scholarly journals A county-level study of the effects of state-mandated COVID-19 lockdowns on urban and rural restaurant visits using consumers’ cell phone geo-location data

Author(s):  
Tannista Banerjee ◽  
Arnab Nayak ◽  
HaiYue Zhao
2020 ◽  
Vol 27 (8) ◽  
Author(s):  
Shiv T Sehra ◽  
Louis J Kishfy ◽  
Alexander Brodski ◽  
Michael D George ◽  
Douglas J Wiebe ◽  
...  
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2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ashley O’Donoghue ◽  
Tenzin Dechen ◽  
Whitney Pavlova ◽  
Michael Boals ◽  
Garba Moussa ◽  
...  

AbstractThe true risk of a COVID-19 resurgence as states reopen businesses is unknown. In this paper, we used anonymized cell-phone data to quantify the potential risk of COVID-19 transmission in business establishments by building a Business Risk Index that measures transmission risk over time. The index was built using two metrics, visits per square foot and the average duration of visits, to account for both density of visits and length of time visitors linger in the business. We analyzed trends in traffic patterns to 1,272,260 businesses across eight states from January 2020 to June 2020. We found that potentially risky traffic behaviors at businesses decreased by 30% by April. Since the end of April, the risk index has been increasing as states reopen. There are some notable differences in trends across states and industries. Finally, we showed that the time series of the average Business Risk Index is useful for forecasting future COVID-19 cases at the county-level (P < 0.001). We found that an increase in a county’s average Business Risk Index is associated with an increase in positive COVID-19 cases in 1 week (IRR: 1.16, 95% CI: (1.1–1.26)). Our risk index provides a way for policymakers and hospital decision-makers to monitor the potential risk of COVID-19 transmission from businesses based on the frequency and density of visits to businesses. This can serve as an important metric as states monitor and evaluate their reopening strategies.


Social Forces ◽  
2020 ◽  
Author(s):  
Byungkyu Lee

Abstract Close elections are rare, but most Americans have experienced a close election at least once in their lifetime. How does intense politicization in close elections affect our close relationships? Using four national egocentric network surveys during the 1992, 2000, 2008, and 2016 election cycles, I find that close elections are associated with a modest decrease in network isolation in Americans’ political discussion networks. While Americans are more politically engaged in close elections, they also are less likely to be exposed to political dissent and more likely to deactivate their kinship ties to discuss politics. I further investigate a potential mechanism, the extent of political advertising, and show that cross-cutting exposure is more likely to disappear in states with more political ads air. To examine the behavioral consequence of close elections within American families, I revisit large-scale cell phone location data during the Thanksgiving holiday in 2016. I find that Americans are less likely to travel following close elections, and that families comprised of members with strong, opposing political views are more likely to shorten their Thanksgiving dinner. These results illuminate a process in which politicization may “close off” strong-tied relationships in the aftermath of close elections.


2019 ◽  
Vol 37 (6) ◽  
pp. 751-775 ◽  
Author(s):  
Emma W. Marshall ◽  
Jennifer L. Groscup ◽  
Eve M. Brank ◽  
Analay Perez ◽  
Lori A. Hoetger

2021 ◽  
pp. 1-9
Author(s):  
John F. Camobreco ◽  
Zhaochen He

ABSTRACT The response to the coronavirus pandemic in the United States has shown that even a serious public health crisis cannot escape the lens of partisanship. The literature shows that most Republicans have viewed the coronavirus as less serious than their Democratic counterparts. This study demonstrates that this partisan gap extends to the real behavior of the public during, and after, the coronavirus state lockdowns. Using location data from mobile phones, we find that county-level partisanship predicts compliance with state shutdown orders, even when controlling for local COVID-19 intensity. Further, the magnitude of this effect is stronger than that of other explanatory variables, such as age, education, and population density. These results show that partisan beliefs can affect behavior regarding issues that are not overtly political, even behaviors that could put one or others at risk.


2021 ◽  
Author(s):  
Behzad Vahedi ◽  
Morteza Karimzadeh ◽  
Hamidreza Zoraghein

Abstract Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19. In this study, we first compare the power of Facebook’s social connectedness with cell phone-derived human mobility for predicting county-level new cases of COVID-19. Our experiments show that social connectedness is a better proxy for measuring human interactions leading to new infections. Next, we develop a SpatioTemporal autoregressive eXtreme Gradient Boosting (STXGB) model to predict county-level new cases of COVID-19 in the coterminous US. We evaluate the model on five weekly forecast dates between October 24 and November 28, 2020 over one- to four-week prediction horizons. Comparing our predictions with a baseline Ensemble of 32-models currently used by the CDC indicates an average 58% improvement in prediction RMSEs over two- to four-week prediction horizons, pointing to the strong predictive power of our model.


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