Hu, Yingjie and Quigley, Brian M. and Taylor, Dane and Cheung, Johnson Chun-Sing (2021) Human mobility data and machine learning reveal geographic differences in alcohol sales and alcohol outlet visits across U.S. states during COVID-19. PLOS ONE, 16 (12). e0255757. ISSN 1932-6203
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Abstract
As many U.S. states implemented stay-at-home orders beginning in March 2020, anecdotes reported a surge in alcohol sales, raising concerns about increased alcohol use and associated ills. The surveillance report from the National Institute on Alcohol Abuse and Alcoholism provides monthly U.S. alcohol sales data from a subset of states, allowing an investigation of this potential increase in alcohol use. Meanwhile, anonymized human mobility data released by companies such as SafeGraph enables an examination of the visiting behavior of people to various alcohol outlets such as bars and liquor stores. This study examines changes to alcohol sales and alcohol outlet visits during COVID-19 and their geographic differences across states. We find major increases in the sales of spirits and wine since March 2020, while the sales of beer decreased. We also find moderate increases in people’s visits to liquor stores, while their visits to bars and pubs substantially decreased. Noticing a significant correlation between alcohol sales and outlet visits, we use machine learning models to examine their relationship and find evidence in some states for likely panic buying of spirits and wine. Large geographic differences exist across states, with both major increases and decreases in alcohol sales and alcohol outlet visits.
Item Type: | Article |
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Subjects: | Science Repository > Social Sciences and Humanities |
Depositing User: | Managing Editor |
Date Deposited: | 05 Dec 2022 08:05 |
Last Modified: | 20 Sep 2023 06:31 |
URI: | http://research.manuscritpub.com/id/eprint/688 |