Model’s forecast results for 2021.

BackgroundWhile time-series models have been applied to forecast brucellosis incidence in China, systematic comparisons of multiple models remain relatively limited. This study aimed to elucidate the epidemic characteristics of human brucellosis and to provide a comparative assessment of several time-series prediction models, in order to identify a suitable predictive framework for future incidence forecasting.MethodsMonthly and annual incidence rates (per 100,000 population) of brucellosis in China from January 2011 to December 2020 were used as raw data. Seven time-series models were developed and compared using R software (version 4.3.1): Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters additive model, Holt-Winters multiplicative model, Neural Network Autoregressive (NNAR) model, Exponential Smoothing State Space (ETS) model, TBATS model, and Prophet model. A rolling-window cross-validation was applied to assess model stability. Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE).ResultsAmong the seven models evaluated, the Holt-Winters multiplicative model demonstrated the most stable and superior predictive performance on the test set (MAE = 0.034, RMSE = 0.040, MAPE = 14.881%, MASE = 0.891), which serves as strong evidence for its best generalization capability among the compared models.ConclusionsGiven its stable and superior performance in the test set, the Holt-Winters multiplicative model is recommended for short-term brucellosis forecasting in China. It captures the characteristic spring-summer peak, and its integration into surveillance systems could enhance early warning and targeted interventions.

研究背景：尽管时序模型已被应用于中国布鲁氏菌病（brucellosis）发病率的预测研究，但针对多种模型的系统性对比分析仍相对匮乏。本研究旨在阐明人类布鲁氏菌病的流行特征，并对多款时序预测模型开展对比评估，以期筛选出适用于未来发病率预测的最优预测框架。研究方法：本研究以2011年1月至2020年12月中国布鲁氏菌病的月度及年度发病率（每10万人口计）作为原始数据集。采用R软件（版本4.3.1）构建并对比了7种时序预测模型：季节性自回归积分滑动平均（Seasonal Autoregressive Integrated Moving Average, SARIMA）模型、霍尔特-温特斯加法模型（Holt-Winters additive model）、霍尔特-温特斯乘法模型（Holt-Winters multiplicative model）、神经网络自回归（Neural Network Autoregressive, NNAR）模型、指数平滑状态空间（Exponential Smoothing State Space, ETS）模型、TBATS模型以及Prophet模型。采用滚动窗口交叉验证法评估模型稳定性，并通过均方根误差（root mean square error, RMSE）、平均绝对误差（mean absolute error, MAE）、平均绝对百分比误差（mean absolute percentage error, MAPE）以及平均绝对标度误差（mean absolute scaled error, MASE）量化评估模型性能。研究结果：在7种被评估的模型中，霍尔特-温特斯乘法模型在测试集上展现出最稳定且最优的预测性能（MAE=0.034、RMSE=0.040、MAPE=14.881%、MASE=0.891），这充分证明其在所有对比模型中具备最佳泛化能力。研究结论：鉴于霍尔特-温特斯乘法模型在测试集上表现稳定且优异，本研究推荐将其应用于中国布鲁氏菌病的短期预测。该模型能够捕捉到布鲁氏菌病特有的春夏发病高峰，将其集成到疾病监测系统中，可强化早期预警与针对性干预措施的实施效果。