Abstract
AbstractAnalysis of variance (ANOVA) is a statistical method concerned with comparing the means of several samples, originally developed by Fisher [13]. When a data set is unclear (for example, due to varying numbers of tests or information noise), intuitionistic fuzziness provides the means to describe this imprecision more accurrately, by allowing a degree of truth, falsity and hesitation for a particular statement. In previous publications [29, 30, 32, 33], we have introduced one-way (1-D IFANOVA) and two-way (2-D IFANOVA) intuitionistic fuzzy ANOVA, which are based on classical analysis of variance [12], Intuitionistic Fuzzy Sets (IFSs, see [2]) and Index Matrices (IMs, [3]). The pandemic caused by COVID-19 conquered the world in 2020. 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 for the first time on evaluating the spread of COVID-19 in Europe using 2-D IFANOVA approach [30, 32] using an intuitionistic fuzzy dataset of daily cases provided by European Centre for Disease Prevention and Control [35] for the data for the period from February 1 to December 3, 2020. 2-D IFANOVA will establish the impact of “season” and “geographic location” factors for the proliferation of COVID-19 in Europe. The factor “geographic location” of countries involves differing climate, economic development, population density and other properties. The factor “season” involves differing temperature and humidity of the environment. To facilitate the analysis, we have applied the command-line utility “Test2” [32], which performs two-way IFANOVA, over an IM of pre-prepared IFPs. We will also analyse the data with classical ANOVA (which does show a dependency) and will perform a comparative analysis of the results obtained from that and from IFANOVA.
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