The Impacts of Climatic Factors and Vegetation on Hemorrhagic Fever with Renal Syndrome Transmission in China: A Study of 109 Counties

被引:12
|
作者
He, Junyu [1 ]
Wang, Yong [2 ]
Mu, Di [3 ]
Xu, Zhiwei [4 ]
Qian, Quan [2 ]
Chen, Gongbo [5 ]
Wen, Liang [2 ]
Yin, Wenwu [3 ]
Li, Shanshan [5 ]
Zhang, Wenyi [2 ]
Guo, Yuming [5 ]
机构
[1] Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China
[2] Chinese PLA Ctr Dis Control & Prevent, Beijing 100071, Peoples R China
[3] Chinese Ctr Dis Control & Prevent, Div Infect Dis, Key Lab Surveillance & Early Warning Infect Dis, Beijing 102206, Peoples R China
[4] Queensland Univ Technol, Sch Publ Hlth & Social Work, Inst Hlth & Biomed Innovat, Brisbane, Qld 4059, Australia
[5] Monash Univ, Sch Publ Hlth & Prevent Med, Dept Epidemiol & Prevent Med, Melbourne, Vic 3004, Australia
基金
中国国家自然科学基金;
关键词
orthohantavirus; hantavirus disease; risk map; distributed lag non-linear model; meta-analysis; HANTAAN VIRUS; HANTAVIRUS INFECTION; AMBIENT-TEMPERATURE; RODENT OUTBREAKS; VARIABILITY; PROVINCE; PRECIPITATION; ASSOCIATION; WEATHER; DISEASE;
D O I
10.3390/ijerph16183434
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne infectious disease caused by hantaviruses. About 90% of global cases were reported in China. We collected monthly data on counts of HFRS cases, climatic factors (mean temperature, rainfall, and relative humidity), and vegetation (normalized difference vegetation index (NDVI)) in 109 Chinese counties from January 2002 to December 2013. First, we used a quasi-Poisson regression with a distributed lag non-linear model to assess the impacts of these four factors on HFRS in 109 counties, separately. Then we conducted a multivariate meta-analysis to pool the results at the national level. The results of our study showed that there were non-linear associations between the four factors and HFRS. Specifically, the highest risks of HFRS occurred at the 45th, 30th, 20th, and 80th percentiles (with mean and standard deviations of 10.58 +/- 4.52 degrees C, 18.81 +/- 17.82 mm, 58.61 +/- 6.33%, 198.20 +/- 22.23 at the 109 counties, respectively) of mean temperature, rainfall, relative humidity, and NDVI, respectively. HFRS case estimates were most sensitive to mean temperature amongst the four factors, and the lag patterns of the impacts of these factors on HFRS were heterogeneous. Our findings provide rigorous scientific support to current HFRS monitoring and the development of early warning systems.
引用
收藏
页数:13
相关论文
共 50 条
  • [21] The Epidemic Characteristics and Changing Trend of Hemorrhagic Fever with Renal Syndrome in Hubei Province, China
    Zhang, Yi-Hui
    Ge, Liang
    Liu, Li
    Huo, Xi-Xiang
    Xiong, Hai-Rong
    Liu, Yuan-Yuan
    Liu, Dong-Ying
    Luo, Fan
    Li, Jin-Lin
    Ling, Jia-Xin
    Chen, Wen
    Liu, Jing
    Hou, Wei
    Zhang, Yun
    Fan, Hong
    Yang, Zhan-Qiu
    PLOS ONE, 2014, 9 (03):
  • [22] Hemorrhagic fever with renal syndrome with secondary hemophagocytic lymphohistiocytosis in West China: a case report
    Yang, Xiaoling
    Wang, Chuan
    Wu, Libo
    Jiang, Xiaoqian
    Zhang, Sumei
    Jing, Fuchun
    BMC INFECTIOUS DISEASES, 2019, 19 (1)
  • [23] Effectiveness of Hemorrhagic Fever with Renal Syndrome Bivalent Vaccine in China:A Metaanalysis
    Xiao-xia Huang
    Lei Yan
    Shi-wen Wang
    InfectionInternational(ElectronicEdition), 2012, 1 (01) : 46 - 50
  • [24] Comparison of Hantaan and Seoul viral infections among patients with hemorrhagic fever with renal syndrome (HFRS) in Heilongjiang, China
    Zhang, Xin
    Chen, Huan-Yong
    Zhu, Li-Ying
    Zeng, Ling-Lan
    Wang, Fei
    Li, Qing-Gang
    Shao, Feng-Juan
    Jiang, Hong-Qi
    Liu, Shi-Jie
    Ma, Ying-Jie
    Zhu, You
    Ma, Ying-Ji
    SCANDINAVIAN JOURNAL OF INFECTIOUS DISEASES, 2011, 43 (08) : 632 - 641
  • [25] Meteorological factors and risk of hemorrhagic fever with renal syndrome in Guangzhou, southern China, 2006-2015
    Wei, Yuehong
    Wang, Yang
    Li, Xiaoning
    Qin, Pengzhe
    Lu, Ying
    Xu, Jianmin
    Chen, Shouyi
    Li, Meixia
    Yang, Zhicong
    PLOS NEGLECTED TROPICAL DISEASES, 2018, 12 (06):
  • [26] Modeling and Predicting Hemorrhagic Fever with Renal Syndrome Trends Based on Meteorological Factors in Hu County, China
    Xiao, Dan
    Wu, Kejian
    Tan, Xin
    Le, Jing
    Li, Haitao
    Yan, Yongping
    Xu, Zhikai
    PLOS ONE, 2015, 10 (04):
  • [27] Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China
    Zhang, Rong
    Zhang, Ning
    Liu, Ying
    Liu, Tianxiao
    Sun, Jimin
    Ling, Feng
    Wang, Zhen
    FRONTIERS IN MEDICINE, 2022, 9
  • [28] Spatiotemporal dynamics of hemorrhagic fever with renal syndrome in Jiangxi province, China
    Yang, Shu
    Gao, Yuan
    Liu, Xiaobo
    Liu, Xiaoqing
    Liu, Yangqing
    Metelmann, Soeren
    Yuan, Chenying
    Yue, Yujuan
    Chen, Shengen
    Liu, Qiyong
    SCIENTIFIC REPORTS, 2020, 10 (01)
  • [29] Meteorological factors are associated with hemorrhagic fever with renal syndrome in Jiaonan County, China, 2006-2011
    Lin, Hualiang
    Zhang, Zhentang
    Lu, Liang
    Li, Xiujun
    Liu, Qiyong
    INTERNATIONAL JOURNAL OF BIOMETEOROLOGY, 2014, 58 (06) : 1031 - 1037
  • [30] Intrinsic and extrinsic drivers of transmission dynamics of hemorrhagic fever with renal syndrome caused by Seoul hantavirus
    Li, Yidan
    Cazelles, Bernard
    Yang, Guoqing
    Laine, Marko
    Huang, Zheng X. Y.
    Cai, Jun
    Tan, Hua
    Stenseth, Nils Chr.
    Tian, Huaiyu
    PLOS NEGLECTED TROPICAL DISEASES, 2019, 13 (09):