Fuzzy Two-Factor Analysis of COVID-19 Cases in Europe

Abstract

In this paper we apply an intuitionistic fuzzy twofactor ANOVA (2-D IFANOVA), based on the concepts of intuitionistic fuzzy sets (IFSs) and index matrices (IMs), over a unique dataset of daily COVID-19 cases up to 24 June 2020 to explore how the number of COVID-19 cases depends on the "density" and "climate zone" factors for the continent of Europe. In the source data, some information may be missing, unclear or imprecise. To deal with the uncertainty in the data, we apply Intuitionistic fuzzy logic. We also present a new software utility, which performs 2-D IFANOVA by using an implementation of Index matrices. Finally, a comparative analysis of the results obtained by the classical ANOVA and IFANOVA is performed.

Interval-Valued Intuitionistic Fuzzy Decision-Making Method using Index Matrices and Application in Outsourcing

“…Any type used with the IM class must provide methods for performing operations on the current object and between two objects, so that they can be substituted in the already prepared IM operation methods. As part of a previous work on intuitionistic fuzzy ANOVA [24], we delevoped a class representing IFPs. Using the work done previously, for this project we are developing a class for IVIF pairs.…”

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

“…IFANOVA algorithm [32] is applied more quickly and easier to the real data of IM Y using a C++ command-line utiity "Test2", which was developed in [34]. The utility uses the IM template class (IndexMatrix T ) as described in [18], which realizes the main IM operations.…”

“…In our previous studies [30,32,35], we have extended classical ANOVA [12] to be able to apply on intuitionistic fuzzy numbers and proposed 1-D IFANOVA and nonreplicated 2-D IFANOVA, based on the apparatuses of IFSs [2] and IMs [3]. Then we have created two command-line utilities "Test1" and "Test2", which perform respectively one-way [33] and two-way IFANOVA [34]. We have used them to investigate the influence of the "population density" and "climate zone" factors on the COVID-19 distribution in Europe [34] up to July 2020.…”

“…Then we have created two command-line utilities "Test1" and "Test2", which perform respectively one-way [33] and two-way IFANOVA [34]. We have used them to investigate the influence of the "population density" and "climate zone" factors on the COVID-19 distribution in Europe [34] up to July 2020. In this paper, we will apply for the first time non-replicated 2-D IFANOVA to explore the influence of "climate zone" and "population density" factors on the intuitionistic fuzzy numbers of COVID-19 cases in Asia, as reported in the IF dataset of daily cases provided by [37] for the period from February 1, 2020 to January 28, 2021.…”

“…In this paper, we will apply for the first time non-replicated 2-D IFANOVA to explore the influence of "climate zone" and "population density" factors on the intuitionistic fuzzy numbers of COVID-19 cases in Asia, as reported in the IF dataset of daily cases provided by [37] for the period from February 1, 2020 to January 28, 2021. We will also interpret the results obtained from two-way IFANOVA using the utility"Test2" [34] and then we will compare them with the results from the classical ANOVA. The main contribution of the paper is in the research of the effectiveness of non-replicated 2-D IFANOVA through its application on IF data on the spread of COVID-19 in Asia to establish the relationships between its spread and the levels of the factors "climate zone" and "population density".…”