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Wang Y, Liang Z, Qing S, Xi Y, Xu C, Lin F. Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model. Sci Rep 2024; 14:9739. [PMID: 38679612 PMCID: PMC11056385 DOI: 10.1038/s41598-024-58023-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 03/25/2024] [Indexed: 05/01/2024] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) poses a major threat in Shandong. This study aimed to investigate the long- and short-term asymmetric effects of meteorological factors on HFRS and establish an early forecasting system using autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Between 2004 and 2019, HFRS exhibited a declining trend (average annual percentage change = - 9.568%, 95% CI - 16.165 to - 2.451%) with a bimodal seasonality. A long-term asymmetric influence of aggregate precipitation (AP) (Wald long-run asymmetry [WLR] = - 2.697, P = 0.008) and aggregate sunshine hours (ASH) (WLR = 2.561, P = 0.011) on HFRS was observed. Additionally, a short-term asymmetric impact of AP (Wald short-run symmetry [WSR] = - 2.419, P = 0.017), ASH (WSR = 2.075, P = 0.04), mean wind velocity (MWV) (WSR = - 4.594, P < 0.001), and mean relative humidity (MRH) (WSR = - 2.515, P = 0.013) on HFRS was identified. Also, HFRS demonstrated notable variations in response to positive and negative changes in ∆MRH(-), ∆AP(+), ∆MWV(+), and ∆ASH(-) at 0-2 month delays over the short term. In terms of forecasting, the NARDL model demonstrated lower error rates compared to ARDL. Meteorological parameters have substantial long- and short-term asymmetric and/or symmetric impacts on HFRS. Merging NARDL model with meteorological factors can enhance early warning systems and support proactive measures to mitigate the disease's impact.
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Affiliation(s)
- Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, The First Affiliated Hospital of Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China.
| | - Ziyue Liang
- Department of Epidemiology and Health Statistics, School of Public Health, The First Affiliated Hospital of Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Siyu Qing
- Department of Epidemiology and Health Statistics, School of Public Health, The First Affiliated Hospital of Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Yue Xi
- Department of Epidemiology and Health Statistics, School of Public Health, The First Affiliated Hospital of Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Chunjie Xu
- Beijing Key Laboratory of Antimicrobial Agents/Laboratory of Pharmacology, Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100050, China
| | - Fei Lin
- Department of Epidemiology and Health Statistics, School of Public Health, The First Affiliated Hospital of Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China.
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Wang Y, Zhang C, Gao J, Chen Z, Liu Z, Huang J, Chen Y, Li Z, Chang N, Tao Y, Tang H, Gao X, Xu Y, Wang C, Li D, Liu X, Pan J, Cai W, Gong P, Luo Y, Liang W, Liu Q, Stenseth NC, Yang R, Xu L. Spatiotemporal trends of hemorrhagic fever with renal syndrome (HFRS) in China under climate variation. Proc Natl Acad Sci U S A 2024; 121:e2312556121. [PMID: 38227655 PMCID: PMC10823223 DOI: 10.1073/pnas.2312556121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 12/05/2023] [Indexed: 01/18/2024] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by the rodent-transmitted orthohantaviruses (HVs), with China possessing the most cases globally. The virus hosts in China are Apodemus agrarius and Rattus norvegicus, and the disease spread is strongly influenced by global climate dynamics. To assess and predict the spatiotemporal trends of HFRS from 2005 to 2098, we collected historical HFRS data in mainland China (2005-2020), historical and projected climate and population data (2005-2098), and spatial variables including biotic, environmental, topographical, and socioeconomic. Spatiotemporal predictions and mapping were conducted under 27 scenarios incorporating multiple integrated representative concentration pathway models and population scenarios. We identify the type of magistral HVs host species as the best spatial division, including four region categories. Seven extreme climate indices associated with temperature and precipitation have been pinpointed as key factors affecting the trends of HFRS. Our predictions indicate that annual HFRS cases will increase significantly in 62 of 356 cities in mainland China. Rattus regions are predicted to be the most active, surpassing Apodemus and Mixed regions. Eighty cities are identified as at severe risk level for HFRS, each with over 50 reported cases annually, including 22 new cities primarily located in East China and Rattus regions after 2020, while 6 others develop new risk. Our results suggest that the risk of HFRS will remain high through the end of this century, with Rattus norvegicus being the most active host, and that extreme climate indices are significant risk factors. Our findings can inform evidence-based policymaking regarding future risk of HFRS.
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Affiliation(s)
- Yuchen Wang
- Vanke School of Public Health, Tsinghua University, Beijing100084, China
- Institute for Healthy China, Tsinghua University, Beijing100084, China
| | - Chutian Zhang
- Vanke School of Public Health, Tsinghua University, Beijing100084, China
- Institute for Healthy China, Tsinghua University, Beijing100084, China
- College of Natural Resources and Environment, Northwest A&F University, Yangling712100, China
| | - Jing Gao
- Vanke School of Public Health, Tsinghua University, Beijing100084, China
- Institute for Healthy China, Tsinghua University, Beijing100084, China
- Respiratory Medicine Unit, Department of Medicine & Centre for Molecular Medicine, Karolinska Institute, Stockholm171 77, Sweden
- Heart and Lung Centre, Department of Pulmonary Medicine, University of Helsinki and Helsinki University Hospital, Helsinki00290, Finland
| | - Ziqi Chen
- Vanke School of Public Health, Tsinghua University, Beijing100084, China
- Institute for Healthy China, Tsinghua University, Beijing100084, China
| | - Zhao Liu
- School of Linkong Economics and Management, Beijing Institute of Economics and Management, Beijing100102, China
| | - Jianbin Huang
- Beijing Yanshan Earth Critical Zone National Research Station, University of Chinese Academy of Sciences, Beijing101408, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing100190, China
| | - Yidan Chen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Zhichao Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing100101, China
| | - Nan Chang
- School of Public Health, Nanjing Medical University, Nanjing210000, China
| | - Yuxin Tao
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing100084, China
| | - Hui Tang
- Department of Geosciences, Natural History Museum, University of Oslo, Blindern, Oslo0316, Norway
- Natural History Museum, University of Oslo, Blindern, Oslo0316, Norway
- Department of Geosciences and Geography, University of Helsinki, Helsinki00014, Finland
| | - Xuejie Gao
- Climate Change Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing100029, China
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing100049, China
| | - Ying Xu
- National Climate Centre, China Meteorological Administration, Beijing100081, China
| | - Can Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Dong Li
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing100084, China
| | - Xiaobo Liu
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing102206, China
| | - Jingxiang Pan
- Joan & Sanford I. Weill Medical College, Cornell University, Ithaca, New York10065
| | - Wenjia Cai
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modelling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
| | - Peng Gong
- Department of Earth Sciences and Geography, University of Hong Kong, Hong Kong Special Administrative Region999077, China
| | - Yong Luo
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modelling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
| | - Wannian Liang
- Vanke School of Public Health, Tsinghua University, Beijing100084, China
- Institute for Healthy China, Tsinghua University, Beijing100084, China
| | - Qiyong Liu
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing102206, China
| | - Nils Chr. Stenseth
- Vanke School of Public Health, Tsinghua University, Beijing100084, China
- Centre for Pandemics and One-Health Research, Faculty of Medicine, University of Oslo, OsloN-0316, Norway
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, Faculty of Mathematics and Natural Sciences, University of Oslo, OsloN-0315, Norway
| | - Ruifu Yang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing100071, China
| | - Lei Xu
- Vanke School of Public Health, Tsinghua University, Beijing100084, China
- Institute for Healthy China, Tsinghua University, Beijing100084, China
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Lv CL, Tian Y, Qiu Y, Xu Q, Chen JJ, Jiang BG, Li ZJ, Wang LP, Hay SI, Liu W, Fang LQ. Dual seasonal pattern for hemorrhagic fever with renal syndrome and its potential determinants in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160339. [PMID: 36427712 DOI: 10.1016/j.scitotenv.2022.160339] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/16/2022] [Accepted: 11/16/2022] [Indexed: 06/16/2023]
Abstract
Hemorrhagic fever with renal syndrome (HFRS) continued to affect human health across Eurasia, which complicated by climate change has posed a challenge for the disease prevention measures. Nation-wide surveillance data of HFRS cases were collected during 2008-2020.The seasonality and epidemiological features were presented by combining the HFRS incidence and the endemic types data. Factors potentially involved in affecting incidence and shaping disease seasonality were investigated by generalized additive mixed model, distributed lag nonlinear model and multivariate meta-analysis. A total of 76 cities that reported totally 111,054 cases were analyzed. Three endemic types were determined, among them the Type I cities (Hantaan virus-dominant) were related to higher incidence level, showing one spike every year in Autumn-Winter season; Type II (Seoul virus-dominant) cities were related to lower incidence, showing one spike in Spring, while Type III (Hantaan/Seoul-mixed type) showed dual peaks with incidence lying between. Persistently heavy rainfall had significantly negative influence on HFRS incidence in Hantaan virus-dominant endemic area, while a significantly opposite effect was identified when continuously heavy rainfall induced floods, where temperature and relative humidity affected HFRS incidence via an approximately parabolic or linear manner, however few or no such effects was shown in Seoul virus-dominant endemic areas, which was more vulnerable to temperature variation. Dual seasonal pattern of HFRS was depended on the dominant genotypes of hantavirus, and impact of climate on HFRS was greater in Hantaan virus-dominant endemic areas, than in Seoul virus-dominant areas.
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Affiliation(s)
- Chen-Long Lv
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Yao Tian
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Yan Qiu
- Beijing Haidian District Center for Disease Control and Prevention, Beijing, China
| | - Qiang Xu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Jin-Jin Chen
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Bao-Gui Jiang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Zhong-Jie Li
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Li-Ping Wang
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, USA.
| | - Wei Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
| | - Li-Qun Fang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
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He J, Wang Y, Wei X, Sun H, Xu Y, Yin W, Wang Y, Zhang W. Spatial-temporal dynamics and time series prediction of HFRS in mainland China: A long-term retrospective study. J Med Virol 2023; 95:e28269. [PMID: 36320103 DOI: 10.1002/jmv.28269] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/08/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022]
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China. The current study aims to characterize the spatial-temporal dynamics of HFRS in mainland China during a long-term period (1950-2018). A total of 1 665 431 cases of HFRS were reported with an average annual incidence of 54.22 cases/100 000 individuals during 1950-2018. The joint regression model was used to define the global trend of the HFRS cases with an increasing-decreasing-slightly increasing-decreasing-slightly increasing trend during the 68 years. Then spatial correlation analysis and wavelet cluster analysis were used to identify four types of clusters of HFRS cases located in central and northeastern China. Lastly, the prophet model outperforms auto-regressive integrated moving average model in the HFRS modeling. Our findings will help reduce the knowledge gap on the transmission dynamics and distribution patterns of the HFRS in mainland China and facilitate to take effective preventive and control measures for the high-risk epidemic area.
