scholarly journals Social distancing beliefs and human mobility: Evidence from Twitter

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0246949 ◽  
Author(s):  
Simon Porcher ◽  
Thomas Renault
Keyword(s):  
Social Networks ◽  
Human Mobility ◽  
State Level ◽  
The State ◽  
Policy Makers ◽  
Social Distancing ◽  
Mobility Data ◽  
Twitter Data ◽  
Precipitation And Temperature

We construct a novel database containing hundreds of thousands geotagged messages related to the COVID-19 pandemic sent on Twitter. We create a daily index of social distancing—at the state level—to capture social distancing beliefs by analyzing the number of tweets containing keywords such as “stay home”, “stay safe”, “wear mask”, “wash hands” and “social distancing”. We find that an increase in the Twitter index of social distancing on day t-1 is associated with a decrease in mobility on day t. We also find that state orders, an increase in the number of COVID-19 cases, precipitation and temperature contribute to reducing human mobility. Republican states are also less likely to enforce social distancing. Beliefs shared on social networks could both reveal the behavior of individuals and influence the behavior of others. Our findings suggest that policy makers can use geotagged Twitter data—in conjunction with mobility data—to better understand individual voluntary social distancing actions.

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

2020 ◽  
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.

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

10.2196/21499 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e21499 ◽  
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.

scholarly journals Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Takahiro Yabe ◽  
Kota Tsubouchi ◽  
Naoya Fujiwara ◽  
Takayuki Wada ◽  
Yoshihide Sekimoto ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Social Contact ◽  
Mobility Patterns ◽  
State Of Emergency ◽  
Social Contacts ◽  
Policy Makers ◽  
Mobility Data ◽  
Contact Rates ◽  
Mobility Behavior

Abstract While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.

scholarly journals An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time

2021 ◽  
Vol 7 (10) ◽  
pp. eabd6989
Author(s):  
Nicole E. Kogan ◽  
Leonardo Clemente ◽  
Parker Liautaud ◽  
Justin Kaashoek ◽  
Nicholas B. Link ◽  
...  
Keyword(s):  
Early Warning ◽  
Data Streams ◽  
Human Mobility ◽  
Warning System ◽  
State Level ◽  
The United States ◽  
Digital Data ◽  
Mobility Data ◽  
Epidemiological Monitoring ◽  
Nonpharmaceutical Interventions

Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.

scholarly journals Using Mobility Data to Understand and Forecast COVID19 Dynamics

2020 ◽  
Author(s):  
Lijing Wang ◽  
Xue Ben ◽  
Aniruddha Adiga ◽  
Adam Sadilek ◽  
Ashish Tendulkar ◽  
...  
Keyword(s):  
Neural Network ◽  
Message Passing ◽  
Spatial Interaction ◽  
Human Mobility ◽  
State Level ◽  
Interaction Patterns ◽  
Disease Dynamics ◽  
Mobility Data ◽  
Spatio Temporal ◽  
The Impact

Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.

scholarly journals Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis (Preprint)

2021 ◽  
Author(s):  
Chengbo Zeng ◽  
Jiajia Zhang ◽  
Zhenlong Li ◽  
Xiaowen Sun ◽  
Bankole Olatosi ◽  
...  
Keyword(s):  
Time Series ◽  
South Carolina ◽  
Prediction Accuracy ◽  
Temporal Relationship ◽  
Time Window ◽  
State Level ◽  
The State ◽  
County Level ◽  
Population Mobility ◽  
Mobility Data

BACKGROUND Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. OBJECTIVE The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. METHODS This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. RESULTS Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. CONCLUSIONS Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

scholarly journals Transmission dynamics and forecasts of the COVID-19 pandemic in Mexico, March 20-November 11, 2020

2021 ◽  
Author(s):  
Amna Tariq ◽  
Juan M. Banda ◽  
Pavel Skums ◽  
Sushma Dahal ◽  
Carlos Castillo-Garsow ◽  
...  
Keyword(s):  
Mexico City ◽  
Disease Transmission ◽  
Growth Models ◽  
State Level ◽  
The State ◽  
Delayed Response ◽  
Mortality Trends ◽  
Social Distancing ◽  
Incidence Data ◽  
Mortality Forecasts

AbstractMexico has experienced one of the highest COVID-19 death rates in the world. A delayed response towards implementation of social distancing interventions until late March 2020 and a phased reopening of the country in June 2020 has facilitated sustained disease transmission in the region. Here, we systematically generate and compare 30-day ahead forecasts using previously validated growth models based on mortality trends from the Institute for Health Metrics and Evaluation for Mexico and Mexico City in near real-time. Moreover, we estimate reproduction numbers for SARS-CoV-2 based on methods that rely on genomic data as well as case incidence data. Subsequently, functional data analysis techniques are utilized to analyze the shapes of COVID-19 growth rate curves at the state level to characterize the spatial-temporal transmission patterns. The early estimates of reproduction number for Mexico were estimated between R∼1.1-from genomic and case incidence data. Moreover, the mean estimate of R has fluctuated ∼1.0 from late July till end of September 2020. The spatial analysis characterizes the state-level dynamics of COVID-19 into four groups with distinct epidemic trajectories. We found that the sequential mortality forecasts from the GLM and Richards model predict downward trends in the number of deaths for all thirteen forecasts periods for Mexico and Mexico City. The sub-epidemic and IHME models predict more realistic stable trajectory of COVID-19 mortality trends for the last three forecast periods (09/21-10/21 - 09/28-10/27) for Mexico and Mexico City. Our findings support the view that phenomenological models are useful tools for short-term epidemic forecasting albeit forecasts need to be interpreted with caution given the dynamic implementation and lifting of social distancing measures.

