Machine Learning Approaches Reveal That the Number of Tests Do Not Matter to the Prediction of Global Confirmed COVID-19 Cases - Khan, Md Hasinur Rahaman; Hossain, Ahmed (Frontiers in Artificial Intelligence, 2020)
Coronavirus disease 2019 (COVID‑19) is a global pandemic and it appears as pandemic for each nation and territory in the world. Machine learning-based approaches are useful to understand the complexity behind the spread of the disease and to contain its spread of such outbreaks effectively. The unsupervised learning could be useful to learn about the shortcomings of health facilities in the groups where the infection is more and what necessary strategies can be used to prevent its spread within or outside the country. To contribute towards the well-being of living society, this paper focusses on implementing 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 analyse the global COVID-19 data of 133 countries obtained from the Worldometer website as on 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- USA, Spain, Italy, France, Germany, UK, and Iran played vital role for explaining the 60% variation of the total variations by the first component characterized by all variables except the rate variables. Remaining countries contribute to explaining only 20% variation of the total variations by 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 any vital role to predict cumulative number of confirmed cases.