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Regression Study on Influencing Factors of COVID-19 Diagnosis Rate and Mortality: A Global Perspective

The novel coronavirus pneumonia (COVID-19) refers to the pulmonary infection caused by the novel coronavirus (2019-nCoV), which has become an urgent public health event of global concern at present. In order to help local governments to find out the factors that curb the spread of COVID-19, we explored the influence factors that cause COVID-19 infection and death in the fields of economy, society, life, and health in this paper. Through correlation analysis, we found that COVID-19 transmission and mortality are relatively strongly associated with human development index (HDI), Median Age, human life expectancy, proportion of smokers, and GDP per capita. Further regression analysis and machine learning regression algorithms also confirmed that HDI, proportion of smokers, GDP per capita, and Median Age have significant effects on COVID-19 transmission and mortality, with GBDT performing best with R² of 0.585 and 0.415 per million confirmed cases and deaths, respectively. This study aims to explore the impact of relevant factors on COVID-19 in the international community, inform the development of measures to reduce diagnosis and mortality rates in countries, and improve the capacity to respond to such public health emergencies.