Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing

AbstractWe employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Our analysis allows us to classify different shades of social distancing measures for the first wave of the pandemic. We observe a strong decrease in the infection rate occurring two to five weeks after the onset of mobility reduction. A universal time scale emerges, after which social distancing shows its impact. We further provide an actual measure of the impact of social distancing for each region, showing that the effect amounts to a reduction by 20–40% in the infection rate in Europe and 30–70% in the US.

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Mining Google and Apple mobility data:Temporal Anatomy for COVID-19 Social Distancing

10.21203/rs.3.rs-76030/v1 ◽

2020 ◽

Author(s):

Corentin Cot ◽

Giacomo Cacciapaglia ◽

Francesco Sannino

Keyword(s):

United States ◽

Infection Rate ◽

The United States ◽

European Countries ◽

The Political ◽

Social Distancing ◽

Mobility Data ◽

American States ◽

Political Decisions ◽

Mobility Reduction

Abstract We employ the Google and Apple mobility data to identify, quantify and clas- sify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, in- dependently from the political decisions. Interestingly we observe a general decrease in the infection rate occurring two to five weeks after the onset of mobility reduction for the European countries and the American states.

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Mining and Validating Social Media for COVID-19-Related Human Behaviors between January and July; An Infodemiology Study (Preprint)

10.2196/preprints.27059 ◽

2021 ◽

Author(s):

Ashlynn R. Daughton ◽

Courtney Diane Shelley ◽

Martha Barnard ◽

Dax Gerts ◽

Chrysm Watson Ross ◽

...

Keyword(s):

Public Health ◽

Social Media ◽

Ground Truth ◽

The United States ◽

Training Data ◽

Long Term Effects ◽

Social Distancing ◽

Human Behaviors ◽

Mobility Data ◽

The Impact

BACKGROUND Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging is also important for minimizing long term effects of an outbreak. OBJECTIVE We used social media data to identify human behaviors relevant to COVID-19 transmission and the perceived impacts of COVID-19 on individuals as a first step toward real time monitoring of public perceptions to inform public health communications. METHODS We develop a coding schema for 6 categories and 11 subcategories, which includes both a wide number of behaviors, as well codes focused on the impacts of the pandemic (e.g., economic and mental health impacts). We use this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that perform adequately are applied to our remaining corpus and temporal and geospatial trends are assessed. We compare the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. RESULTS We apply our labeling schema to ~7200 tweets. The worst performing classifiers have F1 scores of only 0.18-0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, are much stronger with F1 scores of 0.64-0.66. We applied the social distancing classifiers to over 228 million tweets. We show temporal patterns consistent with real-world events, and show correlations of up to -0.5 between social distancing signals on Twitter and ground-truth mobility throughout the United States. CONCLUSIONS Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior as well as informing public health communication strategies by describing awareness of and compliance with suggested behaviors. CLINICALTRIAL N/A

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Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study (Preprint)

10.2196/preprints.24591 ◽

2020 ◽

Author(s):

Romain Garnier ◽

Jan R Benetka ◽

John Kraemer ◽

Shweta Bansal

Keyword(s):

United States ◽

Black People ◽

The United States ◽

Control Measures ◽

Mobility Patterns ◽

Poverty Level ◽

Social Distancing ◽

Mobility Data ◽

Black Populations ◽

The Impact

BACKGROUND Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. OBJECTIVE We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. METHODS We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. RESULTS We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. CONCLUSIONS Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts.

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Forecasting the Spread of COVID-19 under Different Reopening Strategies

10.1101/2020.05.26.20113993 ◽

2020 ◽

Cited By ~ 1

Author(s):

Meng Liu ◽

Raphael Thomadsen ◽

Song Yao

Keyword(s):

United States ◽

Growth Rate ◽

Population Density ◽

The United States ◽

Sir Model ◽

Social Distancing ◽

Mobility Data ◽

The Social ◽

The Us ◽

Case Data

ABSTRACTWe combine COVID-19 case data with demographic and mobility data to estimate a modified susceptible-infected-recovered (SIR) model for the spread of this disease in the United States. We find that the incidence of infectious COVID-19 individuals has a concave effect on contagion, as would be expected if people have inter-related social networks. We also demonstrate that social distancing and population density have large effects on the rate of contagion. The social distancing in late March and April substantially reduced the number of COVID-19 cases. However, the concave contagion pattern means that when social distancing measures are lifted, the growth rate is considerable but will not be exponential as predicted by standard SIR models. Furthermore, counties with the lowest population density could likely avoid high levels of contagion even with no social distancing. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19, about double what would occur if the US only restored to 50% of the way to normalcy.

