Abstract
Coronavirus disease 2019 (COVID-19) has developed into a global pandemic, affecting every nation and territory in the world. Machine learning-based approaches are useful when trying to understand the complexity behind the spread of the disease and how to contain its spread effectively. The unsupervised learning method could be useful to evaluate the shortcomings of health facilities in areas of increased infection as well as what strategies are necessary to prevent disease spread within or outside of the country. To contribute toward the well-being of society, this paper focusses on the implementation of machine learning techniques for identifying common prevailing public health care facilities and concerns related to COVID-19 as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation. Regression tree, random forest, cluster analysis and principal component machine learning techniques are used to analyze the global COVID-19 data of 133 countries obtained from the Worldometer website as of April 17, 2020. The analysis revealed that there are four major clusters among the countries. Eight countries having the highest cumulative infected cases and deaths, forming the first cluster. Seven countries, United States, Spain, Italy, France, Germany, United Kingdom, and Iran, play a vital role in explaining the 60% variation of the total variations by us of the first component characterized by all variables except for the rate variables. The remaining countries explain only 20% of the variation of the total variation by use of the second component characterized by only rate variables. Most strikingly, the analysis found that the variable number of tests by the country did not play a vital role in the prediction of the cumulative number of confirmed cases.
Highlights
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an infectious disease that first emerged in December 2019 in Wuhan, the capital of China’s Hubei province (Roosa et al, 2020)
The main results are reflected in the graph of the scores in Figure 4, where we show the countries in the axes formed by the first two principal components
We demonstrated how to implement the basic machine learning techniques–principal component, cluster analysis, and regression tree to analyze global COVID-19 data that was extracted from the Worldometer website (Max Roser and Ortiz-Ospina, 2020) as of April 17, 2020
Summary
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an infectious disease that first emerged in December 2019 in Wuhan, the capital of China’s Hubei province (Roosa et al, 2020). We use machine learning approaches to explore whether the global cumulative number of infected people can be predicted using the data provided by Worldometer (Max Roser and Ortiz-Ospina, 2020) as of April 17, 2020. We demonstrate useful approaches when using unsupervised machine learning techniques to explore the nature of propagation in different countries This analysis is expected to bring useful findings, as countries with poor health infrastructure, a lack of smart strategies for testing, and a lack of health care for patients could descend into a rapid spread of disease and later stages of infection. It is important to use unsupervised and supervised methods to classify countries in terms of disease spread and prediction of the global number of cumulative cases of COVID-19. Whether the total variations can be explained with some latent groups which are uncorrelated each other
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