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Affiliation(s)
- Junyu He
- Ocean College, Zhejiang University, Zhoushan, China.,Ocean Academy, Zhejiang University, Zhoushan, China
| | - Yanding Wang
- Department of Epidemiology and Biostatistics, School of Public Health, China Medical University, Shenyang, China.,Chinese PLA Center for Disease Control and Prevention, Beijing, China
| | - Xianyu Wei
- Chinese PLA Center for Disease Control and Prevention, Beijing, China.,Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Hailong Sun
- Chinese PLA Center for Disease Control and Prevention, Beijing, China
| | - Yuanyong Xu
- Chinese PLA Center for Disease Control and Prevention, Beijing, China
| | - Wenwu Yin
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, China Medical University, Shenyang, China.,Chinese PLA Center for Disease Control and Prevention, Beijing, China.,Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Wenyi Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, China Medical University, Shenyang, China.,Chinese PLA Center for Disease Control and Prevention, Beijing, China.,Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
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Wang Y, Wei X, Xiao X, Yin W, He J, Ren Z, Li Z, Yang M, Tong S, Guo Y, Zhang W, Wang Y. Climate and socio-economic factors drive the spatio-temporal dynamics of HFRS in Northeastern China. One Health 2022; 15:100466. [DOI: 10.1016/j.onehlt.2022.100466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/15/2022] [Accepted: 11/20/2022] [Indexed: 11/23/2022] Open
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Warnasekara J, Agampodi S, NR A. SARIMA and ARDL models for predicting leptospirosis in Anuradhapura district Sri Lanka. PLoS One 2022; 17:e0275447. [PMID: 36227833 PMCID: PMC9562162 DOI: 10.1371/journal.pone.0275447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 09/16/2022] [Indexed: 11/07/2022] Open
Abstract
Leptospirosis is considered a neglected tropical disease despite its considerable mortality and morbidity. Lack of prediction remains a major reason for underestimating the disease. Although many models have been developed, most of them focused on the districts situated in the wet zone due to higher case numbers in that region. However, leptospirosis remains a major disease even in the dry zone of Sri Lanka. The objective of this study is to develop a time series model to predict leptospirosis in the Anuradhapura district situated in the dry zone of Sri Lanka. Time series data on monthly leptospirosis incidences from January 2008 to December 2018 and monthly rainfall, rainy days, temperature, and relative humidity were considered in model fitting. The first 72 months (55%) were used to fit the model, and the subsequent 60 months(45%) were used to validate the model. The log-transformed dependent variable was employed for fitting the Univariate seasonal ARIMA model. Based on the stationarity of the mean of the five variables, the ARDL model was selected as the multivariate time series technique. Residuals analysis was performed on normality, heteroskedasticity, and serial correlation to validate the model. The lowest AIC and MAPE were used to select the best model. Univariate models could not be fitted without adjusting the outliers. Adjusting seasonal outliers yielded better results than the models without adjustments. Best fitted Univariate model was ARIMA(1,0,0)(0,1,1)12,(AIC-1.08, MAPE-19.8). Best fitted ARDL model was ARDL(1, 3, 2, 1, 0),(AIC-2.04,MAPE-30.4). The number of patients reported in the previous month, rainfall, rainy days, and temperature showed a positive association, while relative humidity was negatively associated with leptospirosis. Multivariate models fitted better than univariate models for the original data. Best-fitted models indicate the necessity of including other explanatory variables such as patient, host, and epidemiological factors to yield better results.
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Affiliation(s)
- Janith Warnasekara
- Department of Community Medicine, Faculty of Medicine and Allied Sciences, Rajarata University of Sri Lanka, Mihintale, Sri Lanka
- Postgraduate Institute of Agriculture, University of Peradeniya, Peradeniya, Sri Lanka
- * E-mail:
| | - Suneth Agampodi
- Department of Community Medicine, Faculty of Medicine and Allied Sciences, Rajarata University of Sri Lanka, Mihintale, Sri Lanka
| | - Abeynayake NR
- Postgraduate Institute of Agriculture, University of Peradeniya, Peradeniya, Sri Lanka
- Department of Agribusiness Management, Faculty of Agriculture and Plantation Management, Wayamba University of Sri Lanka, Kuliyapitiya, Sri Lanka
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Zhang R, Zhang N, Liu Y, Liu T, Sun J, Ling F, Wang Z. Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China. Front Med (Lausanne) 2022; 9:967554. [PMID: 36275790 PMCID: PMC9579348 DOI: 10.3389/fmed.2022.967554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 09/21/2022] [Indexed: 12/03/2022] Open
Abstract
Background Hemorrhagic fever with renal syndrome (HFRS) is a serious public health problem in China. The geographic distribution has went throughout China, among which Zhejiang Province is an important epidemic area. Since 1963, more than 110,000 cases have been reported. Methods We collected the meteorological factors and socioeconomic indicators of Zhejiang Province, and constructed the HFRS ecological niche model of Zhejiang Province based on the algorithm of maximum entropy. Results Model AUC from 2009 to 2018, is 0.806–0.901. The high incidence of epidemics in Zhejiang Province is mainly concentrated in the eastern, western and central regions of Zhejiang Province. The contribution of digital elevation model ranged from 2009 to 2018 from 4.22 to 26.0%. The contribution of average temperature ranges from 6.26 to 19.65%, Gross Domestic Product contribution from 7.53 to 21.25%, and average land surface temperature contribution with the highest being 16.73% in 2011. In addition, the average contribution of DMSP/OLS, 20-8 precipitation and 8-20 precipitation were all in the range of 9%. All-day precipitation increases with the increase of rainfall, and the effect curve peaks at 1,250 mm, then decreases rapidly, and a small peak appears again at 1,500 mm. Average temperature response curve shows an inverted v-shape, where the incidence peaks at 17.8°C. The response curve of HFRS for GDP and DMSP/OLS shows a positive correlation. Conclusion The incidence of HFRS in Zhejiang Province peaked in areas where the average temperature was 17.8°C, which reminds that in the areas where temperature is suitable, personal protection should be taken when going out as to avoid contact with rodents. The impact of GDP and DMSP/OLS on HFRS is positively correlated. Most cities have good medical conditions, but we should consider whether there are under-diagnosed cases in economically underdeveloped areas.
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Affiliation(s)
- Rong Zhang
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Department of Communicable Disease Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Ning Zhang
- Puyan Street Community Health Service Center of Binjiang District, Hangzhou, Zhejiang, China
| | - Ying Liu
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Department of Communicable Disease Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Tianxiao Liu
- School of Science and Technology, University of Tsukuba, Tsukuba, Japan
| | - Jimin Sun
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Department of Communicable Disease Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China,*Correspondence: Jimin Sun,
| | - Feng Ling
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Department of Communicable Disease Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China,Feng Ling,
| | - Zhen Wang
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Department of Communicable Disease Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China,Zhen Wang,
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Zhang R, Zhang N, Sun W, Lin H, Liu Y, Zhang T, Tao M, Sun J, Ling F, Wang Z. Analysis of the effect of meteorological factors on hemorrhagic fever with renal syndrome in Taizhou City, China, 2008–2020. BMC Public Health 2022; 22:1097. [PMID: 35650552 PMCID: PMC9161505 DOI: 10.1186/s12889-022-13423-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/13/2022] [Indexed: 04/06/2023] Open
Abstract
Abstract
Background
Hemorrhagic fever with renal syndrome (HFRS) is endemic in Zhejiang Province, China, while few studies have concentrated on the influence of meteorological factors on HFRS incidence in the area.
Methods
Data on HFRS and meteorological factors from January 1, 2008 to December 31, 2020 in Taizhou City, Zhejiang Province were collected. Multivariate analysis was conducted to the relationship between meteorological factors including minimum temperatures, relative humidity, and cumulative rainfall with HFRS.
Results
The HFRS incidence peaked in November and December and it was negatively correlated with average and highest average temperatures. Compared with median of meteorological factors, the relative risks (RR) of weekly average temperature at 12 ℃, weekly highest temperature at 18 ℃relative humidity at 40%, and cumulative rainfall at 240 mm were most significant and RRs were 1.41 (95% CI: 1.09–1.82), 1.32 (95% CI: 1.05–1.66), 2.18 (95% CI: 1.16–4.07), and 1.91 (95% CI: 1.16–2.73), respectively. Average temperature, precipitation, relative humidity had interactions on HFRS and the risk of HFRS occurrence increased with the decrease of average temperature and the increase of precipitation.
Conclusion
Our study results are indicative of the association of environmental factors with the HFRS incidence, probable recommendation could be use of environmental factors as early warning signals for initiating the control measure and response.
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Koehler FC, Di Cristanziano V, Späth MR, Hoyer-Allo KJR, Wanken M, Müller RU, Burst V. OUP accepted manuscript. Clin Kidney J 2022; 15:1231-1252. [PMID: 35756741 PMCID: PMC9217627 DOI: 10.1093/ckj/sfac008] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Indexed: 01/18/2023] Open
Abstract
Hantavirus-induced diseases are emerging zoonoses with endemic appearances and frequent outbreaks in different parts of the world. In humans, hantaviral pathology is characterized by the disruption of the endothelial cell barrier followed by increased capillary permeability, thrombocytopenia due to platelet activation/depletion and an overactive immune response. Genetic vulnerability due to certain human leukocyte antigen haplotypes is associated with disease severity. Typically, two different hantavirus-caused clinical syndromes have been reported: hemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS). The primarily affected vascular beds differ in these two entities: renal medullary capillaries in HFRS caused by Old World hantaviruses and pulmonary capillaries in HCPS caused by New World hantaviruses. Disease severity in HFRS ranges from mild, e.g. Puumala virus-associated nephropathia epidemica, to moderate, e.g. Hantaan or Dobrava virus infections. HCPS leads to a severe acute respiratory distress syndrome with high mortality rates. Due to novel insights into organ tropism, hantavirus-associated pathophysiology and overlapping clinical features, HFRS and HCPS are believed to be interconnected syndromes frequently involving the kidneys. As there are no specific antiviral treatments or vaccines approved in Europe or the USA, only preventive measures and public awareness may minimize the risk of hantavirus infection. Treatment remains primarily supportive and, depending on disease severity, more invasive measures (e.g., renal replacement therapy, mechanical ventilation and extracorporeal membrane oxygenation) are needed.
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Affiliation(s)
- Felix C Koehler
- Department II of Internal Medicine and Center for Molecular Medicine Cologne, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- CECAD, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Veronica Di Cristanziano
- Institute of Virology, University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | - Martin R Späth
- Department II of Internal Medicine and Center for Molecular Medicine Cologne, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- CECAD, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - K Johanna R Hoyer-Allo
- Department II of Internal Medicine and Center for Molecular Medicine Cologne, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- CECAD, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Manuel Wanken
- Department II of Internal Medicine and Center for Molecular Medicine Cologne, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Roman-Ulrich Müller
- Department II of Internal Medicine and Center for Molecular Medicine Cologne, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- CECAD, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
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Zhang R, Mao Z, Yang J, Liu S, Liu Y, Qin S, Tian H, Guo S, Ren J, Shi X, Li X, Sun J, Ling F, Wang Z. The changing epidemiology of hemorrhagic fever with renal syndrome in Southeastern China during 1963-2020: A retrospective analysis of surveillance data. PLoS Negl Trop Dis 2021; 15:e0009673. [PMID: 34358248 PMCID: PMC8372920 DOI: 10.1371/journal.pntd.0009673] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 08/18/2021] [Accepted: 07/21/2021] [Indexed: 01/18/2023] Open
Abstract
Background Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by hantavirus which was endemic Zhejiang Province, China. In this study, we aim to explore the changing epidemiology of HFRS in Zhejiang, identify high-risk areas and populations, and evaluate relevant policies and interventions to better improve HFRS control and prevention. Methods Surveillance data on HFRS during 1963–2020 in Zhejiang Province were extracted from Zhejiang Provincial Center for Disease Control and Prevention archives and the Chinese Notifiable Disease Reporting System. The changing epidemiological characteristics of HFRS including seasonal distribution, geographical distribution, and demographic features, were analyzed using joinpoint regression, autoregressive integrated moving average model, descriptive statistical methods, and Spatio-temporal cluster analysis. Results From 1963 to 2020, 114 071 HFRS cases and 1269 deaths were reported in Zhejiang Province. The incidence increased sharply from 1973 and peaked in 1986, then decreased steadily and maintained a stable incidence from 2004. HFRS cases were reported in all 11 prefecture-level cities of Zhejiang Province from 1963 to 2020. The joint region (Shengzhou, Xinchang, Tiantai, and surrounding areas), and Kaihua County are the most seriously affected regions throughout time. After 1990, the first HFRS incidence peak was in May-June, with another one from November to January. Most HFRS cases occurred in 21- (26.48%) and 30- years group (24.25%) from 1991 to 2004, but 41- (25.75%) and 51-years (23.30%) had the highest proportion from 2005 to 2020. Farmers accounted for most cases (78.10%), and cases are predominantly males with a male-to-female ratio of 2.6:1. It was found that the median time from onset to diagnosis was 6.5 days (IQR 3.75–10.42), and the time from diagnosis to disease report was significantly shortened after 2011. Conclusions We observed dynamic changes in the seasonal distribution, geographical distribution, and demographic features of HFRS, which should be well considered in the development of control and prevention strategies in future. Additional researches are warranted to elucidate the environmental, meteorological, and social factors associated with HFRS incidence in different decades. This study conducted a long-term and systematic study on the epidemiological characteristics of HFRS in Zhejiang Province from 1963 to 2020 through a combination of time and space analysis and epidemiology, aiming to analyze the distribution characteristics of HFRS and explore the high incidence of epidemics in Zhejiang Province Regional influence. From 1963 to 2020, all 11 prefecture-level cities in Zhejiang Province reported HFRS cases, and the morbidity and mortality rates decreased significantly. However, the geographical distribution of endemic areas has been expanding to eastern Zhejiang Province. Moreover, the age of high-risk groups increases over time. Although the incidence rate has declined in recent years, HFRS is still a huge threat to people’s health. As the incidence rate changes, some epidemiological characteristics have also changed. Comprehensive interventions should also be adjusted, including rodent control in endemic areas, health education, vaccination, and improved detection and diagnosis capabilities for HFRS epidemiological changes.