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Socio-economic disparities in social distancing during the COVID-19 pandemic in the United States

10.1101/2020.11.07.20201335 ◽

2020 ◽

Author(s):

Romain Garnier ◽

Jan R. Benetka ◽

John Kraemer ◽

Shweta Bansal

Keyword(s):

United States ◽

Black People ◽

The United States ◽

Control Measures ◽

Mobility Patterns ◽

Poverty Level ◽

Social Distancing ◽

Mobility Data ◽

Black Populations ◽

The Impact

AbstractImportanceEliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic.ObjectiveTo assess how mobility patterns have varied across the United States during the COVID-19 pandemic, and identify associations with socio-economic factors of populations.Design, Setting, and ParticipantsWe used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level between February and May 2020. Using linear mixed models, we assessed the associations between social distancing and socio-economic variables, including the proportion of people below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density.Main outcomes and ResultsWe find that the speed, depth, and duration of social distancing in the United States is heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; and in contrast, that social distancing is intense in counties with higher population densities and larger Black populations.Conclusions and relevanceSocio-economic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of COVID-19 in communities across the United States. This is likely to amplify existing health disparities, and needs to be addressed to ensure the success of ongoing pandemic mitigation efforts.

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Forecasting the spread of COVID-19 under different reopening strategies

Scientific Reports ◽

10.1038/s41598-020-77292-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Meng Liu ◽

Raphael Thomadsen ◽

Song Yao

Keyword(s):

United States ◽

Social Networks ◽

Exponential Growth ◽

The United States ◽

Sir Model ◽

Disease Spread ◽

Social Distancing ◽

Mobility Data ◽

The Us ◽

Case Data

AbstractWe combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID-19 cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19.

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

Atmosphere ◽

10.3390/atmos12080951 ◽

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.

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Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics

10.1101/2020.06.05.20123760 ◽

2020 ◽

Author(s):

Aniruddha Adiga ◽

Lijing Wang ◽

Adam Sadilek ◽

Ashish Tendulkar ◽

Srinivasan Venkatramanan ◽

...

Keyword(s):

Human Mobility ◽

The United States ◽

Global Scale ◽

International Travel ◽

Population Mixing ◽

Government Interventions ◽

Social Distancing ◽

Epidemic Dynamics ◽

The Us ◽

The Impact

AbstractThis work quantifies the impact of interventions to curtail mobility and social interactions in order to control the COVID-19 pandemic. We analyze the change in world-wide mobility at multiple spatio-temporal resolutions – county, state, country – using an anonymized aggregate mobility map that captures population flows between geographic cells of size 5 km2. We show that human mobility underwent an abrupt and significant change, partly in response to the interventions, resulting in 87% reduction of international travel and up to 75% reduction of domestic travel. Taking two very different countries sampled from the global spectrum, we observe a maximum reduction of 42% in mobility across different states of the United States (US), and a 68% reduction across the states of India between late March and late April. Since then, there has been an uptick in flows, with the US seeing 53% increase and India up to 38% increase with respect to flows seen during the lockdown. As we overlay this global map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell – often before stay-at-home orders were issued. Further, in order to understand mixing within a region, we propose a new metric to quantify the effect of social distancing on the basis of mobility. We find that population mixing has decreased considerably as the pandemic has progressed and are able to measure this effect across the world. Finally, we carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. To our knowledge, this work is the first to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.

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A transnational and transregional study of the impact and effectiveness of social distancing for COVID-19 mitigation

10.1101/2021.09.01.21262990 ◽

2021 ◽

Author(s):

Tarcísio M. Rocha Filho ◽

Marcelo A. Moret ◽

José F. F. Mendes

Keyword(s):

United States ◽

Infection Rate ◽

Rank Order ◽

The United States ◽

Social Distancing ◽

The United Kingdom ◽

The Social ◽

The Impact ◽

The Relationship ◽

Spearman’S Correlation

AbstractWe present an analysis of the relationship between SARS-CoV-2 infection rates and a social distancing metric from data for all the states and most populous cities in the United States and Brazil, all the 22 European Economic Community countries and the United Kingdom. We discuss why the infection rate, instead of the effective reproduction number or growth rate of cases, is a proper choice to perform this analysis when considering a wide span of time. We obtain a strong Spearman’s rank order correlation between the social distancing metric and the infection rate in each locality. We show that mask mandates increase the values of Spearman’s correlation in the United States, where a mandate was adopted. We also obtain an explicit numerical relation between the infection rate and the social distancing metric defined in the present work.

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A Transnational and Transregional Study of the Impact and Effectiveness of Social Distancing for COVID-19 Mitigation

Entropy ◽

10.3390/e23111530 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1530

Author(s):

Tarcísio M. Rocha Filho ◽

Marcelo A. Moret ◽

José F. F. Mendes

Keyword(s):

United States ◽

Infection Rate ◽

Rank Order ◽

The United States ◽

Social Distancing ◽

The United Kingdom ◽

The Social ◽

The Impact ◽

The Relationship ◽

Spearman’S Correlation

We present an analysis of the relationship between SARS-CoV-2 infection rates and a social distancing metric from data for all the states and most populous cities in the United States and Brazil, all the 22 European Economic Community countries and the United Kingdom. We discuss why the infection rate, instead of the effective reproduction number or growth rate of cases, is a proper choice to perform this analysis when considering a wide span of time. We obtain a strong Spearman’s rank order correlation between the social distancing metric and the infection rate in each locality. We show that mask mandates increase the values of Spearman’s correlation in the United States, where a mandate was adopted. We also obtain an explicit numerical relation between the infection rate and the social distancing metric defined in the present work.

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