中国内地人群布鲁氏菌病预测的 ARIMA 模型和 XGBoost 模型比较：时间序列研究。
Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study.
机构信息
Department of Epidemiology, China Medical University, Shenyang, China.
Department of Mathematics, China Medical University, Shenyang, China.
出版信息
BMJ Open. 2020 Dec 7;10(12):e039676. doi: 10.1136/bmjopen-2020-039676.
OBJECTIVES
Human brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In this study, a comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more suitable for predicting the occurrence of brucellosis in mainland China.
DESIGN
Time-series study.
SETTING
Mainland China.
METHODS
Data on human brucellosis in mainland China were provided by the National Health and Family Planning Commission of China. The data were divided into a training set and a test set. The training set was composed of the monthly incidence of human brucellosis in mainland China from January 2008 to June 2018, and the test set was composed of the monthly incidence from July 2018 to June 2019. The mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the effects of model fitting and prediction.
RESULTS
The number of human brucellosis patients in mainland China increased from 30 002 in 2008 to 40 328 in 2018. There was an increasing trend and obvious seasonal distribution in the original time series. For the training set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1) model were 338.867, 450.223 and 10.323, respectively, and the MAE, RSME and MAPE of the XGBoost model were 189.332, 262.458 and 4.475, respectively. For the test set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1) model were 529.406, 586.059 and 17.676, respectively, and the MAE, RSME and MAPE of the XGBoost model were 249.307, 280.645 and 7.643, respectively.
CONCLUSIONS
The performance of the XGBoost model was better than that of the ARIMA model. The XGBoost model is more suitable for prediction cases of human brucellosis in mainland China.
目的
人类布鲁氏菌病是危害中国健康和财产的公共卫生问题。预测人类布鲁氏菌病的趋势和季节性对于预防该病具有重要意义。本研究比较了自回归综合移动平均（ARIMA）模型和极端梯度提升（XGBoost）模型，以确定哪种模型更适合预测中国大陆布鲁氏菌病的发生。
设计
时间序列研究。
地点
中国大陆。
方法
提供了中国国家卫生健康委员会提供的中国大陆人类布鲁氏菌病数据。数据分为训练集和测试集。训练集由中国大陆 2008 年 1 月至 2018 年 6 月的人类布鲁氏菌病每月发病率组成，测试集由 2018 年 7 月至 2019 年 6 月的人类布鲁氏菌病每月发病率组成。使用平均绝对误差（MAE）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）来评估模型拟合和预测效果。
结果
中国大陆布鲁氏菌病患者人数从 2008 年的 30002 例增加到 2018 年的 40328 例。原始时间序列呈上升趋势，且具有明显的季节性分布。对于训练集，ARIMA（0,1,1）×（0,1,1）模型的 MAE、RSME 和 MAPE 分别为 338.867、450.223 和 10.323，XGBoost 模型的 MAE、RSME 和 MAPE 分别为 189.332、262.458 和 4.475。对于测试集，ARIMA（0,1,1）×（0,1,1）模型的 MAE、RSME 和 MAPE 分别为 529.406、586.059 和 17.676，XGBoost 模型的 MAE、RSME 和 MAPE 分别为 249.307、280.645 和 7.643。
结论
XGBoost 模型的性能优于 ARIMA 模型。XGBoost 模型更适合预测中国大陆人类布鲁氏菌病病例。
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