论文标题
Covid-19在欧洲地区引起的总体死亡率与人口组成高度相关：一种基于空间回归的方法
The overall mortality caused by COVID-19 in the European region is highly associated with demographic composition: A spatial regression-based approach
论文作者
论文摘要
人口统计学因素对COVID-19引起的整体伤亡具有重大影响。在这项研究中，使用空间回归模型分析了关键的人口统计学变量与共同19例和死亡情况之间的空间关联。总共13（对于19例病例因子）和8（对于19号死亡因子）为建模考虑了关键变量。对于模型估计值的空间建模和映射，执行了五个空间回归模型，例如地理加权回归（GWR），空间误差模型（SEM），空间滞后模型（SLM），空间error_lag模型（SEM_SLM）和普通最小二平方（OLS）。在意大利和英国，当地的R2值表明所选人口统计学变量对COVID-19引起的整体伤亡的影响。在法国，比利时，荷兰，爱尔兰，丹麦，挪威，瑞典，波兰，斯洛伐克和罗马尼亚观察到适度的本地R2。 COVID-19病例的最低局部R2值是拉脱维亚和立陶宛的。在13个变量中，计算了总人口最高的本地R2（R2 = 0.92），其次是死亡的死亡率（R2 = 0.9），长期疾病（R2 = 0.84），年龄> 80（R2 = 0.59）的人口（R2 = 0.46），就业（R2 = 0.46），预期率为65（R2 = 0.34），寿命（R2 = 0.34），R2 = 0.31，R2 = 31，R2 = 31，R2 = 31，R2 = 31，R2 = 0.31。 0.31），年龄为65-80岁的人口（R2 = 0.29），人口为15-24岁（R2 = 0.27），人口为25-49岁（R2 = 0.27），分别为0-14岁的人口（R2 = 0.23）和人口分别为50-65岁（R2 = 0.23）。
The demographic factors have a substantial impact on the overall casualties caused by the COVID-19. In this study, the spatial association between the key demographic variables and COVID-19 cases and deaths were analyzed using the spatial regression models. Total 13 (for COVID-19 case factor) and 8 (for COVID-19 death factor) key variables were considered for the modelling. Total five spatial regression models such as Geographically weighted regression (GWR), Spatial Error Model (SEM), Spatial Lag Model (SLM), Spatial Error_Lag model (SEM_SLM), and Ordinary Least Square (OLS) were performed for the spatial modelling and mapping of model estimates. The local R2 values, which suggesting the influences of the selected demographic variables on overall casualties caused by COVID-19, was found highest in Italy and the UK. The moderate local R2 was observed for France, Belgium, Netherlands, Ireland, Denmark, Norway, Sweden, Poland, Slovakia, and Romania. The lowest local R2 value for COVID-19 cases was accounted for Latvia and Lithuania. Among the 13 variables, the highest local R2 was calculated for total population (R2 = 0.92), followed by death crude death rate (R2 = 0.9), long time illness (R2 = 0.84), population with age >80 (R2 = 0.59), employment (R2 = 0.46), life expectancy at 65 (R2 = 0.34), crude birth rate (R2 = 0.31), life expectancy (R2 = 0.31), Population with age 65-80 (R2 = 0.29), Population with age 15-24 (R2 = 0.27), Population with age 25-49 (R2 = 0.27), Population with age 0-14 (R2 = 0.23), and Population with age 50-65 (R2 = 0.23), respectively.
