| SARIMA-GRNN组合模型在伤寒与副伤寒逐月发病数预测中的应用 |
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| 引用本文： | 李文豪, 曾昱兴, 李笑颜, 彭远舟, 张艳炜, 陈青山, 程锦泉. SARIMA-GRNN组合模型在伤寒与副伤寒逐月发病数预测中的应用[J]. 中华疾病控制杂志, 2021, 25(11): 1341-1346. doi: 10.16462/j.cnki.zhjbkz.2021.11.019 |
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| 作者姓名： | 李文豪 曾昱兴 李笑颜 彭远舟 张艳炜 陈青山 程锦泉 |
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| 作者单位： | 1.510630 广州，暨南大学基础医学与公共卫生学院流行病学教研室;;2.518000 深圳，中山大学公共卫生学院流行病与卫生统计学教研室;;3.518000 深圳，深圳市疾病预防与控制中心免疫规划所 |
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| 基金项目： | 国家“十三五”科技重大专项2018ZX10715004 |
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| 摘 要： | 目的 建立季节性差分自回归移动平均(seasonal autoregressive integrated moving average, SARIMA)-广义回归神经网络(generalized regression neural network, GRNN)组合模型，为伤寒与副伤寒发病数的预测提供方法学上的新思路。方法 利用2011年1月-2019年12月中国伤寒与副伤寒逐月发病数资料，分别构建SARIMA模型和SARIMA-GRNN组合模型，比较两种模型的拟合和预测效果。结果 最优的SARIMA模型为SARIMA (2, 1, 1) (0, 1, 1)12，SARIMA-GRNN组合模型的最优光滑因子(spread)为0.21。评价SARIMA-GRNN组合模型拟合效果的参数均方根误差(root mean squared error, RMSE)、平均绝对误差(mean absolute error, MAE)和平均绝对百分比误差(mean absolute percentage error, MAPE)为90.08、71.44和7.07%，分别小于SARIMA模型的99.44、79.15和7.86%；评价预测效果的RMSE、MAE和MAPE为100.86、75.94和9.57%，均小于SARIMA模型的125.44、97.33和10.89%。结论 SARIMA-GRNN组合模型比传统SARIMA模型更能拟合中国伤寒与副伤寒逐月的发病数，而且预测精度更高，可应用于伤寒与副伤寒逐月发病数的预测。
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| 关 键 词： | 伤寒与副伤寒 季节性差分自回归移动平均模型 广义回归神经网络 组合模型 |
| 收稿时间： | 2020-12-07 |
| 修稿时间： | 2021-05-20 |
Application of SARIMA-GRNN combined model in forecasting the monthly incidence of typhoid fever and paratyphoid fever |
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| LI Wen-hao, ZENG Yu-xing, LI Xiao-yan, PENG Yuan-zhou, ZHANG Yan-wei, CHEN Qing-shan, CHENG Jin-quan. Application of SARIMA-GRNN combined model in forecasting the monthly incidence of typhoid fever and paratyphoid fever[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(11): 1341-1346. doi: 10.16462/j.cnki.zhjbkz.2021.11.019 |
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| Authors: | LI Wen-hao ZENG Yu-xing LI Xiao-yan PENG Yuan-zhou ZHANG Yan-wei CHEN Qing-shan CHENG Jin-quan |
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| Affiliation: | 1. Department of Epidemiology, College of Basic Medicine and Public Health, Jinan University, Guangzhou 510630, China;;2. Department of Epidemiology and Statistics, College of Public Health, Sun Yat-sen University, Shenzhen 518000, China;;3. Institute of Immunization Program, Shenzhen Center for Disease Control and Prevention, Shenzhen 518000, China |
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| Abstract: | Objective This study aimed to establish a seasonal autoregressive integrated moving average (SARIMA)-general regression neural network (GRNN) combined model, so as to provide new methodological ideas for forecasting the incidence of typhoid fever and paratyphoid fever. Methods Using data of typhoid fever and paratyphoid fever from January 2011 to December 2019, the SARIMA model and the SARIMA-GRNN combined model were constructed respectively, and the fitting and forecasting effects of the two models were compared. Results The optimal SARIMA model was SARIMA (2, 1, 1) (0, 1, 1)12 and the optimal smoothing factor of SARIMA-GRNN combined model was 0.21. The root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the SARIMA-GRNN combined model fitting effect were 90.08, 71.44, and 7.07%, which were smaller than the SARIMA model's 99.44, 79.15, and 7.86% respectively. The RMSE, MAE, and MAPE of the forecasting effect were 100.86, 75.94, 9.57%, which were all smaller than 125.44, 97.33, 10.89% of the SARIMA model. Conclusions The SARIMA-GRNN combined model has a better fitting effect and higher forecasting effect than the traditional SARIMA model to forecast the monthly incidence of typhoid fever and paratyphoid fever in China. It can be used to predict the monthly incidence of typhoid fever and paratyphoid fever. |
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| Keywords: | Typhoid fever and paratyphoid fever SARIMA Model GRNN Combined Model |
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