Department of STD control and prevention, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong Province518020, China.
Department of Epidemiology and Health Statistics, XiangYa School of Public Health, Central South University, Changsha, Hunan Province410078, China.
Epidemiol Infect. 2020 Mar 17;148:e76. doi: 10.1017/S0950268820000680.
Chlamydia trachomatis (CT) infection has been a major public health threat globally. Monitoring and prediction of CT epidemic status and trends are important for programme planning, allocating resources and assessing impact; however, such activities are limited in China. In this study, we aimed to apply a seasonal autoregressive integrated moving average (SARIMA) model to predict the incidence of CT infection in Shenzhen city, China. The monthly incidence of CT between January 2008 and June 2019 in Shenzhen was used to fit and validate the SARIMA model. A seasonal fluctuation and a slightly increasing pattern of a long-term trend were revealed in the time series of CT incidence. The monthly CT incidence ranged from 4.80/100 000 to 21.56/100 000. The mean absolute percentage error value of the optimal model was 8.08%. The SARIMA model could be applied to effectively predict the short-term CT incidence in Shenzhen and provide support for the development of interventions for disease control and prevention.
沙眼衣原体（CT）感染已成为全球主要的公共卫生威胁。监测和预测 CT 流行状况和趋势对于规划方案、分配资源和评估影响至关重要；然而，在中国，此类活动受到限制。本研究旨在应用季节性自回归综合移动平均（SARIMA）模型预测中国深圳市 CT 感染的发病率。使用深圳市 2008 年 1 月至 2019 年 6 月的每月 CT 发病率来拟合和验证 SARIMA 模型。在 CT 发病率的时间序列中显示出季节性波动和长期趋势的略微增加模式。每月 CT 发病率范围为 4.80/100000 至 21.56/100000。最佳模型的平均绝对百分比误差值为 8.08%。SARIMA 模型可用于有效预测深圳市短期 CT 发病率，并为疾病控制和预防干预措施的制定提供支持。
