Sub-national level analysis of 2015 earthquakes injury rates and determinants in Nepal: applications of global and local regression models

Injuries are the direct and immediate impact of any extreme major event particularly earthquakes, which produce higher injuries than other disasters. How to reduce and avoid earthquake injuries has remained a major concern for disaster managers and policy-makers. With this motivation, this study examines the injury rates at the district level and identifies the major factors responsible for the rates caused by Nepal’s two major earthquakes in 2015. Using secondary data, the injury rates were analyzed by the application of One Way Analysis of Variance and by identifying the factors of earthquake injury through two regression models—ordinary least squares (OLS) regression and geographically weighted regression (GWR). In both global (OLS) and local (GWR) models, we included seven independent variables, and the OLS regression identified three statistically significant determinants of 2015 earthquake injury rates in Nepal. Although the global model and local model coefficients are harmonious over Nepal, our results show that the GWR model performs better than the OLS model. Based on the major findings of this study, we conclude that improving the built environment in Nepal by enforcing strict building codes can substantially reduce injuries and collapse of buildings due to earthquakes in the future.