scholarly journals Stay-at-home works to fight against COVID-19: International evidence from Google mobility data

2021 ◽  
pp. 1-11
Author(s):  
Hakan Yilmazkuday
Keyword(s):  
International Evidence ◽  
Mobility Data ◽  
At Home

scholarly journals Case Study: Using Facebook Data to Monitor Adherence to Stay-at-home Orders in Colorado and Utah

2020 ◽  
Author(s):  
Ryan M. Layer ◽  
Bailey Fosdick ◽  
Michael Bradshaw ◽  
Daniel B. Larremore ◽  
Paul Doherty
Keyword(s):  
Limited Resources ◽  
Location Data ◽  
Mobility Data ◽  
Health Measure ◽  
The Us ◽  
User Data ◽  
Interactive Visualizations ◽  
Local Officials ◽  
At Home

ABSTRACTIn the absence of effective treatments or a vaccine, social distancing has been the only public health measure available to combat the COVID-19 pandemic to date. In the US, implementing this response has been left to state, county, and city officials, and many localities have issued some form of a stay-at-home order. Without existing tools and with limited resources, localities struggled to understand how their orders changed behavior. In response, several technology companies opened access to their users’ location data. As part of the COVID-19 Data Mobility Data Network [2], we obtained access to Facebook User data and developed four key metrics and visualizations to monitor various aspects of adherence to stay at home orders. These metrics were carefully incorporated into static and interactive visualizations for dissemination to local officials.All code is open source and freely available at https://github.com/ryanlayer/COvid19

scholarly journals Neighborhood income and physical distancing during the COVID-19 pandemic in the U.S.

2020 ◽  
Author(s):  
Jonathan Jay ◽  
Jacob Bor ◽  
Elaine Nsoesie ◽  
Sarah Ketchen Lipson ◽  
David K. Jones ◽  
...  
Keyword(s):  
Low Income ◽  
High Income ◽  
Transmission Risk ◽  
Percentage Point Increase ◽  
Mobility Data ◽  
Percentage Points ◽  
Point Increase ◽  
Neighborhood Income ◽  
The U.S ◽  
At Home

AbstractIntroductionAlthough physical distancing has been the primary strategy to reduce the spread of COVID-19 in the U.S., people’s ability to distance may vary by socioeconomic characteristics, leading to higher transmission risk in low-income neighborhoods.MethodsWe used mobility data from a large, anonymized sample of smartphone users to assess the relationship between neighborhood median household income and physical distancing during the COVID-19 epidemic. We assessed changes in several behaviors including: spending the day entirely at home; working outside the home; and visits to supermarkets, parks, hospitals, and other locations. We also assessed differences in effects of state policies on physical distancing across neighborhood income levels.ResultsWe found a strong gradient between neighborhood income and physical distancing. Compared to January and February 2020, the proportion of individuals spending the day entirely at home in April 2020 increased by 10.9 percentage points in low-income neighborhoods and by 27.1 percentage points in high-income neighborhoods. During April 2020, people in low-income neighborhoods were more likely to work outside the home, compared to people in higher-income neighborhoods, but not more likely to visit non-work locations. State physical distancing orders were associated with a 1.5 percentage-point increase (95% CI [0.9, 2.1], p < 0.001) in staying home in low-income neighborhoods and a 2.4 percentage point increase (95% CI [1.4, 3.4], p < 0.001) in high-income neighborhoods.DiscussionPeople in lower-income neighborhoods have faced barriers to physical distancing, particularly the need to work outside the home. State physical distancing policies have not mitigated these disparities.

scholarly journals Early social distancing policies in Europe, changes in mobility & COVID-19 case trajectories: Insights from Spring 2020

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253071
Author(s):  
Liana R. Woskie ◽  
Jonathan Hennessy ◽  
Valeria Espinosa ◽  
Thomas C. Tsai ◽  
Swapnil Vispute ◽  
...  
Keyword(s):  
Comprehensive Evaluation ◽  
Population Mobility ◽  
School Closures ◽  
Social Distancing ◽  
Government Response ◽  
Mobility Data ◽  
Aggregate Population ◽  
The Impact ◽  
The Relationship ◽  
At Home

