Derivative, regression and time series analysis in sars-cov-2

Abstract

The Covid-19 pandemic and the need of confinement have had a very significant impact on people’s lives all over the world. Everybody is looking at curves and how those curves evolve to try to understand how their country has been and will be affected in the near future. Thanks to open data and data science tools, we can analyze the evolution of key factors. Derivatives, polynomial regression and time series analysis can be used to capture trends. In this paper we explore and evaluate the use of such techniques, concluding regarding their merits and limitations for the Covid-19 data series. We conclude that polynomial regression on derivative totals, with degree 2 or 3 achieved the lowest average errors (median 5.5 to 6%) over 20 countries, while PROPHET and ARIMA may excel in larger series.