Quantifying the social distancing privilege gap: a longitudinal study of smartphone movement

Dasgupta, Nabarun; M, Jonsson Funk; Lazard, Allison J.; Be, White; Marshall, Stephen W.

doi:10.1101/2020.05.03.20084624

Cited by 36 publications

(42 citation statements)

References 23 publications

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“…Second, we contribute to the small but rapidly growing literature using aggregate GPS data to study the effect of policies trying to contain the spread of COVID-19 on mobility patterns [Allcott et al, 2020, Dasgupta et al, 2020. Using GPS data is one of the main alternatives to using surveys [Briscese et al, 2020, Jørgensen et al, 2020, which likely do not provide reliable data on mobility due to social desirability bias [Daoust et al, 2020].…”

Section: Introductionmentioning

confidence: 99%

Compulsory Face Mask Policies Do not Affect Community Mobility in Germany

2020

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There is currently a heated debate about making face masks compulsory in public spaces to contain COVID-19. A key concern is that such policies could lead to risk compensating behaviour and thereby undermine efforts to maintain social distancing and reduce mobility. We provide first evidence on the impact of compulsory face mask policies on community mobility. We exploit the staggered implementation of policies by German states and measure community mobility using geolocated smartphone data. We find no evidence suggesting that compulsory masking policies affect community mobility in Germany. We can rule out even small increases larger than 0.03 standard deviations.

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Section: Introductionmentioning

confidence: 99%

Compulsory Face Mask Policies Do not Affect Community Mobility in Germany

2020

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“…Previous studies using mobility data have also suggested the potential for inequalities in the ability to practice social distancing based on income 20 , race and education 19 , or on the availability of healthcare providers 21 . However, most of these studies consider these determinants in isolation and do not allow to disentangle the potential additive effects the socioeconomic make-up of counties on the ability of their population to practice social distancing.…”

Section: Introductionmentioning

confidence: 99%

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

Garnier

Benetka²,

Kraemer

et al. 2020

Preprint

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ImportanceEliminating 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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“…In terms of the number of users that are included in Colocation Maps, we have decent coverage for most countries in Europe, North America, South America, South Asia and Southeast Asia, but less coverage in other parts of Asia, Africa and Oceania. Figure 4 compares the age distribution of people who are included in Colocation Maps to distributions from the high-resolution settlement layer (HRSL) and from population data from the United Nations (UN) 8 when HRSL is not available. The HRSL is a dataset, built by data scientists at Facebook based only on satellite data, which provides state-of-the-art estimates of population density in many regions of the world [51].…”

Section: Representativenessmentioning

confidence: 99%

Large-Scale Measurement of Aggregate Human Colocation Patterns for Epidemiological Modeling

Iyer

Karrer

Citron

et al. 2020

Preprint

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To understand and model public health emergencies, epidemiologists need data that describes how humans are moving and interacting across physical space. Such data has traditionally been difficult for researchers to obtain with the temporal resolution and geographic breadth that is needed to study, for example, a global pandemic. This paper describes Colocation Maps, which are spatial network datasets that have been developed within Facebook’s Data For Good program. These Maps estimate how often people from different regions are colocated: in particular, for a pair of geographic regions x and y, these Maps estimate the probability that a randomly chosen person from x and a randomly chosen person from y are simultaneously located in the same place during a randomly chosen minute in a given week. These datasets are well suited to parametrize metapopulation models of disease spread or to measure temporal changes in interactions between people from different regions; indeed, they have already been used for both of these purposes during the COVID-19 pandemic. In this paper, we show how Colocation Maps differ from existing data sources, describe how the datasets are built, provide examples of their use in compartmental modeling, and summarize ideas for further development of these and related datasets. We also conduct the first large-scale analysis of human colocation patterns across the world. Among the findings of this study, we observe that a pair of regions can exhibit high colocation despite few people moving between them. We also find that although few pairs of people are colocated for many days over the course of a week, these pairs can contribute significant fractions of the total colocation time within a region or between pairs of regions.

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Quantifying the social distancing privilege gap: a longitudinal study of smartphone movement

Cited by 36 publications

References 23 publications

Compulsory Face Mask Policies Do not Affect Community Mobility in Germany

Compulsory Face Mask Policies Do not Affect Community Mobility in Germany

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

Large-Scale Measurement of Aggregate Human Colocation Patterns for Epidemiological Modeling

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