基于贝叶斯时空模型的猩红热发病影响因素研究

目的 猩红热(scarlet fever)是我国重点防制的法定乙类传染病之一,严重危害人类健康,其发病数据表现出典型的时空特征,利用时空分析方法研究猩红热发病的影响因素,为疾病防治工作提供帮助.方法 收集2014-2017年我国31个省市猩红热月发病资料及相应的气象因素和空气污染数据,利用Spearman相关分析和逐步...

Influencing factors of scarlet fever based on Bayesian spatio-temporal model

Objective Scarlet fever is one of the statutory category B infectious diseases in China and seriously endangers human health, with typical spatio-temporal characteristics. The spatio-temporal analysis method is used to investigate the influencing factors of scarlet fever incidence so as to provide help for disease prevention and control. Methods Monthly incidence data of scarlet fever and corresponding meteorological factors and air pollution data were collected from 2014 to 2017 in 31 provinces and cities in China, and meteorological factors and air pollution variables affecting the incidence of scarlet fever were screened using Spearman correlation analysis and stepwise regression analysis, and Bayesian spatio-temporal models were applied to the analysis of the influencing factors of scarlet fever incidence. Results CO and NO2 were the factors that had a greater effect on the onset of scarlet fever, with each unit increase bringing a relative risk increase of 5% (95%CI: 3.2%-6.91%) and 1.66% (95%CI: 1.05%-2.22%), respectively. Comparing the application effects of the general Bayesian model without temporal effects, the Bayesian temporal model had obvious advantage, with DIC=9 205.526, MAE=1.06, and RMSE=1.43 for the training set, and DIC=3 060.089, MAE=0.92, and RMSE=1.21 for the test set, all showing excellent fitting and prediction performance. Conclusion Bayesian spatio-temporal models can effectively study the relationship between diseases and related factors, and provide scientific support and theoretical basis for disease prediction and analysis decisions in the field of public health.