Intuitionistic Fuzzy ANOVA for COVID-19 Cases in Asia by Density and Climate Factors

Abstract

AbstractAnalysis of variance (ANOVA), proposed by Fisher, is a statistical tool to determine the influence of factors on a data set. When a data set occurs in an uncertain environment, intuitionistic fuzzy logic is a means of dealing with this imprecision. Compared to fuzzy data, intuitionistic fuzzy (IF) data also has a hesitation degree. In our previous publications in 2020, we have proposed for the first time one-way (1-D IFANOVA) and nonreplicated two-way (2-D IFANOVA) intuitionistic fuzzy ANOVA, combining classical variational analysis with possibilities for modeling of Index Matrices (IMs) and Intuitionistic Fuzzy Sets (IFSs).The pandemic caused by Coronavirus disease 2019 (COVID-19) first occured in China at the end of 2019. The recent events related to the COVID-19 pandemic have posed many questions regarding the disease’s spread rate, including whether various factors may or may not have an influence upon it. The present work focuses on evaluating the rate of COVID-19 in Asia by applying 2-D IFANOVA on the IF dataset of daily cases for the period from February 1, 2020 to January 28, 2021. A command-line utility “Test2”, which performs 2-D IFANOVA, will explore the impact of “density” and “climate zones” factors on the spread of COVID-19 in Asia. We will also compare the results obtained from traditional ANOVA over the same data set and from 2-D IFANOVA.KeywordsCOVID-19IFANOVAIndex matrixIntuitionistic fuzzy setsVariation analysis