The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets (Preprint)

BACKGROUND Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and “flattens the curve” so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. OBJECTIVE The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. METHODS We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. RESULTS We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. CONCLUSIONS We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning.

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The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets

Journal of Medical Internet Research ◽

10.2196/21499 ◽

2020 ◽

Vol 22 (12) ◽

pp. e21499 ◽

Cited By ~ 2

Author(s):

Paiheng Xu ◽

Mark Dredze ◽

David A Broniatowski

Keyword(s):

United States ◽

South Carolina ◽

Social Mobility ◽

State Level ◽

The United States ◽

Mobility Index ◽

Social Distancing ◽

Mobility Data ◽

Response Planning ◽

Twitter Data

Background Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and “flattens the curve” so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. Objective The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. Methods We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. Results We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. Conclusions We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning.

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Impacts of social distancing policies on mobility and COVID-19 case growth in the US

Nature Communications ◽

10.1038/s41467-021-23404-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Gregory A. Wellenius ◽

Swapnil Vispute ◽

Valeria Espinosa ◽

Alex Fabrikant ◽

Thomas C. Tsai ◽

...

Keyword(s):

Public Health ◽

United States ◽

Infection Control ◽

State Level ◽

The United States ◽

Social Consequences ◽

Social Distancing ◽

Mobility Data ◽

The Us ◽

Public Health Officials

AbstractSocial distancing remains an important strategy to combat the COVID-19 pandemic in the United States. However, the impacts of specific state-level policies on mobility and subsequent COVID-19 case trajectories have not been completely quantified. Using anonymized and aggregated mobility data from opted-in Google users, we found that state-level emergency declarations resulted in a 9.9% reduction in time spent away from places of residence. Implementation of one or more social distancing policies resulted in an additional 24.5% reduction in mobility the following week, and subsequent shelter-in-place mandates yielded an additional 29.0% reduction. Decreases in mobility were associated with substantial reductions in case growth two to four weeks later. For example, a 10% reduction in mobility was associated with a 17.5% reduction in case growth two weeks later. Given the continued reliance on social distancing policies to limit the spread of COVID-19, these results may be helpful to public health officials trying to balance infection control with the economic and social consequences of these policies.

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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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Effects of Government Mandated Social Distancing Measures on Cumulative Incidence of COVID-19 in the United States and its Most Populated Cities

10.1101/2020.05.22.20110460 ◽

2020 ◽

Cited By ~ 3

Author(s):

Bhuma Krishnamachari ◽

Alexander Morris ◽

Diane Zastrow ◽

Andrew Dsida ◽

Brian Harper ◽

...

Keyword(s):

United States ◽

Population Density ◽

Cumulative Incidence ◽

Negative Binomial ◽

State Level ◽

The United States ◽

Reverse Causality ◽

Social Distancing ◽

School Closing ◽

Adjusted Rate

AbstractCOVID-19, caused by the SARS-CoV-2 virus, has quickly spread throughout the world, necessitating assessment of the most effective containment methods. Very little research exists on the effects of social distancing measures on this pandemic. The purpose of this study was to examine the effects of government implemented social distancing measures on the cumulative incidence rates of COVID-19 in the United States on a state level, and in the 25 most populated cities, while adjusting for socio-demographic risk factors. The social distancing variables assessed in this study were: days to closing of non-essential business; days to stay home orders; days to restrictions on gathering, days to restaurant closings and days to school closing. Using negative binomial regression, adjusted rate ratios and 95% confidence intervals were calculated comparing two levels of a binary variable: “above median value,” and “median value and below” for days to implementing a social distancing measure. For city level data, the effects of these social distancing variables were also assessed in high (above median value) vs low (median value and below) population density cities. For the state level analysis, days to school closing was associated with cumulative incidence, with an adjusted rate ratio of 1.59 (95% CI:1.03,2.44), p=0.04 at 35 days. Some results were counterintuitive, including inverse associations between cumulative incidence and days to closure of non-essential business and restrictions on gatherings. This finding is likely due to reverse causality, where locations with slower growth rates initially chose not to implement measures, and later implemented measures when they absolutely needed to respond to increasing rates of infection. Effects of social distancing measures seemed to vary by population density in cities. Our results suggest that the effect of social distancing measures may differ between states and cities and between locations with different population densities. States and cities need individual approaches to containment of an epidemic, with an awareness of their own structure in terms of crowding and socio-economic variables. In an effort to reduce infection rates, cities may want to implement social distancing in advance of state mandates.

