热带病与寄生虫学

热带病与寄生虫学 ›› 2026, Vol. 24 ›› Issue (1): 36-40.doi: 10.20199/j.issn.1672-2302.2026.01.007

• 防治研究 • 上一篇    下一篇

基于分布滞后非线性模型的安徽省流感发病与气象因素的关联性研究

刘雅倩1(), 龚磊2, 戴艳妮1, 耿浩翔3, 张力芹1, 孟梦1, 朱梦2, 朱标2, 吴家兵1,2()   

  1. 1 蚌埠医科大学公共卫生学院， 安徽蚌埠 233000
    2 安徽省疾病预防控制中心
    3 安徽医科大学公共卫生学院
  • 收稿日期:2025-09-15 出版日期:2026-02-20 发布日期:2026-03-31
  • 通信作者: 吴家兵，E-mail: wjb0386@126.com
  • 作者简介:刘雅倩，女，硕士在读，研究方向：公共卫生。E-mail: 3014585209@qq.com
  • 基金资助:
    国家疾病预防控制局公共卫生人才培养支持项目(202303)

Association between influenza incidence and meteorological factors in Anhui Province based on a distributed lag non-linear model

LIU Yaqian1(), GONG Lei2, DAI Yanni1, GENG Haoxiang3, ZHANG Liqin1, MENG Meng1, ZHU Meng2, ZHU Biao2, WU Jiabing1,2()   

  1. 1 School of Public Health, Bengbu Medical University, Bengbu 233000, Anhui Province, China
    2 Anhui Provincial Center for Disease Control and Prevention
    3 School of Public Health, Anhui Medical University
  • Received:2025-09-15 Online:2026-02-20 Published:2026-03-31
  • Contact: WU Jiabing, E-mail: wjb0386@126.com

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摘要：

目的 分析气象因素对安徽省流感发病的影响，为制定流感防控策略提供依据。方法 通过中国疾病预防控制信息系统收集2016―2019年安徽省流感病例资料，从安徽省气象局获取同期气象数据，运用分布滞后非线性模型（distributed lag non-linear model, DLNM），探讨气象因素与安徽省流感周发病数之间的暴露-滞后效应。结果 2016―2019年安徽省共报告流感病例152 284例，年均发病率为60.95/10万，发病率呈逐年上升趋势。周平均气温、周平均相对湿度和周总降水量与流感发病风险的暴露-反应曲线分别呈近似倒“U”型、“U”型及倒“U”型。周平均气温在10.40~17.20 ℃时发病风险升高，而在17.40~26.40 ℃时发病风险降低（P均<0.05）；气温在P75（24.27 ℃）时，其滞后0~3周呈保护效应（RR=0.75，95%CI：0.58~0.98）。周平均相对湿度在55.50%~68.40%和83.20%~92.00%时发病风险升高（P均<0.05）；相对湿度在P5（62.77%）和P95（88.34%）时，其滞后0~3周的累积效应显著升高（RR=1.48，95%CI：1.14~1.92；RR=1.51，95%CI：1.16~1.96）。周总降水量在242.20~1 254.60 mm时发病风险升高（P<0.05）；降水量在P95（1 090.65 mm）时，其滞后0~3周的累积效应显著升高（RR=1.46，95%CI：1.02~2.10）。结论 较低的气温、较低的相对湿度、较高的相对湿度、较高的降水量均可能增加安徽省的流感发病风险，而较高的气温可能降低发病风险，且上述效应均具有一定的滞后持续性。将气象指标纳入流感预测模型，有助于提高预警的准确性。

关键词: 流感, 气象因素, 分布滞后非线性模型, 安徽省

Abstract:

Objective To analyze the effects of meteorological factors on influenza incidence in Anhui Province, and to provide evidence for the development of influenza control strategies. Methods Influenza surveillance data in Anhui Province from 2016 to 2019 were retrieved from the Chinese Center for Disease Control and Prevention Information System. Meteorological data for the corresponding period were collected from the Anhui Meteorological Bureau. A distributed lag non-linear model (DLNM) was applied to evaluate the exposure-lag effects of meteorological factors on weekly influenza incidence. Results From 2016 to 2019, a total of 152 284 influenza cases were reported in Anhui Province, with an average annual incidence of 60.95 per 100 000 population, showing an increasing trend over the study period. The exposure-response relationship between influenza risk and weekly mean temperature, weekly mean relative humidity, and weekly precipitation presented approximately in inverted U-shape, U-shape, and U-shape for those years. Influenza risk increased when weekly mean temperature ranged from 10.40 to 17.20 ℃, yet decreased at 17.40-26.40 ℃ (both P<0.05). Relatively high temperature (P75=24.27 ℃) exhibited a protective effect over lag 0-3 weeks (RR=0.75, 95%CI: 0.58-0.98). Weekly mean relative humidity was associated with increased influenza risk at 55.50%-68.40% and 83.20%-92.00% (both P<0.05). Both relatively low humidity (P5=62.77%) and high relatively humidity (P95=88.34%) showed significantly elevated cumulative effects over lag 0-3 weeks (RR=1.48, 95%CI: 1.14-1.92; RR=1.51, 95%CI: 1.16-1.96). Influenza risk increased upon weekly precipitation ranging from 242.20 to 1 254.60 mm (P<0.05), and relatively high precipitation (P95=1 090.65 mm) exhibited significantly elevated cumulative effects over lag 0-3 weeks (RR=1.46, 95%CI: 1.02-2.10). Conclusion Relatively low temperature, low and high relative humidity, and high precipitation may add the risk of influenza incidence in Anhui Province, whereas relatively high temperature may reduce the risks. These effects exhibit certain lagged and sustained characteristics. Incorporating meteorological indicators into influenza prediction models may help improve early warning accuracy.

Key words: Influenza, Meteorological factors, Distributed lag non-linear model, Anhui Province

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