scholarly journals 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 ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0255757
Author(s):  
Yingjie Hu ◽  
Brian M. Quigley ◽  
Dane Taylor
Keyword(s):  
Machine Learning ◽  
Alcohol Use ◽  
Human Mobility ◽  
Alcohol Outlet ◽  
Alcohol Outlets ◽  
Mobility Data ◽  
Liquor Stores ◽  
Surveillance Report ◽  
Geographic Differences ◽  
Alcohol Sales

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.

Neighborhood alcohol outlet density and genetic influences on alcohol use: evidence for gene–environment interaction

2018 ◽  
Vol 49 (3) ◽  
pp. 474-482 ◽  
Author(s):  
Wendy S. Slutske ◽  
Arielle R. Deutsch ◽  
Thomas M. Piasecki
Keyword(s):  
Alcohol Use ◽  
Environment Interaction ◽  
Confidence Limits ◽  
Alcohol Outlet ◽  
Alcohol Outlets ◽  
Alcohol Outlet Density ◽  
Gene Environment Interaction ◽  
Alcohol Involvement ◽  
Gene Environment ◽  
Outlet Density

AbstractBackgroundGenetic influences on alcohol involvement are likely to vary as a function of the ‘alcohol environment,’ given that exposure to alcohol is a necessary precondition for genetic risk to be expressed. However, few gene–environment interaction studies of alcohol involvement have focused on characteristics of the community-level alcohol environment. The goal of this study was to examine whether living in a community with more alcohol outlets would facilitate the expression of the genetic propensity to drink in a genetically-informed national survey of United States young adults.MethodsThe participants were 2434 18–26-year-old twin, full-, and half-sibling pairs from Wave III of the National Longitudinal Study of Adolescent to Adult Health. Participants completed in-home interviews in which alcohol use was assessed. Alcohol outlet densities were extracted from state-level liquor license databases aggregated at the census tract level to derive the density of outlets.ResultsThere was evidence that the estimates of genetic and environmental influences on alcohol use varied as a function of the density of alcohol outlets in the community. For example, the heritability of the frequency of alcohol use for those residing in a neighborhood with ten or more outlets was 74% (95% confidence limits = 55–94%), compared with 16% (95% confidence limits = 0–34%) for those in a neighborhood with zero outlets. This moderating effect of alcohol outlet density was not explained by the state of residence, population density, or neighborhood sociodemographic characteristics.ConclusionsThe results suggest that living in a neighborhood with many alcohol outlets may be especially high-risk for those individuals who are genetically predisposed to frequently drink.

Using Machine Learning to Classify Individuals with Alcohol Use Disorder Based on Treatment Seeking Status

2019 ◽  
Author(s):  
Mary R. Lee ◽  
Vignesh Sankar ◽  
Aaron Hammer ◽  
William G. Kennedy ◽  
Jennifer J. Barb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alcohol Use ◽  
Alcohol Use Disorder ◽  
Treatment Seeking

scholarly journals Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaobin Wang ◽  
Yun Tong ◽  
Yupeng Fan ◽  
Haimeng Liu ◽  
Jun Wu ◽  
...  
Keyword(s):  
Impact Analysis ◽  
Human Mobility ◽  
Economic Activities ◽  
Underdeveloped Countries ◽  
Mobility Data ◽  
Long Run ◽  
Level Of Economic Development ◽  
Human World ◽  
Dynamic Relationships ◽  
The Impact

AbstractSince spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

scholarly journals Mobility Patterns in Different Age Groups in Japan during the COVID-19 Pandemic: a Small Area Time Series Analysis through March 2021

2021 ◽  
Author(s):  
Shuhei Nomura ◽  
Yuta Tanoue ◽  
Daisuke Yoneoka ◽  
Stuart Gilmour ◽  
Takayuki Kawashima ◽  
...  
Keyword(s):  
Care Delivery ◽  
Human Mobility ◽  
Age Groups ◽  
Virus Transmission ◽  
Initial Response ◽  
Health Care Delivery System ◽  
Population Characteristics ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The Status

AbstractIn the COVID-19 era, movement restrictions are crucial to slow virus transmission and have been implemented in most parts of the world, including Japan. To find new insights on human mobility and movement restrictions encouraged (but not forced) by the emergency declaration in Japan, we analyzed mobility data at 35 major stations and downtown areas in Japan—each defined as an area overlaid by several 125-meter grids—from September 1, 2019 to March 19, 2021. Data on the total number of unique individuals per hour passing through each area were obtained from Yahoo Japan Corporation (i.e., more than 13,500 data points for each area). We examined the temporal trend in the ratio of the rolling seven-day daily average of the total population to a baseline on January 16, 2020, by ten-year age groups in five time frames. We demonstrated that the degree and trend of mobility decline after the declaration of a state of emergency varies across age groups and even at the subregional level. We demonstrated that monitoring dynamic geographic and temporal mobility information stratified by detailed population characteristics can help guide not only exit strategies from an ongoing emergency declaration, but also initial response strategies before the next possible resurgence. Combining such detailed data with data on vaccination coverage and COVID-19 incidence (including the status of the health care delivery system) can help governments and local authorities develop community-specific mobility restriction policies. This could include strengthening incentives to stay home and raising awareness of cognitive errors that weaken people's resolve to refrain from nonessential movement.

scholarly journals Comparative Analysis of Geolocation Information through Mobile-Devices under Different COVID-19 Mobility Restriction Patterns in Spain

2021 ◽  
Vol 10 (2) ◽  
pp. 73
Author(s):  
Raquel Pérez-Arnal ◽  
David Conesa ◽  
Sergio Alvarez-Napagao ◽  
Toyotaro Suzumura ◽  
Martí Català ◽  
...  
Keyword(s):  
Mobile Phones ◽  
Human Mobility ◽  
The Other ◽  
Data Sources ◽  
Experimental Setting ◽  
Mobile Data ◽  
Restriction Patterns ◽  
Mobility Data ◽  
The World ◽  
Proper Interpretation

The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility, being the greatest facilitator for the spread of the virus, is at the epicenter of this change. In order to study mobility under COVID-19, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to future crisis, we need to understand all possible mobility data sources at our disposal. Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. Furthermore, the sample of society they cover is large and representative. On the other hand, these data are not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting due to both its large and fast pandemic peak and its implementation of a sustained, generalized lockdown. Our work illustrates how a direct and naive comparison between sources can be misleading, as certain days (e.g., Sundays) exhibit a directly adverse behavior. After understanding their particularities, we find them to be partially correlated and, what is more important, complementary under a proper interpretation. Finally, we confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain.

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