This preprint is under consideration at Environmental Science and Pollution Research. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research Article
Jinting Zhang
Wuhan University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3037-7645
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Since COVID-19 is extremely menacing human’s health, it is a significant to expose on its fator’s impacts for curbing the virus spreading. To tackle the complexity of COVID-19 expansion in spatial-temporal scale, This research is approriatedly analyzed the spatial-temporal heterogeneity at county-level in Texas. First,factors impacts 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 is used COVID-19 CC as the dependent variable and the common factors as the independent variable. According to the virus prevailing hierarchy, spatial-temporal disparity is are categorized four quarters in the modeling GWR analysis according to the virus prevailing hierarchy. The findings are exibited that GWR models provided higher fitness, more geodata-oriented information than OLS models. In Texas El Paso, Odessa, Midland, Randall and Potter County areas, population, hospitalization, and age structure presented static, positive influences on COVID-19 cumulative casesm, indicating they should be adopt stringent stratgies in curbing COVID-19. Winter is the most sensitive season for the virus spreading, implying the last quarter should be pay more attention to prevent the virus and take pracutions. This research are expected to provide references for preventing and controlling COVID-19 and related infectious dieseaces, evidences for disease surveillance and response systems to facilitate the appropriate uptake and reuse of geographical data.
Keywords
Geographical Weighted Regression (GWR), Temporal-spatially varying impacts, COVID-19 Cumulative Case
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Posted 04 Mar, 2021
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This preprint is under consideration at Environmental Science and Pollution Research. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Jinting Zhang
Wuhan University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3037-7645
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 04 Mar, 2021
No community comments so far
Review #? received
Received 24 Feb, 2021
Reviewers invited
Invitations sent on 24 Feb, 2021
Editor invited
On 24 Feb, 2021
First submitted
On 21 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Since COVID-19 is extremely menacing human’s health, it is a significant to expose on its fator’s impacts for curbing the virus spreading. To tackle the complexity of COVID-19 expansion in spatial-temporal scale, This research is approriatedly analyzed the spatial-temporal heterogeneity at county-level in Texas. First,factors impacts 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 is used COVID-19 CC as the dependent variable and the common factors as the independent variable. According to the virus prevailing hierarchy, spatial-temporal disparity is are categorized four quarters in the modeling GWR analysis according to the virus prevailing hierarchy. The findings are exibited that GWR models provided higher fitness, more geodata-oriented information than OLS models. In Texas El Paso, Odessa, Midland, Randall and Potter County areas, population, hospitalization, and age structure presented static, positive influences on COVID-19 cumulative casesm, indicating they should be adopt stringent stratgies in curbing COVID-19. Winter is the most sensitive season for the virus spreading, implying the last quarter should be pay more attention to prevent the virus and take pracutions. This research are expected to provide references for preventing and controlling COVID-19 and related infectious dieseaces, evidences for disease surveillance and response systems to facilitate the appropriate uptake and reuse of geographical data.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
This preprint is available for download as a PDF.
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