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Affiliation(s)
- Rong Zhang
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Zhiyuan Mao
- MPH department, college of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America
| | - Jun Yang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
| | - Shelan Liu
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Ying Liu
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Shuwen Qin
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Song Guo
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Jiangping Ren
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Xuguang Shi
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Xuan Li
- MPH department, college of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America
| | - Jimin Sun
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
- * E-mail: (JS); (FL); (ZW)
| | - Feng Ling
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
- * E-mail: (JS); (FL); (ZW)
| | - Zhen Wang
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
- * E-mail: (JS); (FL); (ZW)
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Ma T, Jiang D, Hao M, Fan P, Zhang S, Quzhen G, Xue C, Han S, Wu W, Zheng C, Ding F. Geographical Detector-based influence factors analysis for Echinococcosis prevalence in Tibet, China. PLoS Negl Trop Dis 2021; 15:e0009547. [PMID: 34252103 PMCID: PMC8297938 DOI: 10.1371/journal.pntd.0009547] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 07/22/2021] [Accepted: 06/08/2021] [Indexed: 11/19/2022] Open
Abstract
Echinococcosis, caused by genus Echinococcus, is the most pathogenic zoonotic parasitic disease in the world. In Tibet of the People's Republic of China, echinococcosis refers principally to two types of severe zoonosis, cystic echinococcosis (CE) and alveolar echinococcosis (AE), which place a serious burden on public health and economy in the local community. However, research on the spatial epidemiology of echinococcosis remains inadequate in Tibet, China. Based on the recorded human echinococcosis data, maps of the spatial distribution of human CE and AE prevalence in Tibet were produced at city level and county level respectively, which show that the prevalence of echinococcosis in northern and western Tibet was much higher than that in other regions. We employ a geographical detector to explore the influencing factors for causing CE and AE while sorting information on the maps of disease prevalence and environment factors (e.g. terrain, population, and yak population). The results of our analysis showed that biological factors have the most impact on the prevalence of echinococcosis, of which the yak population contributes the most for CE, while the dog population contributes the most for AE. In addition, the interaction between various factors, as we found out, might further explain the disease prevalence, which indicated that the echinococcosis prevalence is not simply affected by one single factor, but by multiple factors that are correlated with each other complicatedly. Our results will provide an important reference for the evaluation of the echinococcosis risk, control projects, and prevention programs in Tibet.
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Affiliation(s)
- Tian Ma
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Dong Jiang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Natural Resources of the People’s Republic of China, Beijing, China
| | - Mengmeng Hao
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Peiwei Fan
- Department of Geological Engineering and Environment, China University of Mining and Technology, Beijing, China
| | - Shize Zhang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
| | - Gongsang Quzhen
- Tibet Autonomous Region Center for Diseases Control and Prevention, Lhasa, Tibet Autonomous Region, China
- National Health Council Key Laboratory of Echinococcosis Prevention and Control, Lhasa, Tibet Autonomous Region, China
| | - ChuiZhao Xue
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Chinese Center for Tropical Diseases Research, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Key Laboratory of Parasite and Vector Biology, MOH, Shanghai, China
| | - Shuai Han
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Chinese Center for Tropical Diseases Research, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Key Laboratory of Parasite and Vector Biology, MOH, Shanghai, China
| | - WeiPing Wu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Chinese Center for Tropical Diseases Research, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Key Laboratory of Parasite and Vector Biology, MOH, Shanghai, China
| | - Canjun Zheng
- Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Fangyu Ding
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
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Time series models for prediction of leptospirosis in different climate zones in Sri Lanka. PLoS One 2021; 16:e0248032. [PMID: 33989292 PMCID: PMC8121312 DOI: 10.1371/journal.pone.0248032] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 04/26/2021] [Indexed: 12/28/2022] Open
Abstract
In tropical countries such as Sri Lanka, where leptospirosis—a deadly disease with a high mortality rate—is endemic, prediction is required for public health planning and resource allocation. Routinely collected meteorological data may offer an effective means of making such predictions. This study included monthly leptospirosis and meteorological data from January 2007 to April 2019 from Sri Lanka. Factor analysis was first used with rainfall data to classify districts into meteorological zones. We used a seasonal autoregressive integrated moving average (SARIMA) model for univariate predictions and an autoregressive distributed lag (ARDL) model for multivariable analysis of leptospirosis with monthly average rainfall, temperature, relative humidity (RH), solar radiation (SR), and the number of rainy days/month (RD). Districts were classified into wet (WZ) and dry (DZ) zones, and highlands (HL) based on the factor analysis of rainfall data. The WZ had the highest leptospirosis incidence; there was no difference in the incidence between the DZ and HL. Leptospirosis was fluctuated positively with rainfall, RH and RD, whereas temperature and SR were fluctuated negatively. The best-fitted SARIMA models in the three zones were different from each other. Despite its known association, rainfall was positively significant in the WZ only at lag 5 (P = 0.03) but was negatively associated at lag 2 and 3 (P = 0.04). RD was positively associated for all three zones. Temperature was positively associated at lag 0 for the WZ and HL (P < 0.009) and was negatively associated at lag 1 for the WZ (P = 0.01). There was no association with RH in contrast to previous studies. Based on altitude and rainfall data, meteorological variables could effectively predict the incidence of leptospirosis with different models for different climatic zones. These predictive models could be effectively used in public health planning purposes.
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13
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Changing epidemiology of hemorrhagic fever with renal syndrome in Jiangsu Province, China, 1963–2017. J Public Health (Oxf) 2021. [DOI: 10.1007/s10389-021-01526-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Prediction of hot spot areas of hemorrhagic fever with renal syndrome in Hunan Province based on an information quantity model and logistical regression model. PLoS Negl Trop Dis 2020; 14:e0008939. [PMID: 33347438 PMCID: PMC7785239 DOI: 10.1371/journal.pntd.0008939] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 01/05/2021] [Accepted: 10/27/2020] [Indexed: 11/19/2022] Open
Abstract
Background China’s “13th 5-Year Plan” (2016–2020) for the prevention and control of sudden acute infectious diseases emphasizes that epidemic monitoring and epidemic focus surveys in key areas are crucial for strengthening national epidemic prevention and building control capacity. Establishing an epidemic hot spot areas and prediction model is an effective means of accurate epidemic monitoring and surveying. Objective: This study predicted hemorrhagic fever with renal syndrome (HFRS) epidemic hot spot areas, based on multi-source environmental variable factors. We calculated the contribution weight of each environmental factor to the morbidity risk, obtained the spatial probability distribution of HFRS risk areas within the study region, and detected and extracted epidemic hot spots, to guide accurate epidemic monitoring as well as prevention and control. Methods: We collected spatial HFRS data, as well as data on various types of natural and human social activity environments in Hunan Province from 2010 to 2014. Using the information quantity method and logistic regression modeling, we constructed a risk-area-prediction model reflecting the epidemic intensity and spatial distribution of HFRS. Results: The areas under the receiver operating characteristic curve of training samples and test samples were 0.840 and 0.816. From 2015 to 2019, HRFS case site verification showed that more than 82% of the cases occurred in high-risk areas. Discussion This research method could accurately predict HFRS hot spot areas and provided an evaluation model for Hunan Province. Therefore, this method could accurately detect HFRS epidemic high-risk areas, and effectively guide epidemic monitoring and surveyance. Hunan, the main epidemic area of HRFS in China. Hunan has had a cumulative incidence of 117,000 cases since 1963. During this time Hunan experienced two high incidence periods in the 1980s and 1990s. We used an Information quantity + Logistic regression model (I+LR model) to predict high-incidence and potential epidemic HFRS areas. Normalized difference vegetation index(NDVI)contributed most to HFRS risk. Per capita GDP, population size, land-use type, rainfall, elevation, and soil type were all factors found to influence HFRS risk. Our study is useful for risk prediction, prevention, and control of HFRS.
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Song S, Yao H, Yang Z, He Z, Shao Z, Liu K. Epidemic Changes and Spatio-Temporal Analysis of Japanese Encephalitis in Shaanxi Province, China, 2005-2018. Front Public Health 2020; 8:380. [PMID: 32850600 PMCID: PMC7426712 DOI: 10.3389/fpubh.2020.00380] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 06/30/2020] [Indexed: 12/25/2022] Open
Abstract
Japanese encephalitis (JE) is a mosquito-borne viral disease, which is the most serious viral encephalitis in China and other countries of the Asia-Pacific region. Since 2005, the epidemic patterns of JE have changed dramatically in China because of the vaccination of children younger than 15 years old, and JE is expanding geographically along with global warming. This retrospective epidemiological study analyzed dynamic environmental factors and the spatio-temporal distribution of human cases of JE in Shaanxi Province—one of the most severely affected areas of China—from 2005 to 2018. The results demonstrated that the high-risk population changed rapidly as the annual rate of JE cases increased by more than 40% in the age group >60 years during the study period, and endemic areas expanded northward in Shaanxi. Hotspot analysis detected four hotspots accounting for 52.38% the total cases, and the panel negative binomial regression model revealed that the spatio-temporal distribution of JE was significantly affected by temperature, relative humidity, wind velocity, El Niño-Southern Oscillation, coniferous forest coverage, and urban areas. These findings can provide useful information for improving current strategies and measures to reduce disease incidence.
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Affiliation(s)
- Shuxuan Song
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Hongwu Yao
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Zurong Yang
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China.,Centre for Disease Prevent and Control in Northern Theater Command, Shenyang, China
| | - Zhen He
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Zhongjun Shao
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Kun Liu
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
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Shi F, Yu C, Yang L, Li F, Lun J, Gao W, Xu Y, Xiao Y, Shankara SB, Zheng Q, Zhang B, Wang S. Exploring the Dynamics of Hemorrhagic Fever with Renal Syndrome Incidence in East China Through Seasonal Autoregressive Integrated Moving Average Models. Infect Drug Resist 2020; 13:2465-2475. [PMID: 32801786 PMCID: PMC7383097 DOI: 10.2147/idr.s250038] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 07/05/2020] [Indexed: 01/18/2023] Open
Abstract
Objective The purpose of this study was to explore the dynamics of incidence of hemorrhagic fever with renal syndrome (HFRS) from 2000 to 2017 in Anqiu City, a city located in East China, and find the potential factors leading to the incidence of HFRS. Methods Monthly reported cases of HFRS and climatic data from 2000 to 2017 in the city were obtained. Seasonal autoregressive integrated moving average (SARIMA) models were used to fit the HFRS incidence and predict the epidemic trend in Anqiu City. Univariate and multivariate generalized additive models were fit to identify and characterize the association between the HFRS incidence and meteorological factors during the study period. Results Statistical analysis results indicate that the annualized average incidence at the town level ranged from 1.68 to 6.31 per 100,000 population among 14 towns in the city, and the western towns exhibit high endemic levels during the study periods. With high validity, the optimal SARIMA(0,1,1,)(0,1,1)12 model may be used to predict the HFRS incidence. Multivariate generalized additive model (GAM) results show that the HFRS incidence increases as sunshine time and humidity increases and decreases as precipitation increases. In addition, the HFRS incidence is associated with temperature, precipitation, atmospheric pressure, and wind speed. Those are identified as the key climatic factors contributing to the transmission of HFRS. Conclusion This study provides evidence that the SARIMA models can be used to characterize the fluctuations in HFRS incidence. Our findings add to the knowledge of the role played by climate factors in HFRS transmission and can assist local health authorities in the development and refinement of a better strategy to prevent HFRS transmission.