Background Social distancing have been widely used to mitigate community spread of SARS-CoV-2. We sought to quantify the impact of COVID-19 social distancing policies across 27 European counties in spring 2020 on population mobility and the subsequent trajectory of disease. Methods We obtained data on national social distancing policies from the Oxford COVID-19 Government Response Tracker and aggregated and anonymized mobility data from Google. We used a pre-post comparison and two linear mixed-effects models to first assess the relationship between implementation of national policies and observed changes in mobility, and then to assess the relationship between changes in mobility and rates of COVID-19 infections in subsequent weeks. Results Compared to a pre-COVID baseline, Spain saw the largest decrease in aggregate population mobility (~70%), as measured by the time spent away from residence, while Sweden saw the smallest decrease (~20%). The largest declines in mobility were associated with mandatory stay-at-home orders, followed by mandatory workplace closures, school closures, and non-mandatory workplace closures. While mandatory shelter-in-place orders were associated with 16.7% less mobility (95% CI: -23.7% to -9.7%), non-mandatory orders were only associated with an 8.4% decrease (95% CI: -14.9% to -1.8%). Large-gathering bans were associated with the smallest change in mobility compared with other policy types. Changes in mobility were in turn associated with changes in COVID-19 case growth. For example, a 10% decrease in time spent away from places of residence was associated with 11.8% (95% CI: 3.8%, 19.1%) fewer new COVID-19 cases. Discussion This comprehensive evaluation across Europe suggests that mandatory stay-at-home orders and workplace closures had the largest impacts on population mobility and subsequent COVID-19 cases at the onset of the pandemic. With a better understanding of policies’ relative performance, countries can more effectively invest in, and target, early nonpharmacological interventions.

scholarly journals Expected Health Effects of Reduced Air Pollution from COVID-19 Social Distancing

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 951
Author(s):  
Steve Cicala ◽  
Stephen P. Holland ◽  
Erin T. Mansur ◽  
Nicholas Z. Muller ◽  
Andrew J. Yates
Keyword(s):  
Air Pollution ◽  
Health Effects ◽  
Electricity Consumption ◽  
The United States ◽  
Time Frame ◽  
Social Distancing ◽  
Mobility Data ◽  
Premature Deaths ◽  
The Impact ◽  
At Home

The COVID-19 pandemic resulted in stay-at-home policies and other social distancing behaviors in the United States in spring of 2020. This paper examines the impact that these actions had on emissions and expected health effects through reduced personal vehicle travel and electricity consumption. Using daily cell phone mobility data for each U.S. county, we find that vehicle travel dropped about 40% by mid-April across the nation. States that imposed stay-at-home policies before March 28 decreased travel slightly more than other states, but travel in all states decreased significantly. Using data on hourly electricity consumption by electricity region (e.g., balancing authority), we find that electricity consumption fell about 6% on average by mid-April with substantial heterogeneity. Given these decreases in travel and electricity use, we estimate the county-level expected improvements in air quality, and, therefore, expected declines in mortality. Overall, we estimate that, for a month of social distancing, the expected premature deaths due to air pollution from personal vehicle travel and electricity consumption declined by approximately 360 deaths, or about 25% of the baseline 1500 deaths. In addition, we estimate that CO2 emissions from these sources fell by 46 million metric tons (a reduction of approximately 19%) over the same time frame.

scholarly journals Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning

2021 ◽  
Vol 1 (9) ◽  
pp. 588-597 ◽  
Author(s):  
Roman Levin ◽  
Dennis L. Chao ◽  
Edward A. Wenger ◽  
Joshua L. Proctor
Keyword(s):  
Manifold Learning ◽  
Disease Transmission ◽  
Urban Areas ◽  
Cell Phone ◽  
Human Mobility ◽  
Measurement Instrument ◽  
Geographic Scale ◽  
Mobility Data ◽  
And Behavior ◽  
At Home

AbstractUnderstanding the complex interplay between human behavior, disease transmission and non-pharmaceutical interventions during the COVID-19 pandemic could provide valuable insights with which to focus future public health efforts. Cell phone mobility data offer a modern measurement instrument to investigate human mobility and behavior at an unprecedented scale. We investigate aggregated and anonymized mobility data, which measure how populations at the census-block-group geographic scale stayed at home in California, Georgia, Texas and Washington from the beginning of the pandemic. Using manifold learning techniques, we show that a low-dimensional embedding enables the identification of patterns of mobility behavior that align with stay-at-home orders, correlate with socioeconomic factors, cluster geographically, reveal subpopulations that probably migrated out of urban areas and, importantly, link to COVID-19 case counts. The analysis and approach provide local epidemiologists a framework for interpreting mobility data and behavior to inform policy makers’ decision-making aimed at curbing the spread of COVID-19.

scholarly journals Quantifying behavior change during the first year of the COVID-19 pandemic in the United States

2022 ◽  
Author(s):  
Dennis L Chao ◽  
Victor Cho ◽  
Amanda S Izzo ◽  
Joshua L Proctor ◽  
Marita Zimmermann
Keyword(s):  
United States ◽  
State Level ◽  
The United States ◽  
Early Spring ◽  
First Year ◽  
Mobility Data ◽  
High Incidence ◽  
Low Incidence ◽  
Staying At Home ◽  
At Home

