Linear Regression Analysis of COVID-19 Time-Series Data using the Gumbel Distribution
Received Date: May 30, 2023 / Published Date: Jun 30, 2023
Abstract
This study uses the Gumbel distribution to model and analyze the daily number of COVID-19 deaths in 8 European and North American countries, as well as in the 7 NHS regions of England, during the first wave of the COVID-19 outbreak. Linear regression is used for parameter estimation and data fitting. The analysis focuses on the height and position of the peak as indicators of the efectiveness of the algorithm. The results of the proposed approach show that the Gumbel model reasonably reproduces the time-series data of COVID-19 deaths in many regions. The advantage of the proposed method is its simplicity and straightforwardness, which allow us to obtain preliminary results for an intuitive image of trends without the need for a sophisticated mathematical framework.
Keywords: COVID-19; Extreme value theory; Gumbel distribution; Estimation; Linear regression
Citation: Furutani H, Hiroyasu T (2023) Linear Regression Analysis of COVID-19 Time-Series Data using the Gumbel Distribution. J Infect Dis Ther 11:553 Doi: 10.4172/2332-0877.1000553
Copyright: © 2023 Furutani H, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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