib.citation.volume	13151 LNAI
utslib.location.activity	Univ Technol Sydney, ELECTR NETWORK
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-29T02:32:34Z
pubs.finish-date	2022-02-04
pubs.publication-status	Published
pubs.start-date	2022-02-02
pubs.volume	13151 LNAI

Abstract:

This study identified effective COVID-19 restriction policies and the best times to deploy them to minimise locally acquired COVID-19 cases in Sydney. We normalised stringency levels of individual COVID-19 policies, usage levels of urban mobility, and vaccination rates to establish unbiased multivariate time-series features. We introduced the time-lag from 1 day to 15 d before when the governments have officially announced the number of locally acquired COVID-19 cases to the multivariate features. This time-lag dimension allows us to decide critical timings for announcing various COVID-19 related policies and vaccinations to control rapidly increasing infections. We used principal component analysis (PCA) to reduce the dimensions of the multivariate features. A Gaussian process regression (GPR) estimated the daily number of locally acquired COVID-19 cases based on the reduced dimensional features. The model outperformed diverse parametric and non-parametric models in estimating the daily number of infections. We successfully identified effective restriction policies and the best times to implement them to minimise the rate of confirmed COVID-19 cases by analysing PCA coefficients and kernel functions in GPR.

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http://hdl.handle.net/10453/168775