热带病与寄生虫学 ›› 2024, Vol. 22 ›› Issue (2): 83-88.doi: 10.3969/j.issn.1672-2302.2024.02.004

• 虫媒传染病专题 • 上一篇    下一篇

基于时空多成分模型的中缅边境地区景洪市登革热流行特征分析

唐烨榕1,2(), 周红宁1(), 李菁华2,3, 肖建鹏4   

  1. 1.云南省虫媒传染病防控关键技术创新团队,云南省虫媒传染病防控研究重点实验室,云南省热带传染病国际联合实验室,云南省寄生虫病防治所,云南普洱 665000
    2.中山大学公共卫生学院
    3.中山大学全球卫生研究中心
    4.广东省疾病预防控制中心,广东省公共卫生研究院
  • 收稿日期:2023-11-30 出版日期:2024-04-20 发布日期:2024-04-30
  • 通信作者: 周红宁,E-mail: zhouhn66@163.com
  • 作者简介:唐烨榕,女,硕士,主管医师,研究方向:寄生虫病和虫媒传染病预防控制。E-mail: tangyerong123@163.com
  • 基金资助:
    云南省重点研发计划项目(202103AQ100001);中国科学院重点部署项目(KFZD-SW-316);国家自然科学基金项目(U1602223)

Analysis of the epidemiological characteristics of dengue fever in Jinghong City, China-Myanmar border region by spatial-temporal multicomponent model

TANG Yerong1,2(), ZHOU Hongning1(), LI Jinghua2,3, XIAO Jianpeng4   

  1. 1. Yunnan Innovative Team of Key Techniques for Vector-borne Disease Control and Prevention; Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research; Yunan International Joint Laboratory of Tropical Infectious Diseases; Yunnan Institute of Parasitic Diseases, Pu’er 665000, Yunnan Province, China
    2. School of public health, Sun Yat-sen University
    3. Global Health Research Center, Sun Yat-sen University
    4. Guangdong Provincial Center for Disease Control and Prevention; Guangdong Provincial Institution of Public Health
  • Received:2023-11-30 Online:2024-04-20 Published:2024-04-30
  • Contact: ZHOU Hongning, E-mail: zhouhn66@163.com

摘要:

目的 了解中缅边境地区景洪市2019年登革热时空分布特征及传播影响因素。方法 收集中缅边境地区景洪市2019年登革热本地病例逐日发病数,将人口密度、人均GDP、日平均气温、日平均相对湿度和日照时数等因素作为协变量,构建基于Power-law算法的时空多成分模型,通过赤池信息量(Akaike information criterion, AIC)来评价模型的拟合效果。结果 中缅边境地区景洪市2019年累计报告3 303例登革热本地病例,发病率为763.44/10万。将人均GDP纳入时间自相关成分,同时将人口密度、日平均气温、日平均最高气温和日平均相对湿度纳入局部特性成分的时空多成分模型的拟合效果最优(AIC=2 317),此时时间自相关成分为0.215 1[95%CI:(0.081 2,0.570 2)]、空间流行成分为0.000 4[95%CI:(0.000 2,0.001 0)]、局部特性成分为3.015 2[95%CI:(1.650 7,5.507 8)]。协变量日平均相对湿度、人口密度、人均GDP、日平均气温、日平均最高气温的作用强度依次为2.815 9[95%CI:(0.021 6,367.168 5)]、1.822 7[95%CI:(1.497 6,2.218 3)]、1.208 8[95%CI:(1.057 7,1.381 6)]、0.356 1[95%CI:(0.011 9,10.673 9)]、0.310 4[95%CI:(0.003 6,126.548 2)]。对发病数>20例的区域分析发现,允景洪街道、西双版纳旅游度假区和景洪工业园区时间自相关成分影响较大,嘎洒镇、勐龙镇和勐罕镇局部特性成分影响较大。结论 景洪市不同区域登革热时空构成存在差异性,具有不同的时空传播特征。人均GDP会扩大前期疫情对后期疫情传播的影响,人口密度、日平均气温、日平均最高气温和日平均相对湿度则会影响研究区域登革热本地风险水平。

关键词: 登革热, 中缅边境地区, 景洪市, 时空多成分模型

Abstract:

Objective To understand the spatial and temporal distribution characteristics of dengue fever and the factors affecting the dengue transmission in Jinghong City located in the China-Myanmar border area in 2019. Methods The daily incidences of dengue fever were initially collected in Jinghong City in 2019. Then the spatial-temporal multicomponent model on the Power-law algorithm basis was developed using population density, GDP per capita, daily average temperature, daily average relative humidity and sunshine hours as covariates to analyze the spatial and temporal transmission characteristics of dengue fever in different regions. The fitting effect of the model was evaluated by the Akaike information criterion(AIC) values. Results In 2019, a total of 3 303 local cases of dengue fever were reported in Jinghong City, with an incidence of 763.44/100 000 population. The spatio-temporal multicomponent model showed the best fitting effect if GDP per capita was included in the autoregressive compartment, population density, daily average temperature, daily mean maximum temperature and daily average relative humidity were included in the endemic compartment (AIC=2 317). At this point, the autoregressive compartment, the epidemic component and the endemic compartment were 0.215 1 [95% CI: (0.081 2, 0.570 2)], 0.000 4 [95% CI: (0.000 2, 0.001 0)] and 3.015 2 [95% CI: (1.650 7, 5.507 8)], respectively. The strength of effect of the covariates was 2.815 9 [95% CI: (0.021 6, 367.168 5)], 1.822 7 [95% CI: (1.497 6, 2.218 3)], 1.208 8 [95% CI: (1.057 7, 1.381 6)], 0.356 1 [95% CI: (0.011 9, 10.673 9)] and 0.310 4 [95% CI: (0.003 6, 126.548 2)] respectively for the average daily relative humidity, population density, GDP per capita, mean daily air temperature, and mean daily maximum temperature. Analysis on the incidence over 20 cases in an area demonstrated autocorrelation component produced greater effect on the incidence in Yunjinghong Community, Xishuangbanna Tourist Resort and Jinghong Industrial Park. However, the endemic compartment of Gasa Town, Menglong Town and Menghan Town were relatively large. Conclusion Dengue fever prevalence varies in different areas in Jinghong City, with spatial-temporal transmission trends. GDP per capita may lead to following epidemic from previous situation of dengue fever prevalence, and population density, daily average temperature, daily mean maximum temperature and daily average relative humidity can result in the risks of dengue fever epidemic in the local area.

Key words: Dengue fever, Border area of China-Myanmar, Jinghong City, Spatial-temporal multicomponent model

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