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Statewide Interventions and Covid-19 Mortality in the United States: An Observational Study

Clinical Infectious Diseases ◽

10.1093/cid/ciaa923 ◽

2020 ◽

Cited By ~ 2

Author(s):

Nadir Yehya ◽

Atheendar Venkataramani ◽

Michael O Harhay

Keyword(s):

United States ◽

Negative Binomial ◽

State Level ◽

Negative Binomial Regression ◽

The United States ◽

School Closure ◽

Sensitivity Analyses ◽

Test Model ◽

School Closures ◽

Social Distancing

ABSTRACT Background Social distancing is encouraged to mitigate viral spreading during outbreaks. However, the association between distancing and patient-centered outcomes in Covid-19 has not been demonstrated. In the United States social distancing orders are implemented at the state level with variable timing of onset. Emergency declarations and school closures were two early statewide interventions. Methods To determine whether later distancing interventions were associated with higher mortality, we performed a state-level analysis in 55,146 Covid-19 non-survivors. We tested the association between timing of emergency declarations and school closures with 28-day mortality using multivariable negative binomial regression. Day 1 for each state was set to when they recorded ≥ 10 deaths. We performed sensitivity analyses to test model assumptions. Results At time of analysis, 37 of 50 states had ≥ 10 deaths and 28 follow-up days. Both later emergency declaration (adjusted mortality rate ratio [aMRR] 1.05 per day delay, 95% CI 1.00 to 1.09, p=0.040) and later school closure (aMRR 1.05, 95% CI 1.01 to 1.09, p=0.008) were associated with more deaths. When assessing all 50 states and setting day 1 to the day a state recorded its first death, delays in declaring an emergency (aMRR 1.05, 95% CI 1.01 to 1.09, p=0.020) or closing schools (aMRR 1.06, 95% CI 1.03 to 1.09, p<0.001) were associated with more deaths. Results were unchanged when excluding New York and New Jersey. Conclusions Later statewide emergency declarations and school closure were associated with higher Covid-19 mortality. Each day of delay increased mortality risk 5 to 6%.

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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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Social Media as an Early Proxy for Social Distancing Indicated by the COVID-19 Reproduction Number: Observational Study (Preprint)

10.2196/preprints.21340 ◽

2020 ◽

Author(s):

Joseph Younis ◽

Harvy Freitag ◽

Jeremy S Ruthberg ◽

Jonathan P Romanes ◽

Craig Nielsen ◽

...

Keyword(s):

