Upgrading the Human Development Index (HDI) to control pandemic mortality rates: A data mining approach to COVID-19

In recent years, the occurrence of various pandemics (COVID-19, SARS, etc.) and their widespread impacts on human life have led researchers to focus on their pathology and epidemiology components. One of the most significant inconveniences of these epidemics is the human mortality rate, which has had highly social adverse effects. This work, in addition to the major attributes that affect the COVID-19 mortality rate (health factors, people-health status, and climate) and the social and economic components of the social studies. These components have been extracted from the countries’ human development index (HDI), and the effect of the level of social development on the mortality rate has been investigated using ensemble data mining methods. The results obtained indicate that the level of community education has the highest effect on the disease mortality rate. In a way, the extent of its effect is much higher than the environmental factors such as the air temperature and regional health factors, and the community welfare. The impact of this factor is probably due to the ability of knowledge-based societies on managing the crises , their attention to health advisories , a lower involvement of rumors , and consequently, a lower incidence of mental health problems . This work shows the impact of education on reducing the severity of the crisis in the communities and opens a new window in terms of the cultural and social factors in the interpretation of the medical data. Furthermore, according to the results and comparing different types of single and ensemble data mining methods, the application of ensemble data mining methods VOTE, etc.) in terms of classification accuracy and prediction error has the best result.