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.
Supported by Data Science Institute in University of Technology Sydney.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lee, S., Song, A.Y., Wong, S.C., Chen, F.: A data-driven approach to modelling pandemic policy-mobility-infection feedback cycles during the COVID-19 crisis case studies of Australia and South Korea. Cities Under Review (2021)
Wei, Y., Wang, J., Song, W., Xiu, C., Ma, L., Pei, T.: Fear, lockdown, and diversion: spread of COVID-19 in China: analysis from a city-based epidemic and mobility model. Cities 110, 103010 (2021)
Beck, M.J., Hensher, D.A.: Insights into the impact of COVID-19 on household travel and activities in Australia: the early days of easing restrictions. Transport Policy 99, 95–119 (2020)
Chan, H.Y., Chen, A., Ma, W., Sze, N.N., Liu, X.: COVID-19, community response, public policy, and travel patterns: a tale of Hong Kong. Transport Policy 106, 173–184 (2021)
Bian, Z., et al.: Time lag effects of COVID-19 policies on transportation systems: a comparative study of New York City and Seattle. Transp. Res. Part A Policy Practice 145, 269–283 (2021)
Rasmussen, C.E., Williams, C.K.: Gaussian processes for machine learning. The MIT Press, Cambridge, MA, pp. 13–16 (2006)
Ngoduy, D., Lee, S., Treiber, M., Keyvan-Ekbatani, M., Vu, H.L.: Langevin method for a continuous stochastic car-following model and its stability conditions. Transp. Res. Part C Emerg. Technol. 105, 599–610 (2019)
Lee, S., Ngoduy, D., Keyvan-Ekbatani, M.: Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways. Transp. Res. Part C: Emerg. Technol. 106, 360–377 (2019)
Lee, S., Ryu, I., Ngoduy, D., Hoang, N.H., Choi, K.: A stochastic behaviour model of a personal mobility under heterogeneous low-carbon traffic flow. Transp. Res. Part C: Emerg. Technol. 128, 103163 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Lee, S., Chen, F. (2022). Identifying the Effective Restriction and Vaccination Policies During the COVID-19 Crisis in Sydney: A Machine Learning Approach. In: Long, G., Yu, X., Wang, S. (eds) AI 2021: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13151. Springer, Cham. https://doi.org/10.1007/978-3-030-97546-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-97546-3_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97545-6
Online ISBN: 978-3-030-97546-3
eBook Packages: Computer ScienceComputer Science (R0)