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Affiliation(s)
- Fuyan Shi
- Department of Health Statistics, School of Public Health and Management, Weifang Medical University, Weifang, Shandong, People's Republic of China
| | - Changlan Yu
- Anqiu City Center for Disease Control and Prevention, Anqiu, Shandong, People's Republic of China
| | - Liping Yang
- Health and Medical Center, Xijing Hospital, Air Force Military Medical University, Xi'an, Shannxi, People's Republic of China
| | - Fangyou Li
- Anqiu City Center for Disease Control and Prevention, Anqiu, Shandong, People's Republic of China
| | - Jiangtao Lun
- Anqiu Meteorological Bureau, Anqiu, Shandong, People's Republic of China
| | - Wenfeng Gao
- Department of Immunology and Rheumatology, Affiliated Hospital of Weifang Medical University, Weifang, Shandong, People's Republic of China
| | - Yongyong Xu
- Department of Health Statistics, School of Military Preventive Medicine, Air Force Military Medical University, Xi'an, Shannxi, People's Republic of China
| | - Yufei Xiao
- Department of Health Statistics, School of Public Health and Management, Weifang Medical University, Weifang, Shandong, People's Republic of China
| | - Sravya B Shankara
- Program in Health: Science, Society, and Policy, Brandeis University, Waltham, MA, USA
| | - Qingfeng Zheng
- Institute for Hospital Management of Tsinghua University, Tsinghua Campus, Shenzhen, People's Republic of China
| | - Bo Zhang
- Department of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Suzhen Wang
- Department of Health Statistics, School of Public Health and Management, Weifang Medical University, Weifang, Shandong, People's Republic of China
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Wang Y, Xu C, Wu W, Ren J, Li Y, Gui L, Yao S. Time series analysis of temporal trends in hemorrhagic fever with renal syndrome morbidity rate in China from 2005 to 2019. Sci Rep 2020; 10:9609. [PMID: 32541833 PMCID: PMC7295973 DOI: 10.1038/s41598-020-66758-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 05/26/2020] [Indexed: 12/04/2022] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is seriously endemic in China with 70%~90% of the notified cases worldwide and showing an epidemic tendency of upturn in recent years. Early detection for its future epidemic trends plays a pivotal role in combating this threat. In this scenario, our study investigates the suitability for application in analyzing and forecasting the epidemic tendencies based on the monthly HFRS morbidity data from 2005 through 2019 using the nonlinear model-based self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) methods. The experimental results manifested that the SETAR and LSTAR approaches presented smaller values among the performance measures in both two forecasting subsamples, when compared with the most extensively used seasonal autoregressive integrated moving average (SARIMA) method, and the former slightly outperformed the latter. Descriptive statistics showed an epidemic tendency of downturn with average annual percent change (AAPC) of −5.640% in overall HFRS, however, an upward trend with an AAPC = 1.213% was observed since 2016 and according to the forecasts using the SETAR, it would seemingly experience an outbreak of HFRS in China in December 2019. Remarkably, there were dual-peak patterns in HFRS incidence with a strong one occurring in November until January of the following year, additionally, a weak one in May and June annually. Therefore, the SETAR and LSTAR approaches may be a potential useful tool in analyzing the temporal behaviors of HFRS in China.
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Affiliation(s)
- Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, P.R. China.
| | - Chunjie Xu
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, P.R. China
| | - Weidong Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, P.R. China
| | - Jingchao Ren
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, P.R. China
| | - Yuchun Li
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, P.R. China
| | - Lihui Gui
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, P.R. China
| | - Sanqiao Yao
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, P.R. China
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Li Y, Cazelles B, Yang G, Laine M, Huang ZXY, Cai J, Tan H, Stenseth NC, Tian H. Intrinsic and extrinsic drivers of transmission dynamics of hemorrhagic fever with renal syndrome caused by Seoul hantavirus. PLoS Negl Trop Dis 2019; 13:e0007757. [PMID: 31545808 PMCID: PMC6776365 DOI: 10.1371/journal.pntd.0007757] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 10/03/2019] [Accepted: 09/06/2019] [Indexed: 11/19/2022] Open
Abstract
Seoul hantavirus (SEOV) has recently raised concern by causing geographic range expansion of hemorrhagic fever with renal syndrome (HFRS). SEOV infections in humans are significantly underestimated worldwide and epidemic dynamics of SEOV-related HFRS are poorly understood because of a lack of field data and empirically validated models. Here, we use mathematical models to examine both intrinsic and extrinsic drivers of disease transmission from animal (the Norway rat) to humans in a SEOV-endemic area in China. We found that rat eradication schemes and vaccination campaigns, but below the local elimination threshold, could diminish the amplitude of the HFRS epidemic but did not modify its seasonality. Models demonstrate population dynamics of the rodent host were insensitive to climate variations in urban settings, while relative humidity had a negative effect on the seasonality in transmission. Our study contributes to a better understanding of the epidemiology of SEOV-related HFRS, demonstrates asynchronies between rodent population dynamics and transmission rate, and identifies potential drivers of the SEOV seasonality. Seoul hantavirus (SEOV) infections are common in Europe and Asia where a considerably high seroprevalence among the population is found. However, only relatively few hemorrhagic fever with renal syndrome (HFRS) cases are reported. Comprehensive epidemiological data is necessary to study the patterns and drivers of this underestimated disease. Here, we analyzed rodent host surveillance and seroprevalence data from 1998 to 2015 for disease outbreaks in Huludao City, one of the typical SEOV-endemic areas for HFRS in China. Our mathematical models quantified the drivers on HFRS transmission and estimated the epidemiological parameters. Our study provides an understanding of its ecological process between intrinsic and extrinsic factors, human-rodent interface and disease dynamics.
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Affiliation(s)
- Yidan Li
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Bernard Cazelles
- IBENS, UMR 8197 CNRS-ENS Ecole Normale Supérieure, Paris, France
- International Center for Mathematical and Computational Modeling of Complex Systems (UMMISCO), IRD-Sorbonne Université, Bondy, France
| | - Guoqing Yang
- Huludao Municipal Center for Disease Control and Prevention, Huludao, Liaoning, China
| | - Marko Laine
- Finnish Meteorological Institute, Helsinki, Finland
| | | | - Jun Cai
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, China
| | - Hua Tan
- School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Nils Chr. Stenseth
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Blindern, Oslo, Norway
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
- * E-mail: (NCS); (HT)
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
- * E-mail: (NCS); (HT)
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Zhao Y, Ge L, Liu J, Liu H, Yu L, Wang N, Zhou Y, Ding X. Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space-time cube-based approach. J Int Med Res 2019; 47:3371-3388. [PMID: 31144552 PMCID: PMC6683916 DOI: 10.1177/0300060519850734] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Objective Hemorrhagic fever with renal syndrome (HFRS), a natural–focal infectious disease caused by hantaviruses, resulted in 37 deaths between 2011 and 2015 in Hubei Province, China. HFRS outbreaks are seasonally distributed, exhibiting heterogeneity in space and time. We aimed to identify the spatial and temporal characteristics of HFRS epidemics and their probable influencing factors. Methods We used the space–time cube (STC) method to investigate HFRS epidemics in different space–time locations. STC can be used to visualize the trajectories of moving objects (or changing tendencies) in space and time in three dimensions. We applied space–time statistical methods, including space–time hot spot and space–time local outlier analyses, based on a calculated STC model of HFRS cases, to identify spatial and temporal hotspots and outlier distributions. We used the space–time gravity center method to reveal associations between possible factors and HFRS epidemics. Results In this research, HFRS cases for each space–time location were defined by the STC model, which can present the dynamic characteristics of HFRS epidemics. The STC model delivered accurate and detailed results for the spatiotemporal patterns of HFRS epidemics. Conclusion The methods in this paper can potentially be applied for infectious diseases with similar spatial and temporal patterns.
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Affiliation(s)
- Youlin Zhao
- 1 Business School of Hohai University, Nanjing city, Jiangsu Province, China
| | - Liang Ge
- 2 Tianjin Institute of Surveying and Mapping, Liqizhuang, Tianjin, China
| | - Junwei Liu
- 2 Tianjin Institute of Surveying and Mapping, Liqizhuang, Tianjin, China
| | - Honghui Liu
- 3 Hubei Provincial Centre for Disease Control and Prevention, Wuhan, China
| | - Lei Yu
- 2 Tianjin Institute of Surveying and Mapping, Liqizhuang, Tianjin, China
| | - Ning Wang
- 4 First Crust Deformation Monitoring and Application Center, China Earthquake Administration, Tianjin, China
| | - Yijun Zhou
- 2 Tianjin Institute of Surveying and Mapping, Liqizhuang, Tianjin, China
| | - Xu Ding
- 2 Tianjin Institute of Surveying and Mapping, Liqizhuang, Tianjin, China
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Zheng Z, Wang P, Wang Z, Zhang D, Wang X, Zuo S, Li X. The characteristics of current natural foci of hemorrhagic fever with renal syndrome in Shandong Province, China, 2012-2015. PLoS Negl Trop Dis 2019; 13:e0007148. [PMID: 31107874 PMCID: PMC6544330 DOI: 10.1371/journal.pntd.0007148] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 05/31/2019] [Accepted: 05/02/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Hemorrhagic fever with renal syndrome (HFRS), an infectious disease caused by hantaviruses, is endemic in China and remains a serious public health problem. Historically, Shandong Province has had the largest HFRS burden in China. However, we do not have a comprehensive and clear understanding of the current epidemic foci of HFRS in Shandong Province. METHODOLOGY/PRINCIPAL FINDINGS The incidence and mortality rates were calculated, and a phylogenetic analysis was performed after laboratory testing of the virus in rodents. Spatial epidemiology analysis was applied to investigate the epidemic foci, including their sources. A total of 6,206 HFRS cases and 59 related deaths were reported in Shandong Province. The virus carriage rates of the rodents Rattus norvegicus, Apodemus agrarius and Mus musculus were 10.24%, 6.31% and 0.27%, respectively. The phylogenetic analysis indicated that two novel viruses obtained from R. norvegicus in Anqiu City and Qingzhou City were dissimilar to the other strains, but closely related to strains previously isolated in northeastern China. Three epidemic foci were defined, two of which were derived from the Jining and Linyi epidemic foci, respectively, while the other was the residue of the Jining epidemic focus. CONCLUSIONS/SIGNIFICANCE The southeastern and central Shandong Province are current key HFRS epidemic foci dominated by A. agrarius and R. norvegicus, respectively. Our study could help local departments to strengthen prevention and control measures in key areas to reduce the hazards of HFRS.
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Affiliation(s)
- Zhaolei Zheng
- School of Public Health, Shandong University, Jinan, Shandong Province, China
| | - Peizhu Wang
- School of Public Health, Shandong University, Jinan, Shandong Province, China
| | - Zhiqiang Wang
- Institute of Infectious Disease Control and Prevention, Shandong Provincial Center for Disease Control and Prevention, Jinan, Shandong Province, China
| | - Dandan Zhang
- School of Public Health, Shandong University, Jinan, Shandong Province, China
| | - Xu Wang
- School of Public Health, Shandong University, Jinan, Shandong Province, China
| | - Shuqing Zuo
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Xiujun Li
- School of Public Health, Shandong University, Jinan, Shandong Province, China
- * E-mail:
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21
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Zhao Q, Yang X, Liu H, Hu Y, He M, Huang B, Yao L, Li N, Zhou G, Yin Y, Li M, Gong P, Liu M, Ma J, Ren Z, Wang Q, Xiong W, Fan X, Guo X, Zhang X. Effects of climate factors on hemorrhagic fever with renal syndrome in Changchun, 2013 to 2017. Medicine (Baltimore) 2019; 98:e14640. [PMID: 30817583 PMCID: PMC6831229 DOI: 10.1097/md.0000000000014640] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by hantaviruses (HVs). Climate factors have a significant impact on the transmission of HFRS. Here, we characterized the dynamic temporal trend of HFRS and identified the roles of climate factors in its transmission in Changchun, China.Surveillance data of HFRS cases and data on related environmental variables from 2013 to 2017 were collected. A principal components regression (PCR) model was used to quantify the relationship between climate factors and transmission of HFRS.During 2013 to 2017, a distinctly declining temporal trend of annual HFRS incidence was identified. Four principal components were extracted, with a cumulative contribution rate of 89.282%. The association between HFRS epidemics and climate factors was better explained by the PCR model (F = 10.050, P <.001, adjusted R = 0.456) than by the general multiple regression model (F = 2.748, P <.005, adjusted R = 0.397).The monthly trends of HFRS were positively correlated with the mean wind velocity but negatively correlated with the mean temperature, relative humidity, sunshine duration, and accumulative precipitation of the different previous months. The study results may be useful for the development of HFRS preventive initiatives that are customized for Changchun regarding specific climate environments.
