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 Spatial-temporal relationship between population mobility and COVID-19 outbreaks in South Carolina: A time series forecasting analysis

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

Background: Population mobility is closely associated with coronavirus 2019 (COVID-19) transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive non-pharmaceutical interventions for disease control. South Carolina (SC) is one of the states which reopened early and then suffered from a sharp increase of COVID-19. Objective: To examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility to predict daily new cases at both state- and county- levels in SC. Methods: This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020 in SC and its top five counties with the largest number of cumulative confirmed cases. Daily new case was calculated by subtracting the cumulative confirmed cases of previous day from the total cases. Population mobility was assessed using the number of users with travel distance larger than 0.5 mile which was calculated based on their geotagged twitters. Poisson count time series model was employed to carry out the research goals. Results: Population mobility was positively associated with state-level daily COVID-19 incidence and those of the top five counties (i.e., Charleston, Greenville, Horry, Spartanburg, Richland). At the state-level, final model with time window within the last 7-day 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-, 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% to 74.5%. Conclusions: Population mobility was positively associated with COVID-19 incidences at both state- and county- levels in SC. Using Twitter-based mobility data could provide acceptable prediction for COVID-19 daily new cases. Population mobility measured via social media platform could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

scholarly journals Soil Carbon Regulating Ecosystem Services in the State of South Carolina, USA

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 309
Author(s):  
Elena A. Mikhailova ◽  
Hamdi A. Zurqani ◽  
Christopher J. Post ◽  
Mark A. Schlautman ◽  
Gregory C. Post ◽  
...  
Keyword(s):  
South Carolina ◽  
Ecosystem Services ◽  
Soil Carbon ◽  
Soil Depth ◽  
State Level ◽  
The United States ◽  
The State ◽  
Soil C ◽  
Low Country ◽  
Pee Dee

Sustainable management of soil carbon (C) at the state level requires valuation of soil C regulating ecosystem services (ES) and disservices (ED). The objective of this study was to assess the value of regulating ES from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for the state of South Carolina (SC) in the United States of America (U.S.A.) by soil order, soil depth (0–200 cm), region and county using information from the State Soil Geographic (STATSGO) database. The total estimated monetary mid-point value for TSC in the state of South Carolina was $124.36B (i.e., $124.36 billion U.S. dollars, where B = billion = 109), $107.14B for SOC, and $17.22B for SIC. Soil orders with the highest midpoint value for SOC were: Ultisols ($64.35B), Histosols ($11.22B), and Inceptisols ($10.31B). Soil orders with the highest midpoint value for SIC were: Inceptisols ($5.91B), Entisols ($5.53B), and Alfisols ($5.0B). Soil orders with the highest midpoint value for TSC were: Ultisols ($64.35B), Inceptisols ($16.22B), and Entisols ($14.65B). The regions with the highest midpoint SOC values were: Pee Dee ($34.24B), Low Country ($32.17B), and Midlands ($29.24B). The regions with the highest midpoint SIC values were: Low Country ($5.69B), Midlands ($5.55B), and Pee Dee ($4.67B). The regions with the highest midpoint TSC values were: Low Country ($37.86B), Pee Dee ($36.91B), and Midlands ($34.79B). The counties with the highest midpoint SOC values were Colleton ($5.44B), Horry ($5.37B), and Berkeley ($4.12B). The counties with the highest midpoint SIC values were Charleston ($1.46B), Georgetown ($852.81M, where M = million = 106), and Horry ($843.18M). The counties with the highest midpoint TSC values were Horry ($6.22B), Colleton ($6.02B), and Georgetown ($4.87B). Administrative areas (e.g., counties, regions) combined with pedodiversity concepts can provide useful information to design cost-efficient policies to manage soil carbon regulating ES at the state level.

scholarly journals Predictors of Death Rate during the COVID-19 Pandemic

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 339 ◽  
Author(s):  
Ian Feinhandler ◽  
Benjamin Cilento ◽  
Brad Beauvais ◽  
Jordan Harrop ◽  
Lawrence Fulton
Keyword(s):  
Health Status ◽  
Repeated Measures ◽  
Regression Models ◽  
State Level ◽  
Political Influence ◽  
The State ◽  
The Political ◽  
County Level ◽  
Political Factor ◽  
Death Rates

Coronavirus (COVID-19) is a potentially fatal viral infection. This study investigates geography, demography, socioeconomics, health conditions, hospital characteristics, and politics as potential explanatory variables for death rates at the state and county levels. Data from the Centers for Disease Control and Prevention, the Census Bureau, Centers for Medicare and Medicaid, Definitive Healthcare, and USAfacts.org were used to evaluate regression models. Yearly pneumonia and flu death rates (state level, 2014–2018) were evaluated as a function of the governors’ political party using a repeated measures analysis. At the state and county level, spatial regression models were evaluated. At the county level, we discovered a statistically significant model that included geography, population density, racial and ethnic status, three health status variables along with a political factor. A state level analysis identified health status, minority status, and the interaction between governors’ parties and health status as important variables. The political factor, however, did not appear in a subsequent analysis of 2014–2018 pneumonia and flu death rates. The pathogenesis of COVID-19 has a greater and disproportionate effect within racial and ethnic minority groups, and the political influence on the reporting of COVID-19 mortality was statistically relevant at the county level and as an interaction term only at the state level.

