Influence and prediction of meteorological factors on brucellosis in a northwest region of China.

Abstract

This paper aims to study the cumulative lag effect of meteorological factors on brucellosis incidence and the prediction performance based on Random Forest model. The monthly number of brucellosis cases and meteorological data from 2015 to 2019 in Yongchang of Gansu Province, northwest China, were used to build distributed lag nonlinear model (DLNM). The number of brucellosis cases of lag 1month and meteorological data from 2015 to 2018 were used to build RF model to predict the brucellosis incidence in 2019. Meanwhile, SARIMA model was established to compare the prediction performance with RF model according to R2 and RMSE. The results indicated that the population had a high incidence risk at temperature between 5 and 13°C and lag between 0 and 18days, sunshine duration between 225 and 260h and lag between 0 and 1month, and atmosphere pressure between 789 and 793.5hPa and lag between 0 and 18days. The R2 and RMSE of train set and test set in RF model were 0.903, 1.609, 0.824, and 2.657, respectively, and the R2 and RMSE in SARIMA model were 0.530 and 7.008. This study found significant nonlinear and lag associations between meteorological factors and brucellosis incidence. The prediction performance of RF model was more accurate and practical compared with SARIMA model.