ArticleEnvironmental science and pollution research international2021
Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR).
Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review.The Science of the total environment · 2022Pooled it
- Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Mapping the COVID-19 pandemic in Burkina Faso: spatial patterns, socioeconomic factors, and public health implications.International journal of health geographics · 2026Article
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- Geographical Disparities and Socioeconomic Determinants of Untreated Dental Caries in Permanent Teeth Across Asia, 1990-2019: A Retrospective Population-Based Burden Analysis.International journal of dentistry · 2026Article
- County-Level Hotspot Identification and Spatial Regression Analysis of Health Loss from Kashin-Beck Disease - China, 2019 and 2023.China CDC weekly · 2025Article
- A Bayesian spatio-temporal model of COVID-19 spread in England.Scientific reports · 2024Article
- Article
- Tokyo's COVID-19: An urban perspective on factors influencing infection rates in a global city.Sustainable cities and society · 2023Article
- Analysis of spatial characteristics and geographic weighted regression of tuberculosis prevalence in Kashgar, China.Preventive medicine reports · 2023Article
- Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia.BMC public health · 2023Article
- COVID-19 spread prediction using socio-demographic and mobility-related data.Cities (London, England) · 2023Article
- Understanding the spatial heterogeneity of COVID-19 vaccination uptake in England.BMC public health · 2023Article
- Revealing the spatiotemporal characteristics of the general public's panic levels during the pandemic crisis in China.Transactions in GIS : TG · 2022Article
- Identifying the Determinants of Distribution ofPathogens (Basel, Switzerland) · 2022Article
- Spatial analysis of socio-economic factors and their relationship with the cases of COVID-19 in Pernambuco, Brazil.Tropical medicine & international health : TM & IH · 2022Article
- The lockdown, mobility, and spatial health disparities in COVID-19 pandemic: A case study of New York City.Cities (London, England) · 2022Article
- Article
- Local Characteristics Related to SARS-CoV-2 Transmissions in the Seoul Metropolitan Area, South Korea.International journal of environmental research and public health · 2021Article
- Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States.International journal of environmental research and public health · 2021Article
Corrections and comments
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2 authors.
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Abstract
Since COVID-19 is extremely threatening to human health, it is significant to determine its impact factors to curb the virus spread. To tackle the complexity of COVID-19 expansion on a spatial-temporal scale, this research appropriately analyzed the spatial-temporal heterogeneity at the county-level in Texas. First, the impact factors of COVID-19 are captured on social, economic, and environmental multiple facets, and the communality is extracted through principal component analysis (PCA). Second, this research uses COVID-19 cumulative case as the dependent variable and the common factors as the independent variables. According to the virus prevalence hierarchy, the spatial-temporal disparity is categorized into four quarters in the GWR analysis model. The findings exhibited that GWR models provide higher fitness and more geodata-oriented information than OLS models. In El Paso, Odessa, Midland, Randall, and Potter County areas in Texas, population, hospitalization, and age structures are presented as static, positive influences on COVID-19 cumulative cases, indicating that they should adopt stringent strategies in curbing COVID-19. Winter is the most sensitive season for the virus spread, implying that the last quarter should be paid more attention to preventing the virus and taking precautions. This research is expected to provide references for the prevention and control of COVID-19 and related infectious diseases and evidence for disease surveillance and response systems to facilitate the appropriate uptake and reuse of geographical data.
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