A Regression Analysis on the Covid-19 Transmission

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Gregory L Light
Gregory L Light
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Gregory L. Light
Gregory L. Light

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A Regression Analysis on the Covid-19 Transmission

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Abstract

This note applies least-squares regression to a cross-section comparison of the total infection numbers of COVID-19 as of a particular date among the fifty states of America to investigate any underlying factors; we also check the Gaussian normality of the time progression of the infection rate.

References

7 Cites in Article
  1. R Kubiak,N Arinaminpathy,A R Mclean (2010). Insights into the evolution and emergence of a novel infectious disease.
  2. Alicia Kraay,James Trostle,Andrew Brouwer,William Cevallos,Joseph Eisenberg (2018). Determinants of Short-term Movement in a Developing Region and Implications for Disease Transmission.
  3. Sifat Sharmin,Md. Rayhan (2012). Spatio-temporal modeling of infectious disease dynamics.
  4. R Romanescu,R Deardon (2017). Fast inference for network models of infectious disease spread.
  5. David Welch (2011). Is Network Clustering Detectable in Transmission Trees?.
  6. N Shao (2020). Dynamic models for Coronavirus Disease 2019 and data analysis.
  7. Nicholas Grassly,Christophe Fraser (2008). Mathematical models of infectious disease transmission.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Gregory L Light. 2020. \u201cA Regression Analysis on the Covid-19 Transmission\u201d. Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 20 (GJSFR Volume 20 Issue F3): .

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

Keywords
Classification
GJSFR-F Classification: MSC 2010: 62M10
Version of record

v1.2

Issue date

May 5, 2020

Language
en
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This note applies least-squares regression to a cross-section comparison of the total infection numbers of COVID-19 as of a particular date among the fifty states of America to investigate any underlying factors; we also check the Gaussian normality of the time progression of the infection rate.

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A Regression Analysis on the Covid-19 Transmission

Gregory L. Light
Gregory L. Light

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