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Affiliation(s)
- Qinglong Zhao
- Jilin Provincial Center for Disease Control and Prevention
| | - Xiaodi Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University
| | - Hongjian Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University
| | | | - Minfu He
- Department of Social Medicine and Health Management, School of Public Health, Jilin University
| | - Biao Huang
- Jilin Provincial Center for Disease Control and Prevention
| | - Laishun Yao
- Jilin Provincial Center for Disease Control and Prevention
| | - Na Li
- Jilin Provincial Center for Disease Control and Prevention
| | - Ge Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University
| | - Yuan Yin
- Changchun Center for Disease Control and Preventiona
| | - Meina Li
- The First Hospital of Jilin University, Changchun, China
| | - Ping Gong
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University
| | - Meitian Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University
| | - Juan Ma
- Department of Social Medicine and Health Management, School of Public Health, Jilin University
| | - Zheng Ren
- Department of Social Medicine and Health Management, School of Public Health, Jilin University
| | - Qi Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University
| | - Wenjing Xiong
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University
| | - Xinwen Fan
- Department of Social Medicine and Health Management, School of Public Health, Jilin University
| | - Xia Guo
- Department of Social Medicine and Health Management, School of Public Health, Jilin University
| | - Xiumin Zhang
- Department of Social Medicine and Health Management, School of Public Health, Jilin University
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22
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Tian H, Stenseth NC. The ecological dynamics of hantavirus diseases: From environmental variability to disease prevention largely based on data from China. PLoS Negl Trop Dis 2019; 13:e0006901. [PMID: 30789905 PMCID: PMC6383869 DOI: 10.1371/journal.pntd.0006901] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Hantaviruses can cause hantavirus pulmonary syndrome (HPS) in the Americas and hemorrhagic fever with renal syndrome (HFRS) in Eurasia. In recent decades, repeated outbreaks of hantavirus disease have led to public concern and have created a global public health burden. Hantavirus spillover from natural hosts into human populations could be considered an ecological process, in which environmental forces, behavioral determinants of exposure, and dynamics at the human–animal interface affect human susceptibility and the epidemiology of the disease. In this review, we summarize the progress made in understanding hantavirus epidemiology and rodent reservoir population biology. We mainly focus on three species of rodent hosts with longitudinal studies of sufficient scale: the striped field mouse (Apodemus agrarius, the main reservoir host for Hantaan virus [HTNV], which causes HFRS) in Asia, the deer mouse (Peromyscus maniculatus, the main reservoir host for Sin Nombre virus [SNV], which causes HPS) in North America, and the bank vole (Myodes glareolus, the main reservoir host for Puumala virus [PUUV], which causes HFRS) in Europe. Moreover, we discuss the influence of ecological factors on human hantavirus disease outbreaks and provide an overview of research perspectives.
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Affiliation(s)
- Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
- * E-mail: (HT); (NCS)
| | - Nils Chr. Stenseth
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Blindern, Oslo, Norway
- Department of Earth System Science, Tsinghua University, Beijing, China
- * E-mail: (HT); (NCS)
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23
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Godsmark CN, Irlam J, van der Merwe F, New M, Rother HA. Priority focus areas for a sub-national response to climate change and health: A South African provincial case study. ENVIRONMENT INTERNATIONAL 2019; 122:31-51. [PMID: 30573189 DOI: 10.1016/j.envint.2018.11.035] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 10/26/2018] [Accepted: 11/14/2018] [Indexed: 06/09/2023]
Abstract
INTRODUCTION The intersection of health and climate change is often absent or under-represented in sub-national government strategies. This analysis of the literature, using a new methodological framework, highlights priority focus areas for a sub-national government response to health and climate change, using the Western Cape (WC) province of South Africa as a case study. METHODS A methodological framework was created to conduct a review of priority focus areas relevant for sub-national governments. The framework encompassed the establishment of a Project Steering Group consisting of relevant, sub-national stakeholders (e.g. provincial officials, public and environmental health specialists and academics); an analysis of local climatic projections as well as an analysis of global, national and sub-national health risk factors and impacts. RESULTS Globally, the discussion of health and climate change adaptation strategies in sub-national, or provincial government is often limited. For the case study presented, multiple health risk factors were identified. WC climatic projections include a warmer and potentially drier future with an increased frequency and intensity of extreme weather events. WC government priority focus areas requiring further research on health risk factors include: population migration and environmental refugees, land use change, violence and human conflict and vulnerable groups. WC government priority focus areas for further research on health impacts include: mental ill-health, non-communicable diseases, injuries, poisonings (e.g. pesticides), food and nutrition insecurity-related diseases, water- and food-borne diseases and reproductive health. These areas are currently under-addressed, or not addressed at all, in the current provincial climate change strategy. CONCLUSIONS Sub-national government adaptation strategies often display limited discussion on the health and climate change intersect. The methodological framework presented in this case study can be globally utilized by other sub-national governments for decision-making and development of climate change and health adaptation strategies. Additionally, due to the broad range of sectoral issues identified, a primary recommendation from this study is that sub-national governments internationally should consider a "health and climate change in all policies" approach when developing adaptation and mitigation strategies to address climate change.
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Affiliation(s)
- Christie Nicole Godsmark
- Division of Environmental Health, School of Public Health and Family Medicine, University of Cape Town, South Africa
| | - James Irlam
- Division of Environmental Health, School of Public Health and Family Medicine, University of Cape Town, South Africa; Primary Health Care Directorate, University of Cape Town, South Africa
| | - Frances van der Merwe
- Department of Environmental Affairs and Development Planning, Western Cape Government, South Africa
| | - Mark New
- African Climate and Development Initiative, University of Cape Town, Cape Town, South Africa; School of International Development, University of East Anglia, Norwich, UK
| | - Hanna-Andrea Rother
- Division of Environmental Health, School of Public Health and Family Medicine, University of Cape Town, South Africa.
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24
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Zhao Y, Ge L, Zhou Y, Sun Z, Zheng E, Wang X, Huang Y, Cheng H. A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS). PLoS One 2018; 13:e0207518. [PMID: 30475830 PMCID: PMC6261020 DOI: 10.1371/journal.pone.0207518] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2018] [Accepted: 11/01/2018] [Indexed: 02/04/2023] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is a naturally-occurring, fecally transmitted disease caused by a Hantavirus (HV). It is extremely damaging to human health and results in many deaths annually, especially in Hubei Province, China. One of the primary characteristics of HFRS is the spatiotemporal heterogeneity of its occurrence, with notable seasonal differences. In view of this heterogeneity, the present study suggests that there is a need to focus on trend simulation and the spatiotemporal prediction of HFRS outbreaks. To facilitate this, we constructed a new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model. The SD-STARIMA model is based on the spatial and temporal characteristics of the Space-Time Autoregressive Integrated Moving Average (STARMA) model first developed by Cliff and Ord in 1974, which has proven useful in modelling the temporal aspects of spatially located data. This model can simulate the trends in HFRS epidemics, taking into consideration both spatial and temporal variations. The SD-STARIMA model is also able to make seasonal difference calculations to eliminate temporally non-stationary problems that are present in the HFRS data. Experiments have demonstrated that the proposed SD-STARIMA model offers notably better prediction accuracy, especially for spatiotemporal series data with seasonal distribution characteristics.
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Affiliation(s)
- Youlin Zhao
- Business School of Hohai University, Nanjing city, Jiangsu Province, PR China
- * E-mail: (YZ); (LG)
| | - Liang Ge
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
- * E-mail: (YZ); (LG)
| | - Yijun Zhou
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
| | - Zhongfang Sun
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
| | - Erlong Zheng
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
| | - Xingmeng Wang
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
| | - Yongchun Huang
- Business School of Hohai University, Nanjing city, Jiangsu Province, PR China
| | - Huiping Cheng
- School of Economics and Management, Hubei University of Technology, Wuhan,Hubei Province, PR China
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Zuo SQ, Li XJ, Wang ZQ, Jiang JF, Fang LQ, Zhang WH, Zhang JS, Zhao QM, Cao WC. Genetic Diversity and the Spatio-Temporal Analyses of Hantaviruses in Shandong Province, China. Front Microbiol 2018; 9:2771. [PMID: 30524397 PMCID: PMC6257036 DOI: 10.3389/fmicb.2018.02771] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 10/29/2018] [Indexed: 11/22/2022] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is a serious public health problem in Shandong Province, China. We conducted an epizootiologic investigation and phylogeographic and phylodynamic analyses to infer the phylogenetic relationships of hantaviruses in space and time, and gain further insights into their evolutionary dynamics in Shandong Province. Our data indicated that the Seoul virus (SEOV) is distributed throughout Shandong, whereas Hantaan virus (HTNV) co-circulates with SEOV in the eastern and southern areas of Shandong. Their distribution showed strong geographic clustering. In addition, our analyses indicated multiple evolutionary paths, long-distance transmission, and demographic expansion events for SEOV in some areas. Selection pressure analyses revealed that negative selection on hantaviruses acted as the principal evolutionary force, whereas a little evidence of positive selection exists. We found that several positively selected sites were located within major functional regions and indicated the importance of these residues for adaptive evolution of hantaviruses.
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Affiliation(s)
- Shu-Qing Zuo
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Xiu-Jun Li
- Department of Biostatistics, School of Public Health, Shandong University, Jinan, China
| | - Zhi-Qiang Wang
- Shandong Center for Disease Control and Prevention, Jinan, China
| | - Jia-Fu Jiang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Li-Qun Fang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Wen-Hui Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Jiu-Song Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Qiu-Min Zhao
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Wu-Chun Cao
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
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26
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Xiao H, Tong X, Gao L, Hu S, Tan H, Huang ZYX, Zhang G, Yang Q, Li X, Huang R, Tong S, Tian H. Spatial heterogeneity of hemorrhagic fever with renal syndrome is driven by environmental factors and rodent community composition. PLoS Negl Trop Dis 2018; 12:e0006881. [PMID: 30356291 PMCID: PMC6218101 DOI: 10.1371/journal.pntd.0006881] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Revised: 11/05/2018] [Accepted: 09/29/2018] [Indexed: 12/25/2022] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused mainly by two hantaviruses in China: Hantaan virus and Seoul virus. Environmental factors can significantly affect the risk of contracting hantavirus infections, primarily through their effects on rodent population dynamics and human-rodent contact. We aimed to clarify the environmental risk factors favoring rodent-to-human transmission to provide scientific evidence for developing effective HFRS prevention and control strategies. The 10-year (2006-2015) field surveillance data from the rodent hosts for hantavirus, the epidemiological and environmental data extracted from satellite images and meteorological stations, rodent-to-human transmission rates and impacts of the environment on rodent community composition were used to quantify the relationships among environmental factors, rodent species and HFRS occurrence. The study included 709 cases of HFRS. Rodent species in Chenzhou, a hantavirus hotspot, comprise mainly Rattus norvegicus, Mus musculus, R. flavipectus and some other species (R. losea and Microtus fortis calamorum). The rodent species played different roles across the various land types we examined, but all of them were associated with transmission risks. Some species were associated with HFRS occurrence risk in forest and water bodies. R. norvegicus and R. flavipectus were associated with risk of HFRS incidence in grassland, whereas M. musculus and R. flavipectus were associated with this risk in built-on land. The rodent community composition was also associated with environmental variability. The predictive risk models based on these significant factors were validated by a good-fit model, where: cultivated land was predicted to represent the highest risk for HFRS incidence, which accords with the statistics for HFRS cases in 2014-2015. The spatial heterogeneity of HFRS disease may be influenced by rodent community composition, which is associated with local environmental conditions. Therefore, future work should focus on preventing HFRS is moist, warm environments.
