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Using big data analytics to explore the relationship between government stringency and preventative social behaviour during the COVID-19 pandemic in the United Kingdom [Preprint]

Al-Zubaidy, Noor; Crespo, Roberto; Jones, Sarah; Drikvandi, Reza; Gould, Lisa; Leis, Melanie; Maheswaran, Hendramoorty; Neves, Ana Luisa; Darzi, Ara

Using big data analytics to explore the relationship between government stringency and preventative social behaviour during the COVID-19 pandemic in the United Kingdom [Preprint] Thumbnail


Authors

Noor Al-Zubaidy

Roberto Crespo

Sarah Jones

Lisa Gould

Melanie Leis

Hendramoorty Maheswaran

Ana Luisa Neves

Ara Darzi



Abstract

We evaluated the association between preventative social behaviour and government stringency. Additionally, we sought to evaluate the influence of additional factors including time, need to protect others (using the reported number of COVID-19 deaths as a surrogate measure) and reported confidence in government handling of the COVID-19 pandemic. We used repeated national cross-sectional surveys the UK over the course of 41 weeks from 1st April 2020 to January 28th, 2021, including a total of 38,092 participants. Preventative social behaviour and government stringency index scores were significantly associated on linear regression analyses (R2 =0.6468, p<0.001, and remained significant after controlling for the effect of reported COVID-19 deaths, confidence in government handling of the pandemic, and time (R2=0.898, p<0.001). Longitudinal data suggest that government stringency is an effective tool in promoting preventative social behaviour in the fight against COVID-19.

Citation

Al-Zubaidy, N., Crespo, R., Jones, S., Drikvandi, R., Gould, L., Leis, M., Maheswaran, H., Neves, A. L., & Darzi, A. Using big data analytics to explore the relationship between government stringency and preventative social behaviour during the COVID-19 pandemic in the United Kingdom [Preprint]

Report Type Other
Deposit Date Jul 29, 2021
Publicly Available Date Oct 18, 2021
Publisher SAGE Publications
Public URL https://durham-repository.worktribe.com/output/1244845
Related Public URLs https://www.medrxiv.org/content/10.1101/2021.07.09.21260246v1
Additional Information A version of this preprint was published in the Health Informatics Journal. See the published version at:

https://durham-repository.worktribe.com/output/1987808

https://doi.org/10.1177/14604582231215867

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