The COVID-19 outbreak has become a global pandemic. The spatial variation in the environmental, health, socioeconomic, and demographic risk factors of COVID-19 death rate is not well understood. Global models and local linear models were used to estimate the impact of risk factors of the COVID-19, but these do not account for the nonlinear relationships between the risk factors and the COVID-19 death rate at various geographical locations. We proposed a local nonlinear nonparametric regression model named geographically weighted random forest (GW-RF) to estimate the nonlinear relationship between COVID-19 death rate and 47 risk factors derived from the US Environmental Protection Agency, National Center for Environmental Information, Centers for Disease Control and the US census. The COVID-19 data were employed to a global regression model random forest (RF) and a local model GW-RF. The adjusted R 2 of the RF is 0.69. The adjusted R 2 of the proposed GW-RF is 0.78. The result of GW-RF showed that the risk factors (i.e. going to work by walking, airborne benzene concentration, householder with a mortgage, unemployment, airborne PM 2.5 concentration and per cent of the black or African American) have a high correlation with the spatial distribution of the COVID-19 death rate, and these key factors driven from the GW-RF were mapped, which could provide useful implications for controlling the spread of the COVID-19 pandemic.

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