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1000 Titel
  • Investigating the cases of novel coronavirus disease (COVID-19) in China using dynamic statistical techniques
1000 Autor/in
  1. Sarkodie, Samuel Asumadu |
  2. Owusu, Phebe Asantewaa |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-04-04
1000 Erschienen in
1000 Quellenangabe
  • In Press
1000 Copyrightjahr
  • 2020
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1000 Verlagsversion
  • https://doi.org/10.1016/j.heliyon.2020.e03747 |
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1000 Begutachtungsstatus
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1000 Abstract/Summary
  • The initial investigation by local hospital attributed the outbreak of the novel coronavirus disease (COVID-19) to pneumonia unknown cause that appeared like the severe acute respiratory syndrome (SARS) that occurred in 2003. The World Health Organization has declared COVID-19 as public health emergency after it spread outside China to numerous countries. Thus, an assessment of the novel coronavirus disease (COVID-19) with novel approaches is essential to the global debate. This study is the first to develop both time series and panel data models to construct conceptual tools that examine the nexus between death from COVID-19 and confirmed cases. We collected daily data on four health indicators namely deaths, confirmed cases, suspected cases, and recovered cases across 31 Provinces/States in China. Due to the complexities of the COVID-19, we investigated the unobserved factors including environmental exposures accounting for the disease spread through human-to-human transmission. We used estimation methods capable of controlling for cross-sectional dependence, endogeneity, and unobserved heterogeneity. We predict the impulse-response between confirmed cases of COVID-19 and COVID-19-attributable deaths. Our study reveals that the effect of confirmed cases on the novel coronavirus attributable deaths is heterogeneous across Provinces/States in China. We find a linear relationship between COVID-19 attributable deaths and confirmed cases whereas a nonlinear relationship is confirmed for the nexus between recovery cases and confirmed cases. The empirical evidence reveals that an increase in confirmed cases by 1% increases coronavirus attributable deaths by ∼0.10%–∼1.71% (95% CI). Our empirical results confirm the presence of unobserved heterogeneity and common factors that facilitates the novel coronavirus attributable deaths caused by increased levels of confirmed cases. Yet, the role of such a medium that facilitates the transmission of COVID-19 remains unclear. We highlight safety precaution and preventive measures to circumvent the human-to-human transmission.
1000 Sacherschließung
lokal Economics
gnd 1206347392 COVID-19
lokal Public health
lokal Environmental science
lokal Novel coronavirus disease
lokal Cases of novel coronavirus
lokal Health economics
lokal China
lokal Environmental economics
lokal Econometrics
lokal Modelling COVID-19
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/U2Fya29kaWUsIFNhbXVlbCBBc3VtYWR1|https://frl.publisso.de/adhoc/uri/T3d1c3UsIFBoZWJlIEFzYW50ZXdhYQ==
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1000 @id frl:6419809.rdf
1000 Erstellt am 2020-04-07T11:46:10.235+0200
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1000 Zuletzt bearbeitet 2020-04-07T11:47:30.661+0200
1000 Objekt bearb. Tue Apr 07 11:47:02 CEST 2020
1000 Vgl. frl:6419809
1000 Oai Id
  1. oai:frl.publisso.de:frl:6419809 |
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