Analysis of COVID-19 Mortality in Japan byUsing Support Vector Machine

To look for factors of the COVID-19 spreading in the whole world currently, an empirical study has been tried by using a multi-regression analysis for mortality rates of 47 prefectures as an objective variable, and various indices as the explanatory variables. A support vector machine method was applied to deal with a nonlinear relationship between objective and explanatory variables, and a sensitivity analysis was applied to search the factors of the COVID-19 mortality. Welfare, urbanization, poverty rate, service industry, and sex ratio were obtained as dangerous factors which increase mortality, while single-person households, meals, and sleep were obtained as defensing factors which decrease mortality. Novel and useful knowledge for prevention measure of the COVID-19 was obtained: three factors of urbanization, service industry, and single-person household relating to the Three Cs contribute largest to the mortality, and two factors of welfare and poverty rate, reflecting the reality' of the poor people also contribute.