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Affiliation(s)
- Hong Xiao
- College of Resources and Environmental Sciences, Hunan Normal University, Changsha, Hunan Province, China
- Key Laboratory of Geospatial Big Data Mining and Application, Changsha, Hunan Province, China
| | - Xin Tong
- College of Resources and Environmental Sciences, Hunan Normal University, Changsha, Hunan Province, China
- Key Laboratory of Geospatial Big Data Mining and Application, Changsha, Hunan Province, China
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Lidong Gao
- Hunan Provincial Center for Disease Control and Prevention, Changsha, Hunan Province, China
| | - Shixiong Hu
- Hunan Provincial Center for Disease Control and Prevention, Changsha, Hunan Province, China
| | - Hua Tan
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Zheng Y. X. Huang
- College of Life Sciences, Nanjing Normal University, Nanjing, Jiangsu Province, China
| | - Guogang Zhang
- Key Laboratory of Forest Protection of State Forestry Administration, National Bird Banding Center of China, Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, China
| | - Qiqi Yang
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Xinyao Li
- College of Resources and Environmental Sciences, Hunan Normal University, Changsha, Hunan Province, China
- Key Laboratory of Geospatial Big Data Mining and Application, Changsha, Hunan Province, China
| | - Ru Huang
- College of Resources and Environmental Sciences, Hunan Normal University, Changsha, Hunan Province, China
- Key Laboratory of Geospatial Big Data Mining and Application, Changsha, Hunan Province, China
| | - Shilu Tong
- Shanghai Children’s Medical Center, Shanghai Jiao Tong University, Shanghai, China
- School of Public Health and Institute of Environment and Population Health, Anhui Medical University, Hefei, Anhui Province, China
- School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Queensland, Australia
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
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Sun L, Zou LX. Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China. Epidemiol Infect 2018; 146:1680-1688. [PMID: 30078384 PMCID: PMC9507955 DOI: 10.1017/s0950268818002030] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 05/10/2018] [Accepted: 06/28/2018] [Indexed: 11/07/2022] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) caused by hantaviruses is a serious public health problem in China, accounting for 90% of HFRS cases reported globally. In this study, we applied geographical information system (GIS), spatial autocorrelation analyses and a seasonal autoregressive-integrated moving average (SARIMA) model to describe and predict HFRS epidemic with the objective of monitoring and forecasting HFRS in mainland China. Chinese HFRS data from 2004 to 2016 were obtained from National Infectious Diseases Reporting System (NIDRS) database and Chinese Centre for Disease Control and Prevention (CDC). GIS maps were produced to detect the spatial distribution of HFRS cases. The Moran's I was adopted in spatial global autocorrelation analysis to identify the integral spatiotemporal pattern of HFRS outbreaks, while the local Moran's Ii was performed to identify 'hotspot' regions of HFRS at province level. A fittest SARIMA model was developed to forecast HFRS incidence in the year 2016, which was selected by Akaike information criterion and Ljung-Box test. During 2004-2015, a total of 165 710 HFRS cases were reported with the average annual incidence at province level ranged from 0 to 13.05 per 100 000 persons. Global Moran's I analysis showed that the HFRS outbreaks presented spatially clustered distribution, with the degree of cluster gradually decreasing from 2004 to 2009, then turned out to be randomly distributed and reached lowest point in 2012. Local Moran's Ii identified that four provinces in northeast China contributed to a 'high-high' cluster as a traditional epidemic centre, and Shaanxi became another HFRS 'hotspot' region since 2011. The monthly incidence of HFRS decreased sharply from 2004 to 2009 in mainland China, then increased markedly from 2010 to 2012, and decreased again since 2013, with obvious seasonal fluctuations. The SARIMA ((0,1,3) × (1,0,1)12) model was the most fittest forecasting model for the dataset of HFRS in mainland China. The spatiotemporal distribution of HFRS in mainland China varied in recent years; together with the SARIMA forecasting model, this study provided several potential decision supportive tools for the control and risk-management plan of HFRS in China.
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Affiliation(s)
- Ling Sun
- Department of Nephrology, Xuzhou Central Hospital, Medical College of Southeast University, Xuzhou, Jiangsu, China
| | - Lu-Xi Zou
- School of Management, Zhejiang University, Hangzhou, Zhejiang, China
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28
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Using a distributed lag non-linear model to identify impact of temperature variables on haemorrhagic fever with renal syndrome in Shandong Province. Epidemiol Infect 2018; 146:1671-1679. [PMID: 29976265 DOI: 10.1017/s095026881800184x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Haemorrhagic fever with renal syndrome (HFRS) is transmitted to humans mainly by rodents and this transmission could be easily influenced by meteorological factors. Given the long-term changes in climate associated with global climate change, it is important to better identify the effects of meteorological factors of HFRS in epidemic areas. Shandong province is one of the most seriously suffered provinces of HFRS in China. Daily HFRS data and meteorological data from 2007 to 2012 in Shandong province were applied. Quasi-Poisson regression with the distributed lag non-linear model was used to estimate the influences of mean temperature and Diurnal temperature range (DTR) on HFRS by sex, adjusting for the effects of relative humidity, precipitation, day-of-the-week, long-term trends and seasonality. A total of 6707 HFRS cases were reported in our study. The two peaks of HFRS were from March to June and from October to December, particularly, the latter peak in 2012. The estimated effects of mean temperature and DTR on HFRS were non-linear. The immediate and strong effect of low temperature and high DTR on HFRS was found. The lowest temperature -8.86°C at lag 0 days indicated the largest related relative risk (RRs) with the reference (14.85 °C), respectively, 1.46 (95% CI 1.11-1.90) for total cases, 1.33 (95% CI 1.00-1.78) for the males and 1.76 (95% CI 1.12-2.79) for the females. Highest DTR was associated with a higher risk on HFRS, the largest RRs (95% CI) were obtained when DTR = 15.97 °C with a reference at 8.62 °C, with 1.26 (0.96-1.64) for total cases and 1.52 (0.97-2.38) for the female at lag 0 days, 1.22 (1.05-1.41) for the male at lag 5 days. Non-linear lag effects of mean temperature and DTR on HFRS were identified and there were slight differences for different sexes.
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29
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Meteorological factors and risk of hemorrhagic fever with renal syndrome in Guangzhou, southern China, 2006-2015. PLoS Negl Trop Dis 2018; 12:e0006604. [PMID: 29949572 PMCID: PMC6039051 DOI: 10.1371/journal.pntd.0006604] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 07/10/2018] [Accepted: 06/11/2018] [Indexed: 12/11/2022] Open
Abstract
Background The epidemic tendency of hemorrhagic fever with renal syndrome (HFRS) is on the rise in recent years in Guangzhou. This study aimed to explore the associations between meteorological factors and HFRS epidemic risk in Guangzhou for the period from 2006–2015. Methods We obtained data of HFRS cases in Guangzhou from the National Notifiable Disease Report System (NNDRS) during the period of 2006–2015. Meteorological data were obtained from the Guangzhou Meteorological Bureau. A negative binomial multivariable regression was used to explore the relationship between meteorological variables and HFRS. Results The annual average incidence was 0.92 per 100000, with the annual incidence ranging from 0.64/100000 in 2009 to 1.05/100000 in 2012. The monthly number of HFRS cases decreased by 5.543% (95%CI -5.564% to -5.523%) each time the temperature was increased by 1°C and the number of cases decreased by 0.075% (95%CI -0.076% to -0.074%) each time the aggregate rainfall was increased by 1 mm. We found that average temperature with a one-month lag was significantly associated with HFRS transmission. Conclusions Meteorological factors had significant association with occurrence of HFRS in Guangzhou, Southern China. This study provides preliminary information for further studies on epidemiological prediction of HFRS and for developing an early warning system. The prevalence of HFRS was on the rise in recent years, especially in the large and medium-sized cities in China. We obtained data of HFRS cases in Guangzhou from the National Notifiable Disease Report System (NNDRS) during the period of 2006–2015. Meteorological data were obtained from the Guangzhou Meteorological Bureau. A negative binomial multivariable regression was used to explore the relationship between meteorological variables and HFRS. Meteorological factors had significant association with occurrence of HFRS in Guangzhou, Southern China. This study provides preliminary information for further studies on epidemiological prediction of HFRS and for developing an early warning system.
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Kim HC, Kim WK, No JS, Lee SH, Gu SH, Chong ST, Klein TA, Song JW. Urban Rodent Surveillance, Climatic Association, and Genomic Characterization of Seoul Virus Collected at U.S. Army Garrison, Seoul, Republic of Korea, 2006-2010. Am J Trop Med Hyg 2018; 99:470-476. [PMID: 29869603 DOI: 10.4269/ajtmh.17-0459] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Rodent-borne pathogens pose a critical public health threat in urban areas. An epidemiological survey of urban rodents was conducted from 2006 to 2010 at the U.S. Army Garrison (USAG), Seoul, Republic of Korea (ROK), to determine the prevalence of Seoul virus (SEOV), a rodent-borne hantavirus. A total of 1,950 rodents were captured at USAG, Yongsan, near/in 19.4% (234/1,206) of the numbered buildings. Annual mean rodent infestation rates were the highest for food service facilities, e.g., the Dragon Hill Lodge complex (38.0 rodents) and the Hartell House (18.8 rodents). The brown rat, Rattus norvegicus, accounted for 99.4% (1,939/1,950) of all the rodents captured in the urban area, whereas only 0.6% (11/1,950) of the rodents was house mice (Mus musculus). In November 2006, higher numbers of rats captured were likely associated with climatic factors, e.g., rainfall and temperatures as rats sought harborage in and around buildings. Only 4.7% (34/718) of the rodents assayed for hantaviruses was serologically positive for SEOV. A total of 8.8% (3/34) R. norvegicus were positive for SEOV RNA by reverse transcription polymerase chain reaction, of which two SEOV strains were completely sequenced and characterized. The 3' and 5' terminal sequences revealed incomplete complementary genomic configuration. Seoul virus strains Rn10-134 and Rn10-145 formed a monophyletic lineage with the prototype SEOV strain 80-39. Seoul virus Medium segment showed the highest evolutionary rates compared with the Large and Small segments. In conclusion, this report provides significant insights into continued rodent-borne disease surveillance programs that identify hantaviruses for analysis of disease risk assessments and development of mitigation strategies.
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Affiliation(s)
- Heung-Chul Kim
- Medical Command Activity-Korea, 65th Medical Brigade, Unit 15281, APO AP 96271-5281
| | - Won-Keun Kim
- Department of Microbiology, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Jin Sun No
- Department of Microbiology, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Seung-Ho Lee
- Department of Microbiology, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Se Hun Gu
- 5th R&D Institute, Agency for Defense Development, Daejeon, Republic of Korea
| | - Sung-Tae Chong
- Medical Command Activity-Korea, 65th Medical Brigade, Unit 15281, APO AP 96271-5281
| | - Terry A Klein
- Medical Command Activity-Korea, 65th Medical Brigade, Unit 15281, APO AP 96271-5281
| | - Jin-Won Song
- Department of Microbiology, College of Medicine, Korea University, Seoul, Republic of Korea
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Raharinosy V, Olive MM, Andriamiarimanana FM, Andriamandimby SF, Ravalohery JP, Andriamamonjy S, Filippone C, Rakoto DAD, Telfer S, Heraud JM. Geographical distribution and relative risk of Anjozorobe virus (Thailand orthohantavirus) infection in black rats (Rattus rattus) in Madagascar. Virol J 2018; 15:83. [PMID: 29743115 PMCID: PMC5944027 DOI: 10.1186/s12985-018-0992-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Accepted: 04/30/2018] [Indexed: 11/10/2022] Open
Abstract
Background Hantavirus infection is a zoonotic disease that is associated with hemorrhagic fever with renal syndrome and cardiopulmonary syndrome in human. Anjozorobe virus, a representative virus of Thailand orthohantavirus (THAIV), was recently discovered from rodents in Anjozorobe-Angavo forest in Madagascar. To assess the circulation of hantavirus at the national level, we carried out a survey of small terrestrial mammals from representative regions of the island and identified environmental factors associated with hantavirus infection. As we were ultimately interested in the potential for human exposure, we focused our research in the peridomestic area. Methods Sampling was achieved in twenty districts of Madagascar, with a rural and urban zone in each district. Animals were trapped from a range of habitats and examined for hantavirus RNA by nested RT-PCR. We also investigated the relationship between hantavirus infection probability in rats and possible risk factors by using Generalized Linear Mixed Models. Results Overall, 1242 specimens from seven species were collected (Rattus rattus, Rattus norvegicus, Mus musculus, Suncus murinus, Setifer setosus, Tenrec ecaudatus, Hemicentetes semispinosus). Overall, 12.4% (111/897) of Rattus rattus and 1.6% (2/125) of Mus musculus were tested positive for THAIV. Rats captured within houses were less likely to be infected than rats captured in other habitats, whilst rats from sites characterized by high precipitation and relatively low seasonality were more likely to be infected than those from other areas. Older animals were more likely to be infected, with infection probability showing a strong increase with weight. Conclusions We report widespread distribution of THAIV in the peridomestic rats of Madagascar, with highest prevalence for those living in humid areas. Although the potential risk of infection to human may also be widespread, our results provide a first indication of specific zone with high transmission. Gathered data will be helpful to implement policies for control and prevention of human risk infection.
