texas county
Recently Published Documents


TOTAL DOCUMENTS

51
(FIVE YEARS 8)

H-INDEX

6
(FIVE YEARS 0)

2021 ◽  
Vol 116 (1) ◽  
pp. S1398-S1398
Author(s):  
Justin Stubbs ◽  
Luis Rodriguez ◽  
Mario Soliman ◽  
Christina Dela Cruz ◽  
Marissa A. Diaz de Leon ◽  
...  

Author(s):  
Jinting Zhang ◽  
Xiu Wu ◽  
T. Edwin Chow

As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental 14 impact’s indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, age structure, natural supply, economic condition, air quality, and medical care. We established the GWR model to seek the sensitive factors. The result shows that population, hospitalization, and economic condition are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).


2021 ◽  
Author(s):  
Jinting Zhang ◽  
Xiu Wu ◽  
T. Edwin Chow

Abstract As COVID-19 run rampant in high-density housing sites, it is important to use real-time data tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research is appropriately analyzed the disparities between spatial-temporal clusters, expectation Maximization clustering (EM) and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to South-east Texas analysis in GWR modeling. The sensitive period took place in the last two quarters in 2020. We explored Postgre application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental 14 impact’s indices to perform Principal Component Analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, age structure, natural supply, economic condition, air quality and medical care. We established the GWR model to seek the sensitive factors. The result shows that population, hospitalization, and economic condition are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility and satisfy for the need of Emergency Operations Plan (EOP).


2021 ◽  
Vol 149 ◽  
Author(s):  
Tuan D. Le ◽  
Michele Bosworth ◽  
Gerald Ledlow ◽  
Tony T. Le ◽  
Jeffrey Bell ◽  
...  

Abstract As the on-going severe acute respiratory syndrome coronavirus 2 pandemic, we aimed to understand whether economic reopening (EROP) significantly influenced coronavirus disease 2019 (COVID-19) incidence. COVID-19 data from Texas Health and Human Services between March and August 2020 were analysed. COVID-19 incidence rate (cases per 100 000 population) was compared to statewide for selected urban and rural counties. We used joinpoint regression analysis to identify changes in trends of COVID-19 incidence and interrupted time-series analyses for potential impact of state EROP orders on COVID-19 incidence. We found that the incidence rate increased to 145.1% (95% CI 8.4–454.5%) through 4th April, decreased by 15.5% (95% CI −24.4 −5.9%) between 5th April and 30th May, increased by 93.1% (95% CI 60.9–131.8%) between 31st May and 11th July and decreased by 13.2% (95% CI −22.2 −3.2%) after 12 July 2020. The study demonstrates the EROP policies significantly impacted trends in COVID-19 incidence rates and accounted for increases of 129.9 and 164.6 cases per 100 000 populations for the 24- or 17-week model, respectively, along with other county and state reopening ordinances. The incidence rate decreased sharply after 12th July considering the emphasis on a facemask or covering requirement in business and social settings.


2019 ◽  
Vol 31 (3) ◽  
Author(s):  
Jon Lasser ◽  
Eric Schmidt ◽  
James Diep ◽  
Amy Huebel

This article reports data collected in a rural Texas county that explores the beliefs and perceptions of youth about alcohol use. Results from the study suggest high rates of underage drinking and present significant health risks. The data also shed some light on how yuth perceive parents, responsibilities, access, and prevention strategies with regard to alcohol usage. Implications for rural educators and health care providers are discussed in light of the findings, with an emphasis on both prevention and intervention.


2018 ◽  
Author(s):  
Kate Broome
Keyword(s):  

Author(s):  
Timothy K. Perttula

An ancestral Caddo ceramic vessel from East Texas has been recently donated to the collections of the Texas Archeological Research Laboratory at The University of Texas (TARL). The vessel comes from an unknown site in an unknown East Texas county, and had been purchased in Marshall, Texas some years ago. The decorative style of the ceramic vessel, however, does indicate the defined ceramic type, its likely age, and where in East Texas the ceramic vessel was likely manufactured.


Sign in / Sign up

Export Citation Format

Share Document