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  • Name: Investigating the cases of novel coronavirus disease (COVID-19) Using dynamic statistical techniques
  • Type: PDF and MS Word (DOC)
  • Size: [666 KB]
  • Length: [50] Pages
  • See abstract below

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Download the complete project research material from chapters 1-5 titled; Investigating the cases of novel coronavirus disease (COVID-19) Using dynamic statistical techniques. See below for the abstract. Scroll down to click the DOWNLOAD NOW button to get the complete project work immediately.

Investigating the cases of novel coronavirus disease (COVID-19) Using dynamic statistical techniques

The Material File Details

  • Name: Investigating the cases of novel coronavirus disease (COVID-19) Using dynamic statistical techniques
  • Type: PDF and MS Word (DOC)
  • Size: [666 KB]
  • Length: [50] Pages

Abstract

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.

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