Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study.
Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study.
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DOI：
10.1136/bmjopen-2020-039676
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发表时间：
2020-12-07
期刊：
影响因子：
2.9
通讯作者：
Wu W
中科院分区：
文献类型：
--
作者：
Alim M;Ye GH;Guan P;Huang DS;Zhou BS;Wu W
人间布鲁氏菌病是我国严重危害健康和财产安全的公共卫生问题。预测布病流行趋势和季节性对布病的预防具有重要意义。本研究比较了自回归积分移动平均（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。时间序列研究。中国大陆中国大陆人间布鲁氏菌病数据由中国国家卫生和计划生育委员会提供。将数据分为训练集和测试集。训练集由2008年1月至2018年6月中国大陆人间布鲁氏菌病月发病率组成，测试集由2018年7月至2019年6月发病率组成。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）评价模型拟合和预测效果。中国大陆的人类布鲁氏菌病患者人数从2008年的30 002人增加到2018年的40 328人。在原始时间序列中有增加的趋势和明显的季节性分布。对于训练集，ARIMA（0，1，1）×（0，1，1）12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。对于检验集，ARIMA（0，1，1）×（0，1，1）12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。XGBoost模型的预测效果优于ARIMA模型。XGBoost模型更适合于中国大陆人间布鲁氏菌病病例的预测。
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. Time-series study. Mainland China. 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. 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)12 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)12 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. 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.
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期刊：
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影响因子：
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