Background: During the first year of the COVID-19 pandemic, the most effective way to reduce transmission and to protect oneself was to reduce contact with others. However, it is unclear how behavior changed, despite numerous surveys about peoples' attitudes and actions during the pandemic and public health efforts to influence behavior. Methods: We used two sources of data to quantify changes in behavior at the county level during the first year of the pandemic in the United States: aggregated mobile device (smartphone) location data to approximate the fraction of people staying at home each day and digital invitation data to capture the number and size of social gatherings. Results: Between mid-March to early April 2020, the number of events fell and the fraction of devices staying at home peaked, independently of when states issued emergency orders or stay-at-home recommendations. Activity began to recover in May or June, with later rebounds in counties that suffered an early spring wave of reported COVID-19 cases. Counties with high incidence in the summer had more events, higher mobility, and less stringent state-level COVID-related restrictions the month before than counties with low incidence. Counties with high incidence in early fall stayed at home less and had less stringent state-level COVID-related restrictions in October, when cases began to rise in some parts of the US. During the early months of the pandemic, the number of events was inversely correlated with the fraction of devices staying at home, but after the fall of 2020 mobility appeared to stay constant as the number of events fell. Greater changes in behavior were observed in counties where a larger fraction voted for Biden in the 2020 US Presidential election. The number of people invited per event dropped gradually throughout the first year of the pandemic. Conclusions: The mobility and events datasets uncovered different kinds of behavioral responses to the pandemic. Our results indicate that people did in fact change their behavior in ways that likely reduced COVID exposure and transmission, though the degree of change appeared to be affected by political views. Though the mobility data captured the initial massive behavior changes in the first months of the pandemic, the digital invitation data, presented here for the first time, continued to show large changes in behavior later in the first year of the pandemic.

scholarly journals Linking excess mortality to Google mobility data during the COVID-19 pandemic in England and Wales

2020 ◽  
Author(s):  
Ugofilippo Basellini ◽  
Diego Alburez-Gutierrez ◽  
Emanuele Del Fava ◽  
Daniela Perrotta ◽  
Marco Bonetti ◽  
...  
Keyword(s):  
Regional Differences ◽  
Excess Mortality ◽  
Human Mobility ◽  
Positive Association ◽  
Policy Implications ◽  
England And Wales ◽  
Mobility Data ◽  
The Relationship ◽  
Second Wave ◽  
At Home

Following the outbreak of COVID-19, a number of non-pharmaceutical interventions have been implemented to contain the spread of the pandemic. Despite the recent reduction in the number of infections and deaths in Europe, it is still unclear to which extent these governmental actions have contained the spread of the disease and reduced mortality. In this article, we estimate the effects of reduced human mobility on excess mortality using digital mobility data at the regional level in England and Wales. Specifically, we employ the Google COVID-19 Community Mobility Reports, which offer an approximation to the changes in mobility due to different social distancing measures. Considering that changes in mobility would require some time before having an effect on mortality, we analyse the relationship between excess mortality and lagged indicators of human mobility. We find a negative association between excess mortality and time spent at home, as well as a positive association with changes in outdoor mobility, after controlling for the time trend of the pandemic and regional differences. We estimate that almost 130,000 excess deaths have been averted as a result of the increased time spent at home. In addition to addressing a key scientific question, our results have important policy implications for future pandemics and a potential second wave of COVID-19.

scholarly journals Cell phone mobility data and manifold learning: Insights into population behavior during the COVID-19 pandemic

2020 ◽  
Author(s):  
Roman Levin ◽  
Dennis L. Chao ◽  
Edward A. Wenger ◽  
Joshua L. Proctor
Keyword(s):  
Manifold Learning ◽  
Disease Transmission ◽  
Urban Areas ◽  
Cell Phone ◽  
Human Mobility ◽  
The United States ◽  
Geographic Scale ◽  
Mobility Data ◽  
And Behavior ◽  
At Home

AbstractAs COVID-19 cases resurge in the United States, understanding the complex interplay between human behavior, disease transmission, and non-pharmaceutical interventions during the pandemic could provide valuable insights to focus future public health efforts. Cell-phone mobility data offers a modern measurement instrument to investigate human mobility and behavior at an unprecedented scale. We investigate mobility data collected, aggregated, and anonymized by SafeGraph Inc. which measures how populations at the census-block-group geographic scale stayed at home in California, Georgia, Texas, and Washington since the beginning of the pandemic. Using manifold learning techniques, we find patterns of mobility behavior that align with stay-at-home orders, correlate with socioeconomic factors, cluster geographically, and reveal sub-populations that likely migrated out of urban areas. The analysis and approach provides policy makers a framework for interpreting mobility data and behavior to inform actions aimed at curbing the spread of COVID-19.

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