Results of assessment of stationarity of the coefficients of the predictors...
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xlsUpdated Jun 13, 2023
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EmailClick to copy linkLink copiedCitePraachi Das; Morganne Igoe; Suzanne Lenhart; Lan Luong; Cristina Lanzas; Alun L. Lloyd; Agricola Odoi (2023). Results of assessment of stationarity of the coefficients of the predictors of the COVID-19 risks in the Greater St. Louis Area, Missouri. [Dataset]. http://doi.org/10.1371/journal.pone.0274899.t004xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0274899.t004Dataset updatedJun 13, 2023AuthorsPraachi Das; Morganne Igoe; Suzanne Lenhart; Lan Luong; Cristina Lanzas; Alun L. Lloyd; Agricola OdoiLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyArea coveredSt. Louis MO-IL, Metropolitan Statistical Area, MissouriDescriptionResults of assessment of stationarity of the coefficients of the predictors of the COVID-19 risks in the Greater St. Louis Area, Missouri.
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Univariable associations between ZCTA-level COVID-19 risk and potential...
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xlsUpdated Jun 16, 2023+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCitePraachi Das; Morganne Igoe; Suzanne Lenhart; Lan Luong; Cristina Lanzas; Alun L. Lloyd; Agricola Odoi (2023). Univariable associations between ZCTA-level COVID-19 risk and potential predictors in the Greater St. Louis Area, Missouri (USA). [Dataset]. http://doi.org/10.1371/journal.pone.0274899.t002xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0274899.t002Dataset updatedJun 16, 2023Dataset provided byPLOS ONEAuthorsPraachi Das; Morganne Igoe; Suzanne Lenhart; Lan Luong; Cristina Lanzas; Alun L. Lloyd; Agricola OdoiLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyArea coveredUnited States, St. Louis MO-IL, Metropolitan Statistical Area, MissouriDescriptionUnivariable associations between ZCTA-level COVID-19 risk and potential predictors in the Greater St. Louis Area, Missouri (USA).
Final global negative binomial model showing significant predictors of...
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- datasetcatalog.nlm.nih.gov
xlsUpdated Jun 13, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCitePraachi Das; Morganne Igoe; Suzanne Lenhart; Lan Luong; Cristina Lanzas; Alun L. Lloyd; Agricola Odoi (2023). Final global negative binomial model showing significant predictors of COVID-19 risk in the Greater St. Louis Area, Missouri (USA). [Dataset]. http://doi.org/10.1371/journal.pone.0274899.t003xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0274899.t003Dataset updatedJun 13, 2023AuthorsPraachi Das; Morganne Igoe; Suzanne Lenhart; Lan Luong; Cristina Lanzas; Alun L. Lloyd; Agricola OdoiLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyArea coveredSt. Louis MO-IL, Metropolitan Statistical Area, Missouri, United StatesDescriptionFinal global negative binomial model showing significant predictors of COVID-19 risk in the Greater St. Louis Area, Missouri (USA).
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Results of assessment of stationarity of the coefficients of the predictors of the COVID-19 risks in the Greater St. Louis Area, Missouri.