The Spatiotemporal Pattern and Its Determinants of Hemorrhagic Fever With Renal Syndrome in Northeastern China: Spatiotemporal Analysis

被引:12
作者
Wang, Yanding [1 ,2 ]
Wei, Xianyu [3 ]
Jia, Ruizhong [2 ]
Peng, XingYu [1 ]
Zhang, Xiushan [2 ]
Yang, Meitao [1 ,2 ]
Li, Zhiqiang [1 ,2 ]
Guo, Jinpeng [2 ]
Chen, Yong [2 ]
Yin, Wenwu [4 ]
Zhang, Wenyi [1 ,2 ,3 ]
Wang, Yong [1 ,2 ,3 ,5 ]
机构
[1] China Med Univ, Sch Publ Hlth, Shenyang, Peoples R China
[2] Chinese PLA Ctr Dis Control & Prevent, Beijing, Peoples R China
[3] Anhui Med Univ, Sch Publ Hlth, Hefei, Peoples R China
[4] Chinese Ctr Dis Control & Prevent, Beijing, Peoples R China
[5] Chinese PLA Ctr Dis Control & Prevent, 20 East St, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
HFRS; climate change; Northeastern China; spatiotemporal dynamic; Geodetector; VIRUS; ANTIBODY; PROVINCE;
D O I
10.2196/42673
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background: Hemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease mainly transmitted by rodents. However, the determinants of its spatiotemporal patterns in Northeast China remain unclear.Objective: This study aimed to investigate the spatiotemporal dynamics and epidemiological characteristics of HFRS and detect the meteorological effect of the HFRS epidemic in Northeastern China.Methods: The HFRS cases of Northeastern China were collected from the Chinese Center for Disease Control and Prevention, and meteorological data were collected from the National Basic Geographic Information Center. Times series analyses, wavelet analysis, Geodetector model, and SARIMA model were performed to identify the epidemiological characteristics, periodical fluctuation, and meteorological effect of HFRS in Northeastern China.Results: A total of 52,655 HFRS cases were reported in Northeastern China from 2006 to 2020, and most patients with HFRS (n=36,558, 69.43%) were aged between 30-59 years. HFRS occurred most frequently in June and November and had a significant 4-to 6-month periodicity. The explanatory power of the meteorological factors to HFRS varies from 0.15 <= q <= 0.01. In Heilongjiang province, mean temperature with a 4-month lag, mean ground temperature with a 4-month lag, and mean pressure with a 5-month lag had the most explanatory power on HFRS. In Liaoning province, mean temperature with a 1-month lag, mean ground temperature with a 1-month lag, and mean wind speed with a 4-month lag were found to have an effect on HFRS, but in Jilin province, the most important meteorological factors for HFRS were precipitation with a 6-month lag and maximum evaporation with a 5-month lag. The interaction analysis of meteorological factors mostly showed nonlinear enhancement. The SARIMA model predicted that 8,343 cases of HFRS are expected to occur in Northeastern China.Conclusions: HFRS showed significant inequality in epidemic and meteorological effects in Northeastern China, and eastern prefecture-level cities presented a high risk of epidemic. This study quantifies the hysteresis effects of different meteorological factors and prompts us to focus on the influence of ground temperature and precipitation on HFRS transmission in future studies, which could assist local health authorities in developing HFRS-climate surveillance, prevention, and control strategies targeting high-risk populations in China.
引用
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页数:12
相关论文
共 53 条
[1]   Distribution of geographical scale, data aggregation unit and period in the correlation analysis between temperature and incidence of HFRS in mainland China: A systematic review of 27 ecological studies [J].
Bai, Xing-Hua ;
Peng, Cheng ;
Jiang, Tao ;
Hu, Zhu-Min ;
Huang, De-Sheng ;
Guan, Peng .
PLOS NEGLECTED TROPICAL DISEASES, 2019, 13 (08)
[2]   Progress on the Prevention and Treatment of Hantavirus Disease [J].
Brocato, Rebecca L. ;
Hooper, Jay W. .
VIRUSES-BASEL, 2019, 11 (07)
[3]   Time-dependent spectral analysis of epidemiological time-series with wavelets [J].
Cazelles, Bernard ;
Chavez, Mario ;
de Magny, Guillaume Constantin ;
Guegan, Jean-Francois ;
Hales, Simon .
JOURNAL OF THE ROYAL SOCIETY INTERFACE, 2007, 4 (15) :625-636
[4]   Spatiotemporal variations of surface ozone and its influencing factors across Tibet: A Geodetector-based study [J].
Chen, Yan ;
Zhou, Yunqiao ;
NixiaCiren ;
Zhang, Huifang ;
Wang, Caihong ;
GesangDeji ;
Wang, Xiaoping .
SCIENCE OF THE TOTAL ENVIRONMENT, 2022, 813
[5]   Soil microbial respiration adapts to ambient temperature in global drylands [J].
Dacal, Marina ;
Bradford, Mark A. ;
Plaza, Cesar ;
Maestre, Fernando T. ;
Garcia-Palacios, Pablo .
NATURE ECOLOGY & EVOLUTION, 2019, 3 (02) :232-+
[6]  
Government of Qitaihe City China, US
[7]   Probabilistic logic analysis of the highly heterogeneous spatiotemporal HFRS incidence distribution in Heilongjiang province (China) during 2005-2013 [J].
He, Junyu ;
Christakos, George ;
Wu, Jiaping ;
Jankowski, Piotr ;
Langousis, Andreas ;
Wang, Yong ;
Yin, Wenwu ;
Zhang, Wenyi .
PLOS NEGLECTED TROPICAL DISEASES, 2019, 13 (01)
[8]   Spatiotemporal variation of the association between climate dynamics and HFRS outbreaks in Eastern China during 2005-2016 and its geographic determinants [J].
He, Junyu ;
Christakos, George ;
Wu, Jiaping ;
Cazelles, Bernard ;
Qian, Quan ;
Mu, Di ;
Wang, Yong ;
Yin, Wenwu ;
Zhang, Wenyi .
PLOS NEGLECTED TROPICAL DISEASES, 2018, 12 (06)
[9]   Hemorrhagic fever with renal syndrome and coexisting hantavirus pulmonary syndrome [J].
Hong, Young Min ;
Moon, Jin Chang ;
Yang, Hee Chan ;
Kang, Kyung Pyo ;
Kim, Won ;
Park, Sung Kwang ;
Lee, Sik .
KIDNEY RESEARCH AND CLINICAL PRACTICE, 2012, 31 (02) :118-120
[10]   Hantavirus infection: a global zoonotic challenge [J].
Jiang, Hong ;
Zheng, Xuyang ;
Wang, Limei ;
Du, Hong ;
Wang, Pingzhong ;
Bai, Xuefan .
VIROLOGICA SINICA, 2017, 32 (01) :32-43