Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan City, Shanxi Province, China.
Endemic Disease Prevention and Control Section, Shanxi Center for Disease Control and Prevention, Taiyuan City, Shanxi Province, China.
BMC Infect Dis. 2021 Mar 19;21(1):280. doi: 10.1186/s12879-021-05973-4.
Brucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of great significance in controlling brucellosis and taking preventive measures.
Our human brucellosis incidence data were extracted from Shanxi Provincial Center for Disease Control and Prevention. We used seasonal-trend decomposition using Loess (STL) and monthplot to analyse the seasonal characteristics of human brucellosis in Shanxi Province from 2007 to 2017. The autoregressive integrated moving average (ARIMA) model, a combined model of ARIMA and the back propagation neural network (ARIMA-BPNN), and a combined model of ARIMA and the Elman recurrent neural network (ARIMA-ERNN) were established separately to make predictions and identify the best model. Additionally, the mean squared error (MAE), mean absolute error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the performance of the model.
We observed that the time series of human brucellosis in Shanxi Province increased from 2007 to 2014 but decreased from 2015 to 2017. It had obvious seasonal characteristics, with the peak lasting from March to July every year. The best fitting and prediction effect was the ARIMA-ERNN model. Compared with those of the ARIMA model, the MAE, MSE and MAPE of the ARIMA-ERNN model decreased by 18.65, 31.48 and 64.35%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 60.19, 75.30 and 64.35%, respectively. Second, compared with those of ARIMA-BPNN, the MAE, MSE and MAPE of ARIMA-ERNN decreased by 9.60, 15.73 and 11.58%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 31.63, 45.79 and 29.59%, respectively.
The time series of human brucellosis in Shanxi Province from 2007 to 2017 showed obvious seasonal characteristics. The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA-BPNN and ARIMA models. This will provide some theoretical support for the prediction of infectious diseases and will be beneficial to public health decision making.
布鲁氏菌病是一个严重影响发展中国家的重大公共卫生问题，可能给畜牧业造成重大经济损失，对人类健康造成严重危害。合理预测发病率对控制布鲁氏菌病和采取预防措施具有重要意义。
我们从山西省疾病预防控制中心提取了人类布鲁氏菌病发病率数据。我们使用季节性趋势分解使用局部均值（STL）和月图分析 2007 年至 2017 年山西省人类布鲁氏菌病的季节性特征。分别建立了自回归积分移动平均（ARIMA）模型、ARIMA 和反向传播神经网络（ARIMA-BPNN）的组合模型以及 ARIMA 和 Elman 递归神经网络（ARIMA-ERNN）的组合模型，以进行预测并识别最佳模型。此外，使用均方误差（MAE）、平均绝对误差（MSE）和平均绝对百分比误差（MAPE）评估模型的性能。
我们观察到山西省人类布鲁氏菌病的时间序列从 2007 年到 2014 年增加，但从 2015 年到 2017 年减少。它具有明显的季节性特征，每年的高峰期从 3 月持续到 7 月。最佳拟合和预测效果是 ARIMA-ERNN 模型。与 ARIMA 模型相比，ARIMA-ERNN 模型的 MAE、MSE 和 MAPE 分别降低了 18.65%、31.48%和 64.35%；在预测性能方面，MAE、MSE 和 MAPE 分别降低了 60.19%、75.30%和 64.35%。其次，与 ARIMA-BPNN 相比，ARIMA-ERNN 的 MAE、MSE 和 MAPE 分别降低了 9.60%、15.73%和 11.58%；在预测性能方面，MAE、MSE 和 MAPE 分别降低了 31.63%、45.79%和 29.59%。
2007 年至 2017 年山西省人类布鲁氏菌病的时间序列具有明显的季节性特征。ARIMA-ERNN 模型的拟合和预测性能优于 ARIMA-BPNN 和 ARIMA 模型。这将为传染病的预测提供一些理论支持，有利于公共卫生决策。
