背景:人类布鲁氏菌病在中国是一个严重的公共卫生问题。这项研究的目的是建立一个合适的模型来预测中国大陆的人类布鲁氏菌病病例。
方法:中国大陆31个省市2012年1月至2021年12月人类布鲁氏菌病月度病例数据来源于国家卫生健康委员会网站。构建了TBATS和ELM模型。MAE,MSE,地图,和RMSE进行了计算,以评估两个模型的预测性能。
结果:最优TBATS模型为TBATS(1,{0,0},-{<12,4>}),最低AIC值为1854.703。在最优TBATS模型中,{0,0}表示ARIMA(0,0)模型,{<12,4>}是季节周期的参数和相应的傅立叶项数,分别,并且Box-Cox变换的参数ω为1。最佳ELM模型隐藏层数为33,R平方值为0.89。ELM模型提供了较低的MAE值,MSE,地图,和RMSE用于拟合和预测性能。
结论:结果表明,在2012年1月至2021年12月期间,ELM模型的预测性能优于TBATS模型。ELM模型的预测有助于为中国大陆地区的人类布鲁氏菌病提供早期预警和更有效的防控措施。
BACKGROUND: Human brucellosis is a serious public health concern in China. The objective of this
study is to develop a suitable model for forecasting human brucellosis cases in mainland China.
METHODS: Data on monthly human brucellosis cases from January 2012 to December 2021 in 31 provinces and municipalities in mainland China were obtained from the National Health Commission of the People\'s Republic of China website. The TBATS and ELM models were constructed. The MAE, MSE, MAPE, and RMSE were calculated to evaluate the prediction performance of the two models.
RESULTS: The optimal TBATS model was TBATS (1, {0,0}, -, {< 12,4 >}) and the lowest AIC value was 1854.703. In the optimal TBATS model, {0,0} represents the ARIMA (0,0) model, {< 12,4 >} are the parameters of the seasonal periods and the corresponding number of Fourier terms, respectively, and the parameters of the Box-Cox transformation ω are 1. The optimal ELM model hidden layer number was 33 and the R-squared value was 0.89. The ELM model provided lower values of MAE, MSE, MAPE, and RMSE for both the fitting and forecasting performance.
CONCLUSIONS: The results suggest that the forecasting performance of ELM model outperforms the TBATS model in predicting human brucellosis between January 2012 and December 2021 in mainland China. Forecasts of the ELM model can help provide early warnings and more effective prevention and control measures for human brucellosis in mainland China.