Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China.
Jpn J Infect Dis. 2010 Jul;63(4):264-70.
Bacillary dysentery is still a common and serious public health problem in China. This paper is aimed at developing and evaluating an innovative hybrid model, which combines the seasonal autoregressive integrated moving average (SARIMA) and the generalized regression neural network (GRNN) models, for bacillary dysentery forecasting. Data of monthly bacillary dysentery incidence in Yichang City from 2000-2007 was obtained from Yichang Disease Control and Prevention Center. The SARIMA and SARIMA-GRNN model were developed and validated by dividing the data file into two data sets: data from the past 5 years was used to construct the models, and data from January to June of the 6th year was used to validate them. Simulation and forecasting performance was evaluated and compared between the two models. The hybrid SARIMA-GRNN model was found to outperform the SARIMA model with the lower mean square error, mean absolute error, and mean absolute percentage error in simulation and prediction results. Developing and applying the SARIMA-GRNN hybrid model is an effective decision supportive method for producing reliable forecasts of bacillary dysentery for the study area.
细菌性痢疾在中国仍是一个普遍而严重的公共卫生问题。本文旨在开发和评估一种创新的混合模型，该模型结合了季节性自回归综合移动平均 (SARIMA) 和广义回归神经网络 (GRNN) 模型，用于细菌性痢疾预测。从宜昌市疾病预防控制中心获得了 2000-2007 年宜昌市每月细菌性痢疾发病率的数据。通过将数据文件分为两个数据集来开发和验证 SARIMA 和 SARIMA-GRNN 模型：过去 5 年的数据用于构建模型，第 6 年 1 月至 6 月的数据用于验证模型。对两个模型的模拟和预测性能进行了评估和比较。结果发现，混合 SARIMA-GRNN 模型在模拟和预测结果中的均方误差、平均绝对误差和平均绝对百分比误差均低于 SARIMA 模型，表现更为优异。开发和应用 SARIMA-GRNN 混合模型是为研究区域产生可靠细菌性痢疾预测的有效决策支持方法。