United States ◽

Social Media ◽

Observational Study ◽

Social Mobility ◽

Reproduction Number ◽

The United States ◽

Google Trends ◽

Basic Reproductive Number ◽

Social Distancing ◽

Google Search

BACKGROUND The magnitude and time course of the COVID-19 epidemic in the United States depends on early interventions to reduce the basic reproductive number to below 1. It is imperative, then, to develop methods to actively assess where quarantine measures such as social distancing may be deficient and suppress those potential resurgence nodes as early as possible. OBJECTIVE We ask if social media is an early indicator of public social distancing measures in the United States by investigating its correlation with the time-varying reproduction number (Rt) as compared to social mobility estimates reported from Google and Apple Maps. METHODS In this observational study, the estimated Rt was obtained for the period between March 5 and April 5, 2020, using the EpiEstim package. Social media activity was assessed using queries of “social distancing” or “#socialdistancing” on Google Trends, Instagram, and Twitter, with social mobility assessed using Apple and Google Maps data. Cross-correlations were performed between Rt and social media activity or mobility for the United States. We used Pearson correlations and the coefficient of determination (ρ) with significance set to P<.05. RESULTS Negative correlations were found between Google search interest for “social distancing” and Rt in the United States (P<.001), and between search interest and state-specific Rt for 9 states with the highest COVID-19 cases (P<.001); most states experienced a delay varying between 3-8 days before reaching significance. A negative correlation was seen at a 4-day delay from the start of the Instagram hashtag “#socialdistancing” and at 6 days for Twitter (P<.001). Significant correlations between Rt and social media manifest earlier in time compared to social mobility measures from Google and Apple Maps, with peaks at –6 and –4 days. Meanwhile, changes in social mobility correlated best with Rt at –2 days and +1 day for workplace and grocery/pharmacy, respectively. CONCLUSIONS Our study demonstrates the potential use of Google Trends, Instagram, and Twitter as epidemiological tools in the assessment of social distancing measures in the United States during the early course of the COVID-19 pandemic. Their correlation and earlier rise and peak in correlative strength with Rt when compared to social mobility may provide proactive insight into whether social distancing efforts are sufficiently enacted. Whether this proves valuable in the creation of more accurate assessments of the early epidemic course is uncertain due to limitations. These limitations include the use of a biased sample that is internet literate with internet access, which may covary with socioeconomic status, education, geography, and age, and the use of subtotal social media mentions of social distancing. Future studies should focus on investigating how social media reactions change during the course of the epidemic, as well as the conversion of social media behavior to actual physical behavior.

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Social Media as an Early Proxy for Social Distancing Indicated by the COVID-19 Reproduction Number: Observational Study

JMIR Public Health and Surveillance ◽

10.2196/21340 ◽

2020 ◽

Vol 6 (4) ◽

pp. e21340 ◽

Cited By ~ 1

Author(s):

Joseph Younis ◽

Harvy Freitag ◽

Jeremy S Ruthberg ◽

Jonathan P Romanes ◽

Craig Nielsen ◽

...

Keyword(s):

United States ◽

Social Media ◽

Observational Study ◽

Social Mobility ◽

Reproduction Number ◽

The United States ◽

Google Trends ◽

Basic Reproductive Number ◽

Social Distancing ◽

Google Search

Background The magnitude and time course of the COVID-19 epidemic in the United States depends on early interventions to reduce the basic reproductive number to below 1. It is imperative, then, to develop methods to actively assess where quarantine measures such as social distancing may be deficient and suppress those potential resurgence nodes as early as possible. Objective We ask if social media is an early indicator of public social distancing measures in the United States by investigating its correlation with the time-varying reproduction number (Rt) as compared to social mobility estimates reported from Google and Apple Maps. Methods In this observational study, the estimated Rt was obtained for the period between March 5 and April 5, 2020, using the EpiEstim package. Social media activity was assessed using queries of “social distancing” or “#socialdistancing” on Google Trends, Instagram, and Twitter, with social mobility assessed using Apple and Google Maps data. Cross-correlations were performed between Rt and social media activity or mobility for the United States. We used Pearson correlations and the coefficient of determination (ρ) with significance set to P<.05. Results Negative correlations were found between Google search interest for “social distancing” and Rt in the United States (P<.001), and between search interest and state-specific Rt for 9 states with the highest COVID-19 cases (P<.001); most states experienced a delay varying between 3-8 days before reaching significance. A negative correlation was seen at a 4-day delay from the start of the Instagram hashtag “#socialdistancing” and at 6 days for Twitter (P<.001). Significant correlations between Rt and social media manifest earlier in time compared to social mobility measures from Google and Apple Maps, with peaks at –6 and –4 days. Meanwhile, changes in social mobility correlated best with Rt at –2 days and +1 day for workplace and grocery/pharmacy, respectively. Conclusions Our study demonstrates the potential use of Google Trends, Instagram, and Twitter as epidemiological tools in the assessment of social distancing measures in the United States during the early course of the COVID-19 pandemic. Their correlation and earlier rise and peak in correlative strength with Rt when compared to social mobility may provide proactive insight into whether social distancing efforts are sufficiently enacted. Whether this proves valuable in the creation of more accurate assessments of the early epidemic course is uncertain due to limitations. These limitations include the use of a biased sample that is internet literate with internet access, which may covary with socioeconomic status, education, geography, and age, and the use of subtotal social media mentions of social distancing. Future studies should focus on investigating how social media reactions change during the course of the epidemic, as well as the conversion of social media behavior to actual physical behavior.

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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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