A machine learning approach to analyzing spatiotemporal impacts of mobility restriction policies on infection rates

Young Song, A; Lee, S; Wong, SC

A machine learning approach to analyzing spatiotemporal impacts of mobility restriction policies on infection rates

Young Song, A Lee, S

Wong, SC

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Transportation Research Part A: Policy and Practice, 2023, 176
Issue Date:: 2023-10-01

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Field	Value	Language
dc.contributor.author	Young Song, A
dc.contributor.author	Lee, S https://orcid.org/0000-0003-2356-3677
dc.contributor.author	Wong, SC
dc.date.accessioned	2024-03-17T21:52:23Z
dc.date.available	2024-03-17T21:52:23Z
dc.date.issued	2023-10-01
dc.identifier.citation	Transportation Research Part A: Policy and Practice, 2023, 176
dc.identifier.issn	0965-8564
dc.identifier.issn	1879-2375
dc.identifier.uri	http://hdl.handle.net/10453/176816
dc.description.abstract	This study analyzed the impact of a range of policies that restrict travel accessibility and mobility on infection rates for the original strain of the virus during the first year of the COVID-19 crisis. We constructed a multidimensional dataset and developed an effective data-driven predictive model to investigate causality between a policy, mobility, and an infection, drawing upon spatiotemporal perspectives. The multidimensional dataset included daily infections, daily restriction policies, and daily and hourly multimodal travel patterns. We quantified and normalized the dataset in relation to pre-COVID-19 policies and travel activities. A machine learning framework that integrated principal component analysis (PCA) and a Gaussian process regression (GPR) was formulated to evaluate the effectiveness of mobility restriction policies and their optimal implementation time during the infancy stage of the pandemic. In a case study, we selected Seoul in South Korea and Sydney in Australia for model calibrations and validations. Both countries deployed comprehensive urban restriction policies during the worldwide pandemic. The proposed model produced better performance than diverse non-parametric and parametric models to estimate the daily number of infections in the two areas. Furthermore, we discovered effective restriction policies and the best times to implement them to minimize the number of acquired COVID-19 cases by analyzing coefficients in PCA and GPR kernel functions. Our finding has far-reaching policy implications. First, the proposed methods can be used for formulating restriction policies for other regions with diverse population densities as the chosen cities in this case study. Second, our finding contributes to evidence-based policymaking.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Transportation Research Part A: Policy and Practice
dc.relation.isbasedon	10.1016/j.tra.2023.103795
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1205 Urban and Regional Planning, 1507 Transportation and Freight Services
dc.subject.classification	Logistics & Transportation
dc.subject.classification	3304 Urban and regional planning
dc.subject.classification	3509 Transportation, logistics and supply chains
dc.title	A machine learning approach to analyzing spatiotemporal impacts of mobility restriction policies on infection rates
dc.type	Journal Article
utslib.citation.volume	176
utslib.for	1205 Urban and Regional Planning
utslib.for	1507 Transportation and Freight Services
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	2024-03-17T21:52:19Z
pubs.publication-status	Published
pubs.volume	176

Abstract:

This study analyzed the impact of a range of policies that restrict travel accessibility and mobility on infection rates for the original strain of the virus during the first year of the COVID-19 crisis. We constructed a multidimensional dataset and developed an effective data-driven predictive model to investigate causality between a policy, mobility, and an infection, drawing upon spatiotemporal perspectives. The multidimensional dataset included daily infections, daily restriction policies, and daily and hourly multimodal travel patterns. We quantified and normalized the dataset in relation to pre-COVID-19 policies and travel activities. A machine learning framework that integrated principal component analysis (PCA) and a Gaussian process regression (GPR) was formulated to evaluate the effectiveness of mobility restriction policies and their optimal implementation time during the infancy stage of the pandemic. In a case study, we selected Seoul in South Korea and Sydney in Australia for model calibrations and validations. Both countries deployed comprehensive urban restriction policies during the worldwide pandemic. The proposed model produced better performance than diverse non-parametric and parametric models to estimate the daily number of infections in the two areas. Furthermore, we discovered effective restriction policies and the best times to implement them to minimize the number of acquired COVID-19 cases by analyzing coefficients in PCA and GPR kernel functions. Our finding has far-reaching policy implications. First, the proposed methods can be used for formulating restriction policies for other regions with diverse population densities as the chosen cities in this case study. Second, our finding contributes to evidence-based policymaking.

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http://hdl.handle.net/10453/176816