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Affiliation(s)
- Vololoniaina Raharinosy
- Virology Unit, Institute Pasteur de Madagascar, Ambatofotsikely, BP 1274, Antananarivo, Madagascar.,Ecole Doctorale des Sciences de la Vie et de l'Environnement, Equipe Pathogènes et Diversité Moléculaire, Faculté des Sciences, Université d'Antananarivo, Antananarivo, Madagascar
| | - Marie-Marie Olive
- Virology Unit, Institute Pasteur de Madagascar, Ambatofotsikely, BP 1274, Antananarivo, Madagascar
| | | | - Soa Fy Andriamandimby
- Virology Unit, Institute Pasteur de Madagascar, Ambatofotsikely, BP 1274, Antananarivo, Madagascar
| | - Jean-Pierre Ravalohery
- Virology Unit, Institute Pasteur de Madagascar, Ambatofotsikely, BP 1274, Antananarivo, Madagascar
| | - Seta Andriamamonjy
- Virology Unit, Institute Pasteur de Madagascar, Ambatofotsikely, BP 1274, Antananarivo, Madagascar
| | - Claudia Filippone
- Virology Unit, Institute Pasteur de Madagascar, Ambatofotsikely, BP 1274, Antananarivo, Madagascar
| | - Danielle Aurore Doll Rakoto
- Département de Biochimie Fondamentale et Appliquée, Faculté des Sciences, Université d'Antananarivo, Antananarivo, Madagascar
| | - Sandra Telfer
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK
| | - Jean-Michel Heraud
- Virology Unit, Institute Pasteur de Madagascar, Ambatofotsikely, BP 1274, Antananarivo, Madagascar.
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Abstract
Urbanization reduces exposure risk to many wildlife parasites and in general, improves overall health. However, our study importantly shows the complicated relationship between the diffusion of zoonotic pathogens and urbanization. Here, we reveal an unexpected relationship between hemorrhagic fever with renal syndrome incidence caused by a severe rodent-borne zoonotic pathogen worldwide and the process of urbanization in developing China. Our findings show that the number of urban immigrants is highly correlated with human incidence over time and also explain how the endemic turning points are associated with economic growth during the urbanization process. Our study shows that urbanizing regions of the developing world should focus their attention on zoonotic diseases. Urbanization and rural–urban migration are two factors driving global patterns of disease and mortality. There is significant concern about their potential impact on disease burden and the effectiveness of current control approaches. Few attempts have been made to increase our understanding of the relationship between urbanization and disease dynamics, although it is generally believed that urban living has contributed to reductions in communicable disease burden in industrialized countries. To investigate this relationship, we carried out spatiotemporal analyses using a 48-year-long dataset of hemorrhagic fever with renal syndrome incidence (HFRS; mainly caused by two serotypes of hantavirus in China: Hantaan virus and Seoul virus) and population movements in an important endemic area of south China during the period 1963–2010. Our findings indicate that epidemics coincide with urbanization, geographic expansion, and migrant movement over time. We found a biphasic inverted U-shaped relationship between HFRS incidence and urbanization, with various endemic turning points associated with economic growth rates in cities. Our results revealed the interrelatedness of urbanization, migration, and hantavirus epidemiology, potentially explaining why urbanizing cities with high economic growth exhibit extended epidemics. Our results also highlight contrasting effects of urbanization on zoonotic disease outbreaks during periods of economic development in China.
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Liang W, Gu X, Li X, Zhang K, Wu K, Pang M, Dong J, Merrill HR, Hu T, Liu K, Shao Z, Yan H. Mapping the epidemic changes and risks of hemorrhagic fever with renal syndrome in Shaanxi Province, China, 2005-2016. Sci Rep 2018; 8:749. [PMID: 29335595 PMCID: PMC5768775 DOI: 10.1038/s41598-017-18819-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 11/24/2017] [Indexed: 11/24/2022] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is a major rodent-borne zoonosis. Each year worldwide, 60,000–100,000 HFRS human cases are reported in more than seventy countries with almost 90% these cases occurring in China. Shaanxi Province in China has been among the most seriously affected areas since 1955. During 2009–2013, Shaanxi reported 11,400 human cases, the most of all provinces in China. Furthermore, the epidemiological features of HFRS have changed over time. Using long-term data of HFRS from 2005 to 2016, we carried out this retrospective epidemiological study combining ecological assessment models in Shaanxi. We found the majority of HFRS cases were male farmers who acquired infection in Guanzhong Plain, but the geographic extent of the epidemic has slowly spread northward. The highest age-specific attack rate since 2011 was among people aged 60–74 years, and the percentage of HFRS cases among the elderly increased from 12% in 2005 to 25% in 2016. We highly recommend expanding HFRS vaccination to people older than 60 years to better protect against the disease. Multivariate analysis revealed artificial area, cropland, pig and population density, GDP, and climate conditions (relative humidity, precipitation, and wind speed) as significant risk factors in the distribution of HFRS.
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Affiliation(s)
- Weifeng Liang
- Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University College of Medicine, Xi'an, 710061, China
| | - Xu Gu
- Department of Epidemiology, School of Public Health, Fourth Military Medical University, Xi'an, 710032, China.,Department of Epidemiology and Medical Statistics, School of Public Health and Management, Weifang Medical College, Weifang, 261000, China
| | - Xue Li
- Department of Epidemiology, School of Public Health, Fourth Military Medical University, Xi'an, 710032, China
| | - Kangjun Zhang
- Department of Epidemiology, School of Public Health, Fourth Military Medical University, Xi'an, 710032, China
| | - Kejian Wu
- Department of Mathematics, School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China
| | - Miaomiao Pang
- Shaanxi Provincial Corps Hospital of Chinese People's Armed Police Force, Xi'an, 710054, China
| | - Jianhua Dong
- Shaanxi Provincial Center for Disease Control and Prevention, Xi'an, 710054, China
| | - Hunter R Merrill
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida, 32611, USA
| | - Tao Hu
- Digital Resources and Information Center, Taishan Medical University, Taian, 271016, China
| | - Kun Liu
- Department of Epidemiology, School of Public Health, Fourth Military Medical University, Xi'an, 710032, China.
| | - Zhongjun Shao
- Department of Epidemiology, School of Public Health, Fourth Military Medical University, Xi'an, 710032, China.
| | - Hong Yan
- Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University College of Medicine, Xi'an, 710061, China.
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Xiao H, Tong X, Huang R, Gao L, Hu S, Li Y, Gao H, Zheng P, Yang H, Huang ZYX, Tan H, Tian H. Landscape and rodent community composition are associated with risk of hemorrhagic fever with renal syndrome in two cities in China, 2006-2013. BMC Infect Dis 2018; 18:37. [PMID: 29329512 PMCID: PMC5767038 DOI: 10.1186/s12879-017-2827-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 11/12/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by hantaviruses. Landscape can influence the risk of hantavirus infection for humans, mainly through its effect on rodent community composition and distribution. It is important to understand how landscapes influence population dynamics for different rodent species and the subsequent effect on HFRS risk. METHODS To determine how rodent community composition influenced human hantavirus infection, we monitored rodent communities in the prefecture-level cities of Loudi and Shaoyang, China, from 2006 to 2013. Land use data were extracted from satellite images and rodent community diversity was analyzed in 45 trapping sites, in different environments. Potential contact matrices, determining how rodent community composition influence HFRS infection among different land use types, were estimated based on rodent community composition and environment type for geo-located HFRS cases. RESULTS Apodemus agrarius and Rattus norvegicus were the predominant species in Loudi and Shaoyang, respectively. The major risk of HFRS infection was concentrated in areas with cultivated land and was associated with A. agrarius, R. norvegicus, and Rattus flavipectus. In urban areas in Shaoyang, Mus musculus was related to risk of hantavirus infection. CONCLUSIONS Landscape features and rodent community dynamics may affect the risk of human hantavirus infection. Results of this study may be useful for the development of HFRS prevention initiatives that are customized for regions with different geographical environments.
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Affiliation(s)
- Hong Xiao
- College of Resources and Environmental Sciences, Hunan Normal University, Changsha, 410081, China. .,Key Laboratory of Geospatial Big Data Mining and Application, Changsha, Hunan Province, 410081, China.
| | - Xin Tong
- College of Resources and Environmental Sciences, Hunan Normal University, Changsha, 410081, China.,Key Laboratory of Geospatial Big Data Mining and Application, Changsha, Hunan Province, 410081, China
| | - Ru Huang
- College of Resources and Environmental Sciences, Hunan Normal University, Changsha, 410081, China.,Key Laboratory of Geospatial Big Data Mining and Application, Changsha, Hunan Province, 410081, China
| | - Lidong Gao
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, China
| | - Shixiong Hu
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, China
| | - Yapin Li
- Center for Disease Control and Prevention of Beijing Military Region, Beijing, 100042, China
| | - Hongwei Gao
- Institute of Disaster Medicine and Public Health, Affiliated Hospital of Logistics University of Chinese People's Armed Police Force (PAP), Tianjin, China
| | - Pai Zheng
- Department of Occupational and Environmental Health, Peking University School of Public Health, Beijing, 100191, China
| | - Huisuo Yang
- Center for Disease Control and Prevention of Beijing Military Region, Beijing, 100042, China
| | - Zheng Y X Huang
- College of Life Sciences, Nanjing Normal University, Nanjing, China
| | - Hua Tan
- School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China.
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Joshi YP, Kim EH, Cheong HK. The influence of climatic factors on the development of hemorrhagic fever with renal syndrome and leptospirosis during the peak season in Korea: an ecologic study. BMC Infect Dis 2017; 17:406. [PMID: 28592316 PMCID: PMC5463320 DOI: 10.1186/s12879-017-2506-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 05/30/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Hemorrhagic fever with renal syndrome (HFRS) and leptospirosis are seasonal rodent-borne infections in the Republic of Korea (Korea). The occurrences of HFRS and leptospirosis are influenced by climatic variability. However, few studies have examined the effects of local climatic variables on the development of these infections. The purpose of this study was to estimate the effect of climatic factors on the occurrence of HFRS and leptospirosis in Korea. METHODS Daily records on human cases of HFRS and leptospirosis between January 2001 to December 2009 were analyzed. The associations of climatic factors with these cases in high incidence provinces were estimated using the time-series method and multivariate generalized linear Poisson models with a maximal lag of 12 weeks. RESULTS From 2001 to 2009, a total of 2912 HFRS and 889 leptospirosis cases were reported, with overall incidences of 0.67 and 0.21 cases per 100,000, respectively, in the study areas. The increase in minimum temperature (1 °C) at a lag of 11 weeks was associated with 17.8% [95% confidence interval (CI): 15.1, 20.6%] and 22.7% (95% CI: 16.5, 29.3%) increases in HFRS and leptospirosis cases, respectively. A 1-h increase in the daily sunshine was related to a 27.5% (95% CI: 18.2, 37.6%) increase in HFRS at a lag of 0 week. A 1% increase in daily minimum relative humidity and a 1 mm increase in daily rainfall were associated with 4.0% (95% CI:1.8, 6.1) and 2.0% (95% CI: 1.2, 2.8%) increases in weekly leptospirosis cases at 11 and 6 weeks later, respectively. A 1 mJ/m2 increase in daily solar radiation was associated with a 13.7% (95% CI: 4.9, 23.2%) increase in leptospirosis cases, maximized at a 2-week lag. CONCLUSIONS During the peak season in Korea, climatic factors play a significant role in the development of HFRS and leptospirosis. The findings of this study may be applicable to the forecasting and prediction of disease outbreaks.
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Affiliation(s)
- Yadav Prasad Joshi
- Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Eun-Hye Kim
- Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Hae-Kwan Cheong
- Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea.
