Wang Yongbin, Liang Yifang, Xue Chenlu, Zhang Bingjie, Zhou Peiping, Li Yanyan, Li Xinxiao, Xu Chunjie
Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China.
Beijing Key Laboratory of Antimicrobial Agents/Laboratory of Pharmacology, Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zoonoses Public Health. 2025 Sep;72(6):534-543. doi: 10.1111/zph.13229. Epub 2025 Jun 24.
Human brucellosis (HB) has re-emerged as a critical public health threat in China, necessitating robust forecasting tools for early intervention. This study evaluates the seasonal autoregressive fractionally integrated moving average (SARFIMA) model's performance in predicting HB epidemics, comparing it with the widely used seasonal autoregressive integrated moving average (SARIMA).
Monthly HB morbidity data from January 2012 to May 2023 in Henan were collected retrospectively and divided into training (January 2012 to December 2021) and testing (January 2022 to May 2023) segments to evaluate the predictive ability of SARFIMA, comparing it with the seasonal autoregressive integrated moving average (SARIMA). Sensitivity and secondary analyses were also conducted using HB incidence data in different periods in Henan and mainland China to confirm the predictive robustness.
HB incidence exhibited marked seasonality (peaks: May-June; troughs: December-January) and surged post-2018 (annual increase: 34.9%). The analysis identified distinct SARIMA and SARFIMA configurations for different prediction horizons in Henan. 17-step forecasts required autoregressive components with seasonal differencing, while 5-step predictions benefited from moving average terms. The SARFIMA models consistently exhibited fractional differencing parameters (0.329-0.487), indicating persistent temporal dependencies in the data structure. Although the SARFIMA produced smaller forecast errors than the best SARIMA in both horizons, the forecast errors were still large, and the prediction intervals of the SARFIMA were wider than those of the SARIMA. Further cross-validation and secondary analysis also showed that SARFIMA outperformed SARIMA in assessing HB epidemics.
SARFIMA marginally improves HB forecasting accuracy over SARIMA by addressing long-range dependence, but prediction reliability remains limited. Hybrid models integrating environmental/livestock data are recommended. Escalating HB incidence underscores urgent needs for livestock vaccination, public education on unpasteurized dairy risks, and real-time surveillance to mitigate zoonotic transmission in high-risk regions.
人类布鲁氏菌病（HB）在中国已再度成为严重的公共卫生威胁，因此需要强大的预测工具以便进行早期干预。本研究评估了季节性自回归分数积分移动平均（SARFIMA）模型在预测HB疫情方面的表现，并将其与广泛使用的季节性自回归积分移动平均（SARIMA）模型进行比较。
回顾性收集了河南省2012年1月至2023年5月的月度HB发病数据，并将其分为训练期（2012年1月至2021年12月）和测试期（2022年1月至2023年5月），以评估SARFIMA的预测能力，并与季节性自回归积分移动平均（SARIMA）模型进行比较。还利用河南省和中国大陆不同时期的HB发病率数据进行了敏感性分析和二次分析，以确认预测的稳健性。
HB发病率呈现出明显的季节性（高峰期：5 - 6月；低谷期：12月至1月），且在2018年后激增（年增长率：34.9%）。分析确定了河南省不同预测期的SARIMA和SARFIMA的不同配置。17步预测需要带有季节性差分的自回归成分，而5步预测则受益于移动平均项。SARFIMA模型始终呈现出分数差分参数（0.329 - 0.487），表明数据结构中存在持续的时间依赖性。尽管在两个预测期内，SARFIMA产生的预测误差均小于最佳SARIMA模型，但预测误差仍然很大，且SARFIMA的预测区间比SARIMA的更宽。进一步的交叉验证和二次分析也表明，在评估HB疫情方面，SARFIMA优于SARIMA。
SARFIMA通过解决长期依赖性，在一定程度上提高了HB预测的准确性，但预测可靠性仍然有限。建议采用整合环境/牲畜数据的混合模型。HB发病率的不断上升凸显了对牲畜进行疫苗接种、开展关于未杀菌乳制品风险的公众教育以及进行实时监测以减轻高风险地区人畜共患病传播的迫切需求。
