热带病与寄生虫学 ›› 2025, Vol. 23 ›› Issue (4): 199-204,210.doi: 10.20199/j.issn.1672-2302.2025.04.002

• 登革热防控专题 • 上一篇    下一篇

2005—2023年云南省境外输入登革热流行特征及预测研究

傅佳璐1(), 郑尔达2(), 戚艳波2, 吴林波3, 何继波2(), 杨锐3()   

  1. 1.大理大学公共卫生学院云南大理 671003
    2.云南省疾病预防控制中心
    3.云南省寄生虫病防治所
  • 收稿日期:2025-06-30 出版日期:2025-08-20 发布日期:2025-09-19
  • 通信作者: 何继波,E-mail: ynhejibo@163.com;杨锐,E-mail: jody877@163.com
  • 作者简介:傅佳璐,女,硕士在读,研究方向:公共卫生。E-mail: fujialu0729@163.com;|郑尔达,男,硕士,主管医师,研究方向:卫生应急、传染病流行病学。E-mail: 755130864@qq.com

Epidemiological characteristics and prediction of overseas imported dengue fever in Yunnan Province from 2005 to 2023

FU Jialu1(), ZHENG Erda2(), QI Yanbo2, WU Linbo3, HE Jibo2(), YANG Rui3()   

  1. 1. School of Public Health, Dali University, Dali 671003, Yunnan Province, China
    2. Yunnan Center for Disease Control and Prevention
    3. Yunnan Institute of Parasitic Diseases
  • Received:2025-06-30 Online:2025-08-20 Published:2025-09-19
  • Contact: HE Jibo, E-mail: ynhejibo@163.com; YANG Rui, E-mail: jody877@163.com

摘要:

目的 分析云南省境外输入登革热的流行特征并对发病情况进行预测,为登革热的防控提供参考。方法 从中国疾病预防控制信息系统中获取2005—2023年云南省境外输入登革热病例的相关资料,使用描述性研究方法分析其流行特征;使用2005—2023年的病例数据拟合SARIMA模型,以2024年病例数据评价模型。结果 2005—2023年,云南省累计报告境外输入登革热病例6 030例,占登革热报告病例总数的19.50%。境外输入病例报告主要集中在8—11月,累计报告5 094例(占84.48%)。报告病例数前3位的州(市)分别为德宏傣族景颇族自治州(2 610例,占43.28%)、西双版纳傣族自治州(1 688例,占27.99%)和临沧市(918例,占15.22%);病例主要来源国为缅甸(4 907例,占81.38%)。男、女病例数分别为3 477例(占57.66%)和2 553例(占42.34%);主要集中在20~49岁(4 063例,占67.38%);农民最多(2 575例,占42.70%)。SARIMA(1,0,1)(1,0,1)12模型的拟合效果好,2024年各月份的实际病例数均在模型预测值的95%置信区间内。结论 云南省境外输入登革热病例呈现明显季节性,病例主要来自缅甸,输入地集中于德宏傣族景颇族自治州与西双版纳傣族自治州。建议结合季节与地域特征,强化入境重点人群的防控。SARIMA(1,0,1)(1,0,1)12模型能较好地拟合云南省境外输入登革热的月发病数,适合用于其发病趋势的短期预测。

关键词: 登革热, 境外输入病例, 流行特征, 差分自回归移动平均模型, 云南省

Abstract:

Objective To analyze the epidemiological characteristics of overseas imported dengue fever cases in Yunnan Province, and predict the incidence trend for evidence for prevention and control of dengue fever. Methods Case data of overseas imported dengue fever reported in Yunnan Province from 2005 to 2023 were obtained from the Chinese Disease Prevention and Control Information System, and descriptive research methods were used to analyze the epidemiological characteristics of this infection. The SARIMA model was fitted using the case data from 2005 to 2023, and the model was evaluated with the case data of 2024. Results A total of 6 030 overseas imported dengue fever cases were reported in Yunnan Province from 2005 to 2023, which accounted for 19.50% of the total reported cases of dengue fever. The peak period for the imported cases was from August to November (5 094 cases, 84.48%). The top three prefectures/municipalities with the highest case number were Dehong Dai and Jingpo Autonomous Prefecture (2 610 cases, 43.28%), Xishuangbanna Dai Autonomous Prefecture (1 688 cases, 27.99%), and Lincang City (918 cases, 15.22%). The main source country of the cases was Myanmar (4 907 cases, 81.38%). The number of male and female cases was 3 477 (57.66%) and 2 553 (42.34%), respectively. The cases were mainly concentrated in the age group of 20-49 years (4 063 cases, 67.38%), and farmers were dominant in the cases reported (2 575 cases, 42.70%). The SARIMA(1,0,1)(1,0,1)12 model demonstrated excellent fitting effect, with the actual monthly case counts in 2024 all lying within the 95% confidence interval of the model’s forecasts. Conclusion Overseas imported dengue fever cases in Yunnan Province exhibited distinct seasonality, mainly originating from Myanmar and concentrating in Dehong Dai and Jingpo Autonomous Prefecture and Xishuangbanna Dai Autonomous Prefecture. It is recommended to strengthen dengue prevention and control measures for key inbound populations in line with seasonal and regional characteristics. Additionally, the SARIMA(1,0,1)(1,0,1)12 model can well fit the monthly number of overseas imported dengue fever cases in Yunnan Province, making it suitable for short-term prediction of the incidence trend of such cases.

Key words: Dengue fever, Overseas imported case, Epidemiology characteristics, Autoregressive integrated moving average model, Yunnan Province

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