Regression Polynomial Analysis of the COVID 19 Epidemics: Progress Report 1
30 Pages Posted: 2 Jul 2020
Date Written: June 27, 2020
COVID19 pandemic generates large data bases about daily new cases, deaths, recoveries, etc. that are freely available at Internet. Modeling the dynamics of this pandemic is a challenging but necessary task to help establishing adequate safety measures about social distance and mobility; environment cleaning and disinfection; health services planning and organization; etc. There exist a plethora of techniques and tools for data basis mining analysis.
Here, we focus in a very simple of these techniques named Polynomial Regression Analysis, in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear. For this reason, polynomial regression is considered to be a special case of multiple linear regression analysis. It is a very simple analysis that is easily available to anyone, as it is included in popular software like Excel. This is progress report of the application of this type of analysis to follow up COVID19 epidemics in 20 different countries from the site corona.help/.
The results from this simple analysis seem to show that epidemics have a similar and predictable pattern for all different countries but also revealed COVID19 epidemics has special signatures for each studied country. These findings indicate that new theoretical models are in need for the understanding of the COVID19 epidemics.
Note: Funding: None to declare
Declaration of Interest: None to declare
Keywords: COVID19; epidemics; Polynomial Regression Analysis; Lockdown; Panic Crisis
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