Modelling emergency disaster mortality around the world: a network-based distributional inference approach
利用最优传输理论和网络分析，提出一种基于气候、社会、经济和人口变量的分布推断框架，在数据不完整时重建自然灾害和技术灾害的死亡率风险分布，并基于基尼风险价值指标揭示各国灾害风险预防策略与死亡率风险的关系及不平等。
Abstract In this paper, we leverage Optimal Transport theory and network analysis to propose a novel distributional framework for the inference of mortality risks arising from worldwide emergency disasters. We propose a propagation method grounded on Wasserstein barycentre and similarity networks, based on climate, social, economic, and demographic variables, able to model mortality risk distributions, when country-level information is incomplete or unavailable. We apply our proposal to the International Emergency Events Database showing that the method is able to coherently reconstruct the mortality risk distributions related to natural and technological disasters via the information embedded in country similarity networks. We provide a comprehensive indicator of countries potential extreme losses from various types of disasters through the Gini-based Value-at-Risk (Gini-VaR). This measure provides country-specific and disaster-specific probabilistic impacts of extremely severe hazards. We also shed light on the relationship between disaster risk preventive strategies put in place by nations and their mortality risk profiles based on the Gini-VaR, highlighting major inequalities across countries.