South Asian Countries Are Less Fatal Concerning COVID-19: A Hybrid Approach Using Machine Learning and M-AHP

The outbreak of pneumonia in December 2019 in Wuhan, China, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly throughout the world. With over 4.62 million confirmed cases of COVID-19 and 311,000 deaths in more than 188 countries, this ongoing pandemic has wreaked havoc all around the globe. However, the SAARC (South Asian Association for Regional Cooperation) countries, compared to the First World nations, have significantly low death rate. In this paper, the authors have determined this uneven distribution of COVID-19 deaths with the help of some possible factors, which are the prime cause of such variability among the different nations. This paper presents the significance of these factors through analysis of the data corresponding to each of these factors from 165 different countries. On the basis of the relationship between the factors and their significance on the concerned countries’ death toll, we have labelled each factor’s risk index using the multiple analytical hierarchy process (M-AHP), as it provides several experts’ views instead of a single expert’s opinion. The risk index of all the factors has been used to generate the susceptibility of COVID-19 for each of the countries in study, specifically the SAARC nations. Finally, we have applied a hierarchical clustering-based machine learning approach to visualize the countries’ death toll corresponding to their susceptibility index. This paper’s major findings are that authors holistically searched the root causes of why South Asian countries are less fatal concerning COVID-19.