Abstract P91: Excess Cerebrovascular Mortality in the U.S. During the Covid-19 Pandemic

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Richa Sharma ◽  
Lindsey Kuohn ◽  
Daniel Weinberger ◽  
Joshua Warren ◽  
Lauren H Sansing ◽  
...  
Keyword(s):  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
The State ◽  
Social Distancing ◽  
Binomial Regression ◽  
Stroke Death ◽  
The U.S ◽  
Related Mortality ◽  
Health Statistic

Introduction: The magnitude and drivers of excess cerebrovascular-specific mortality during the coronavirus-19 (COVID-19) pandemic are unknown. We aim to quantify excess stroke-related death and characterize its association with psychosocial factors and emerging COVID-19 related mortality. Methods: U.S. and state-level excess cerebrovascular deaths from January-May 2020 were quantified by Poisson regression models built using National Center for Health Statistic (NCHS) data. Weekly excess cerebrovascular deaths in the U.S. were analyzed as functions of time-varying, weekly stroke-related EMS calls and weekly COVID-19 deaths by univariable linear regression. A state-level negative binomial regression analysis was performed to determine the association between excess cerebrovascular deaths and social distancing (degree of change in mobility per Google COVID-19 Community Mobility Reports) during the height of the pandemic after the first COVID-19 death (February 29, 2020), adjusting for cumulative COVID-19 related deaths and completeness of deaths attributable to COVID-19 in NCHS. Findings: There were 918 more cerebrovascular deaths than expected from January 1-May 16 th , 2020 in the U.S. Excess cerebrovascular mortality occurred during every week between March 28-May 2 nd , 2020, up to 7.8% during the week of April 18 th . Decreased stroke-related EMS calls were associated with excess stroke deaths one (β -0.06, 95% CI -0.11, -0.02) and two weeks (β -0.08, 95% CI -0.12, -0.04) later. There was no significant association between weekly excess stroke death and COVID-19 death. Twenty-three states and NYC experienced excess cerebrovascular mortality during the pandemic height. At the state level, a 10% increase in social distancing was associated with a 4.3% increase in stroke deaths (IRR 1.043, 95% CI 1.001–1.085) after adjusting for COVID-19 mortality. Conclusions: Excess U.S. cerebrovascular deaths during the COVID-19 pandemic were observed with decreases in stroke-related EMS calls nationally and less mobility at the state level. Public health measures are needed to identify and counter the reticence to seeking medical care for acute stroke during the COVID-19 pandemic.

scholarly journals Forecasting the spread of COVID-19 under different reopening strategies

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.

scholarly journals Quantifying the Impact of a Flood and Hurricane Event on Tree Farms in South Carolina: A Survey

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 546
Author(s):  
Patrick Hiesl ◽  
Shari L. Rodriguez
Keyword(s):  
South Carolina ◽  
Financial Aid ◽  
State Level ◽  
Forest Products ◽  
The State ◽  
Policy Makers ◽  
Family Forest ◽  
Forested Landscapes ◽  
Flooding Events ◽  
The Impact

Natural disturbances in forested landscapes are increasing in frequency. Hurricanes and flooding events can cause extreme damages to forested ecosystems and the forest products industry. The state of South Carolina experienced four major hurricanes and flooding events between 2015 and 2018. A survey was sent out to the members of the American Tree Farm System (ATFS) in South Carolina in 2017 to better understand the impact of two of these events—the historical flood of October 2015 and hurricane Matthew in October 2016—on family forest operations. Forty-eight percent of surveys were returned. Surveys were received from all counties except one. Average losses of $6.21/acre and $6.48/acre for flood and hurricane damage, respectively, were reported across all of the respondents. Major damage from the flood was reported to be on forest roads, while uprooted and broken trees were the most reported damage from the hurricane. Extrapolating damages to the state level indicated total estimated damages that were in excess of $80 million for each event. The responses also showed that only one-third of respondents were aware of disaster relief programs and less than 2% actually received financial aid. The results from this survey provide forest managers, policy makers, and extension personnel with information regarding the damages that were associated with the 2015 flood and the 2016 hurricane. Events such as these are bound to happen again in the future and information from this survey may allow foresters, policy makers, and forestry associations to refine the ways that financial aid information is distributed to increase the awareness of these programs.

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