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 Effectiveness of COVID-19 shelter-in-place orders varied by state

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0245008
Author(s):  
Yevgeniy Feyman ◽  
Jacob Bor ◽  
Julia Raifman ◽  
Kevin N. Griffith
Keyword(s):  
Population Density ◽  
Study Design ◽  
Regression Discontinuity ◽  
State Level ◽  
County Level ◽  
Opportunities To Learn ◽  
Mobility Data ◽  
Percentage Points ◽  
Reduced Mobility ◽  
The U.S

State “shelter-in-place” (SIP) orders limited the spread of COVID-19 in the U.S. However, impacts may have varied by state, creating opportunities to learn from states where SIPs have been effective. Using a novel dataset of state-level SIP order enactment and county-level mobility data form Google, we use a stratified regression discontinuity study design to examine the effect of SIPs in all states that implemented them. We find that SIP orders reduced mobility nationally by 12 percentage points (95% CI: -13.1 to -10.9), however the effects varied substantially across states, from -35 percentage points to +11 percentage points. Larger reductions were observed in states with higher incomes, higher population density, lower Black resident share, and lower 2016 vote shares for Donald J. Trump. This suggests that optimal public policies during a pandemic will vary by state and there is unlikely to be a “one-size fits all” approach that works best.

Estimating the Effects of Non-Pharmaceutical Interventions (NPIs) and Population Mobility on Daily COVID-19 Cases: Evidence from Ontario

2020 ◽  
Vol COVID-19 ◽  
pp. e2021022
Author(s):  
Nathaniel T. Stevens ◽  
Anindya Sen ◽  
Francis Kiwon ◽  
Plinio P. Morita ◽  
Stefan H. Steiner ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Instrumental Variables ◽  
Time Series Models ◽  
Population Mobility ◽  
Simultaneity Bias ◽  
Mobility Data ◽  
Business Operations ◽  
Iv Estimation ◽  
Pharmaceutical Interventions

This study employs COVID-19 case counts and Google mobility data for twelve of Ontario’s largest Public Health Units from Spring 2020 until the end of January 2021 to evaluate the effects of Non-Pharmaceutical Interventions (NPIs: policy restrictions on business operations and social gatherings) and population mobility on daily cases. Instrumental Variables (IV) estimation is used to account for potential simultaneity bias, as both daily COVID-19 cases and NPIs are dependent on lagged case numbers. IV estimates based on differences in lag lengths to infer causal estimates, imply that the implementation of stricter NPIs and indoor mask mandates are associated with COVID-19 case reductions. Further, estimates based on Google mobility data suggest that increases in workplace attendance are correlated with higher case counts. Finally, from October 2020 to January 2021, daily Ontario forecasts from Box-Jenkins time-series models are more accurate than official forecasts and forecasts from a Susceptible-Infected-Removed (SIR) epidemiology model.

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.

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 characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic

2020 ◽  
Author(s):  
Xiao Huang ◽  
Zhenlong Li ◽  
Yuqin Jiang ◽  
Xinyue Ye ◽  
Chengbin Deng ◽  
...  
Keyword(s):  
Time Series ◽  
Human Mobility ◽  
County Level ◽  
Lower Income ◽  
Mobility Patterns ◽  
Weighted Distance ◽  
Social Distancing ◽  
Mobility Data ◽  
Positive Correlations ◽  
The U.S

This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S. during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties. We collect, process, and compute mobility data from four sources: 1) Apple mobility trend reports, 2) Google community mobility reports, 3) mobility data from Descartes Labs, and 4) Twitter mobility calculated via weighted distance. We further design a Responsive Index (RI) based on the time series of mobility change percentages to quantify the general degree of mobility-based responsiveness to COVID-19 at the U.S. county level. We find statistically significant positive correlations in the RI between either two data sources, revealing their general similarity, albeit with varying Pearson r coefficients. Despite the similarity, however, mobility from each source presents unique and even contrasting characteristics, in part demonstrating the multifaceted nature of human mobility. The positive correlation between RI and income at the county level is significant in all mobility datasets, suggesting that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic. Most states present a positive difference in RI between their upper-income and lower-income counties, where diverging patterns in time series of mobility changes percentages can be found. To our best knowledge, this is the first study that cross-compares multi-source mobility datasets. The findings shed light on not only the characteristics of multi-source mobility data but also the mobility patterns in tandem with the economic disparity.

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