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1000 Titel
  • Contextualizing COVID-19 spread: a county level analysis, urban versus rural, and implications for preparing for the next wave [version 1; peer review: 1 approved]
1000 Autor/in
  1. Rao, J. Sunil |
  2. Zhang, Hang |
  3. Mantero, Alejandro |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-05-21
1000 Erschienen in
1000 Quellenangabe
  • 9:418
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.12688/f1000research.23903.1 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7277009/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • BACKGROUND: Contextual determinants of health including social, environmental, healthcare and others, are a so-called deck of cards one is dealt. The ability to modify health outcomes varies then based upon how one’s hand is played. It is thus of great interest to understand how these determinants associate with the emerging pandemic coronavirus disease 2019 (COVID-19). : METHODS: To this end, we conducted a deep-dive analysis into this problem using a recently curated public dataset on COVID-19 that connects infection spread over time to a rich collection of contextual determinants for all counties of the U.S and Washington, D.C. : RESULTS: Using random forest machine learning methodology, we identified a relevant constellation of contextual factors of disease spread which manifest differently for urban and rural counties. : CONCLUSIONS: The findings also have clear implications for better preparing for the next wave of disease.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal Machine Learning
lokal County Level
lokal Pandemic
lokal Growth Curves
lokal Random Forests
lokal Contextual Factors
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-6450-3200|https://frl.publisso.de/adhoc/uri/WmhhbmcsIEhhbmc=|https://frl.publisso.de/adhoc/uri/TWFudGVybywgQWxlamFuZHJv
1000 Label
1000 Förderer
  1. National Science Foundation |
  2. National Institutes of Health |
1000 Fördernummer
  1. DMS 1915976
  2. U54 MD010722; UL1 TR000460; UL1 TR000460
1000 Förderprogramm
  1. -
  2. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer National Science Foundation |
    1000 Förderprogramm -
    1000 Fördernummer DMS 1915976
  2. 1000 joinedFunding-child
    1000 Förderer National Institutes of Health |
    1000 Förderprogramm -
    1000 Fördernummer U54 MD010722; UL1 TR000460; UL1 TR000460
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6430414.rdf
1000 Erstellt am 2021-11-24T11:22:38.776+0100
1000 Erstellt von 291
1000 beschreibt frl:6430414
1000 Bearbeitet von 317
1000 Zuletzt bearbeitet 2021-11-25T11:57:29.777+0100
1000 Objekt bearb. Thu Nov 25 11:56:59 CET 2021
1000 Vgl. frl:6430414
1000 Oai Id
  1. oai:frl.publisso.de:frl:6430414 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
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