Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries

Abstract

The numbers of confirmed cases of new coronavirus (Covid-19) are increased daily in different countries. To determine the policies and plans, the study of the relations between the distributions of the spread of this virus in other countries is critical. In this work, the distributions of the spread of Covid-19 in Unites States America, Spain, Italy, Germany, United Kingdom, France, and Iran were compared and clustered using fuzzy clustering technique. At first, the time series of Covid-19 datasets in selected countries were considered. Then, the relation between spread of Covid-19 and population's size was studied using Pearson correlation. The effect of the population's size was eliminated by rescaling the Covid-19 datasets based on the population's size of USA. Finally, the rescaled Covid-19 datasets of the countries were clustered using fuzzy clustering. The results of Pearson correlation indicated that there were positive and significant between total confirmed cases, total dead cases and population's size of the countries. The clustering results indicated that the distribution of spreading in Spain and Italy was approximately similar and differed from other countries.

A new mathematical model of COVID-19 using real data from Pakistan

“…In this research, it was shown that with a regulated transmission rate, the methods used by the public health sector and the government to address the situation leading to an increase in the pandemic will be more effective. Following the strategy employed in recently published researches [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , the proposed COVID-19 model will be investigated in more detail in future research work.…”

Clustering of European countries and territories based on cumulative relative number of COVID 19 patients in 2020

“…The proposed method demonstrates a close similarity in the evolution of cases and deaths. In [11] the correlation between spread of Covid-19 and population's size is studied, after that the fuzzy clustering is applied on countries with high spread risk.…”

Modeling the dynamic of COVID-19 with different types of transmissions

“…In [41] , the authors presented a mathematical model qualitatively using stability theory of differential equations and the basic reproduction number that represents an epidemic indicator obtained from the largest eigenvalue of the so-called next-generation matrix to find the likely outcome of an outbreak that is beneficial for public health initiatives. Other mathematical models with ordinary time derivatives can be found in literature [4] , [14] , [30] , [31] , [32] , [41] .…”