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Ge L, Zhao Y, Zhou K, Mu X, Yu H, Wang Y, Wang N, Fan H, Guo L, Huo X. Spatio-Temporal Pattern and Influencing Factors of Hemorrhagic Fever with Renal Syndrome (HFRS) in Hubei Province (China) between 2005 and 2014. PLoS One 2016; 11:e0167836. [PMID: 28030550 PMCID: PMC5193338 DOI: 10.1371/journal.pone.0167836] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 11/21/2016] [Indexed: 11/18/2022] Open
Abstract
Hemorrhagic Fever with Renal Syndrome (HFRS) is considered as a globally distributed infectious disease, which results in many deaths annually in Hubei Province, China. The outbreak of HFRS is usually characterized with spatio-temporal heterogeneity and is seasonally distributed. Further, it might also be impacted by the influencing factors such as socio-economic and geographical environment. To better understand and predict the outbreak of HFRS in the Hubei Province, the spatio-temporal pattern and influencing factors were investigated in this study. Moran's I Index value was adopted in spatial global autocorrelation analysis to identify the overall spatio-temporal pattern of HFRS outbreak. Kulldorff scan statistical analysis was performed to further identify the changing trends of the clustering patterns of HFRS outbreak. Spearman's rank correlation analysis was used to explore the possible influencing factors on HFRS epidemics such as climate and geographic. The results demonstrated that HFRS outbreak in Hubei Province decreased from 2005 to 2012 in general while increasing slightly from 2012 to 2014. The spatial and temporal scan statistical analysis indicated that HFRS epidemic was temporally clustered in summer and autumn from 2005 to 2014 except 2008 and 2011. The seasonal epidemic pattern of HFRS in Hubei Province was characterized by a bimodal pattern (March to May and September to November) while peaks often occurring in the spring time. SEOV-type HFRS was presumed to influence more on the total number of HFRS incidence than HTNV-type HFRS do. The average humidity and human population density were the main influencing factors during these years. HFRS outbreaks were more in plains than in other areas of Hubei Province. We did not find that whether the terrain of the wetland (water system) plays a significant role in the outbreak of HFRS incidence. With a better understanding of rodent infection rate, socio-economic status and ecological environment characteristics, this study may help to reduce the outbreak of HFRS disease.
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Affiliation(s)
- Liang Ge
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan city, Hubei Province, PR China
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
- * E-mail:
| | - Youlin Zhao
- Business School of Hohai University, Nanjing city, Jiangsu Province, PR China
| | - Kui Zhou
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
| | - Xiangming Mu
- School of Information Studies in University of Wisconsin-Milwaukee 2025 E Newpot Ave #NWQB, Milwaukee, WI, United States of America
| | - Haibo Yu
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
| | - Yongfeng Wang
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
| | - Ning Wang
- First Crust Deformation Monitoring and Application Center, China Earthquake administration, Tianjin city, PR China
| | - Hong Fan
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan city, Hubei Province, PR China
| | - Liqiang Guo
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
| | - XiXiang Huo
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, China
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Ge L, Zhao Y, Sheng Z, Wang N, Zhou K, Mu X, Guo L, Wang T, Yang Z, Huo X. Construction of a Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) Model and Comparative Analysis with GWR-Based Models for Hemorrhagic Fever with Renal Syndrome (HFRS) in Hubei Province (China). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:E1062. [PMID: 27801870 PMCID: PMC5129272 DOI: 10.3390/ijerph13111062] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Revised: 10/08/2016] [Accepted: 10/26/2016] [Indexed: 11/16/2022]
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is considered a globally distributed infectious disease which results in many deaths annually in Hubei Province, China. In order to conduct a better analysis and accurately predict HFRS incidence in Hubei Province, a new model named Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) was constructed. The SD-GTWR model, which integrates the analysis and relationship of seasonal difference, spatial and temporal characteristics of HFRS (HFRS was characterized by spatiotemporal heterogeneity and it is seasonally distributed), was designed to illustrate the latent relationships between the spatio-temporal pattern of the HFRS epidemic and its influencing factors. Experiments from the study demonstrated that SD-GTWR model is superior to traditional models such as GWR- based models in terms of the efficiency and the ability of providing influencing factor analysis.
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Affiliation(s)
- Liang Ge
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
- Tianjin Institute of Surveying and Mapping, Tianjin 300381, China.
| | - Youlin Zhao
- Business School of Hohai University, Nanjing 211100, China.
| | - Zhongjie Sheng
- Tianjin Institute of Surveying and Mapping, Tianjin 300381, China.
| | - Ning Wang
- First Crust Deformation Monitoring and Application Center, China Earthquake Administration, Tianjin 300180, China.
| | - Kui Zhou
- Tianjin Institute of Surveying and Mapping, Tianjin 300381, China.
| | - Xiangming Mu
- School of Information Studies of University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA.
| | - Liqiang Guo
- Tianjin Institute of Surveying and Mapping, Tianjin 300381, China.
| | - Teng Wang
- Business School of Hohai University, Nanjing 211100, China.
| | - Zhanqiu Yang
- State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan 430079, China.
| | - Xixiang Huo
- Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China.
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Li S, Cao W, Ren H, Lu L, Zhuang D, Liu Q. Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China. PLoS One 2016; 11:e0163771. [PMID: 27706256 PMCID: PMC5051726 DOI: 10.1371/journal.pone.0163771] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Accepted: 09/14/2016] [Indexed: 11/30/2022] Open
Abstract
Exact prediction of Hemorrhagic fever with renal syndrome (HFRS) epidemics must improve to establish effective preventive measures in China. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish a highly predictive model of HFRS. Meteorological factors were considered external variables through a cross correlation analysis. Then, these factors were included in the SARIMA model to determine if they could improve the predictive ability of HFRS epidemics in the region. The optimal univariate SARIMA model was identified as (0,0,2)(1,1,1)12. The R2 of the prediction of HFRS cases from January 2014 to December 2014 was 0.857, and the Root mean square error (RMSE) was 2.708. However, the inclusion of meteorological variables as external regressors did not significantly improve the SARIMA model. This result is likely because seasonal variations in meteorological variables were included in the seasonal characteristics of the HFRS itself.
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Affiliation(s)
- Shujuan Li
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing, 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Beijing, 100049, China
| | - Wei Cao
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing, 100101, China
| | - Hongyan Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing, 100101, China
- * E-mail:
| | - Liang Lu
- State Key Laboratory for Infectious Diseases Prevention and Control, National Institute for Communicable Disease Control and Prevention, China CDC, 5 Changbai Road, Changping, Beijing, 102206, China
| | - Dafang Zhuang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing, 100101, China
| | - Qiyong Liu
- State Key Laboratory for Infectious Diseases Prevention and Control, National Institute for Communicable Disease Control and Prevention, China CDC, 5 Changbai Road, Changping, Beijing, 102206, China
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Wang T, Liu J, Zhou Y, Cui F, Huang Z, Wang L, Zhai S. Prevalence of hemorrhagic fever with renal syndrome in Yiyuan County, China, 2005-2014. BMC Infect Dis 2016; 16:69. [PMID: 26852019 PMCID: PMC4744626 DOI: 10.1186/s12879-016-1404-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Accepted: 02/02/2016] [Indexed: 11/10/2022] Open
Abstract
Background Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China, where human cases account for 90 % of the total global cases. Yiyuan County is one of the most serious affected areas in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence in Yiyuan to make the control of HFRS more effective. Methods The study was based on the reported cases of HFRS from the National Notifiable Disease Surveillance System. The demographic and spatial distributions of HFRS in Yiyuan were established. Then we fit autoregressive integrated moving average (ARIMA) models and predict the HFRS epidemic trend. Results There were 362 cases reported in Yiyuan during the 10-year study period. The human infections in the fall and winter reflected a seasonal characteristic pattern of Hantaan virus (HTNV) transmission. The best model was ARIMA (2, 1, 1) × (0, 1, 1)12 (AIC value 516.86) with a high validity. Conclusion The ARIMA model fits the fluctuations in HFRS frequency and it can be used for future forecasting when applied to HFRS prevention and control.
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Affiliation(s)
- Tao Wang
- Department of Infectious Disease Control and Prevention, Zibo Center for Disease Control and Prevention, Zibo, Shandong Province, P. R. China
| | - Jie Liu
- Department of Infectious Disease Control and Prevention, Zibo Center for Disease Control and Prevention, Zibo, Shandong Province, P. R. China
| | - Yunping Zhou
- Department of Infectious Disease Control and Prevention, Zibo Center for Disease Control and Prevention, Zibo, Shandong Province, P. R. China
| | - Feng Cui
- Department of Infectious Disease Control and Prevention, Zibo Center for Disease Control and Prevention, Zibo, Shandong Province, P. R. China
| | - Zhenshui Huang
- Department of Infectious Disease Control and Prevention, Zibo Center for Disease Control and Prevention, Zibo, Shandong Province, P. R. China
| | - Ling Wang
- Department of Infectious Disease Control and Prevention, Zibo Center for Disease Control and Prevention, Zibo, Shandong Province, P. R. China.
| | - Shenyong Zhai
- Department of Infectious Disease Control and Prevention, Zibo Center for Disease Control and Prevention, Zibo, Shandong Province, P. R. China.
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Zheng Y, Wei J, Zhou BY, Xu Y, Dong JH, Guan LY, Ma P, Yu PB, Wang JJ. Long-term persistence of anti-hantavirus antibodies in sera of patients undergoing hemorrhagic fever with renal syndrome and subjects vaccinated against the disease. Infect Dis (Lond) 2015; 48:262-266. [DOI: 10.3109/23744235.2015.1121289] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Wang T, Zhou Y, Wang L, Huang Z, Cui F, Zhai S. Using an Autoregressive Integrated Moving Average Model to Predict the Incidence of Hemorrhagic Fever with Renal Syndrome in Zibo, China, 2004-2014. Jpn J Infect Dis 2015; 69:279-84. [PMID: 26370428 DOI: 10.7883/yoken.jjid.2014.567] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China, where human cases account for 90% of the total global cases. Zibo City is one of the most seriously affected areas in Shandong Province, China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence in Zibo to make the control of HFRS more effective. In this study, we constructed an autoregressive integrated moving average (ARIMA) model for monthly HFRS incidence in Zibo from 2004 to 2013. The ARIMA (3,1,1) × (2,1,1)12 model is reliable with a high validity, which can be used to predict the next year's HFRS incidence in Zibo. The forecast results suggest that the HFRS incidence in Zibo will experience a slight growth in the next year.
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Affiliation(s)
- Tao Wang
- Zibo Center for Disease Control and Prevention, Zibo, People's Republic of China
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Bai Y, Xu Z, Lu B, Sun Q, Tang W, Liu X, Yang W, Xu X, Liu Q. Effects of Climate and Rodent Factors on Hemorrhagic Fever with Renal Syndrome in Chongqing, China, 1997-2008. PLoS One 2015; 10:e0133218. [PMID: 26193359 PMCID: PMC4507865 DOI: 10.1371/journal.pone.0133218] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Accepted: 06/23/2015] [Indexed: 11/18/2022] Open
Abstract
China has the highest global incidence of hemorrhagic fever with renal syndrome (HFRS), constituting 90% of the cases in the world. Chongqing, located in the Three Gorges Reservoir Region, has been experiencing differences in the occurrence of HFRS from 1997 to 2008. The current study was designed to explore the effects of climate and rodent factors on the transmission of HFRS in Chongqing. Data on monthly HFRS cases, rodent strains, and climatic factors were collected from 1997 to 2008. Spatio-temporal analysis indicated that most HFRS cases were clustered in central Chongqing and that the incidence of HFRS decreased from 1997 to 2008. Poisson regression models showed that temperature (with lagged months of 0 and 5) and rainfall (with 2 lagged months) were key climatic factors contributing to the transmission of HFRS. A zero-inflated negative binomial model revealed that rodent density was also significantly associated with the occurrence of HFRS in the Changshou district. The monthly trend in HFRS incidence was positively associated with rodent density and rainfall and negatively associated with temperature. Possible mechanisms are proposed through which construction of the dam influenced the incidence of HFRS in Chongqing. The findings of this study may contribute to the development of early warning systems for the control and prevention of HFRS in the Three Gorges Reservoir Region.
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Affiliation(s)
- Yuntao Bai
- Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, Ohio, United States of America
| | - Zhiguang Xu
- Department of Statistics, The Ohio State University, Columbus, Ohio, United States of America
| | - Bo Lu
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio, United States of America
| | - Qinghua Sun
- Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, Ohio, United States of America
| | - Wenge Tang
- Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Xiaobo Liu
- Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
- State Key Laboratory for Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Weizhong Yang
- Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
- * E-mail: (QL); (XX); (WY)
| | - Xinyi Xu
- Department of Statistics, The Ohio State University, Columbus, Ohio, United States of America
- * E-mail: (QL); (XX); (WY)
| | - Qiyong Liu
- Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
- State Key Laboratory for Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- * E-mail: (QL); (XX); (WY)
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Transmission of haemorrhagic fever with renal syndrome in china and the role of climate factors: a review. Int J Infect Dis 2015; 33:212-8. [PMID: 25704595 DOI: 10.1016/j.ijid.2015.02.010] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Accepted: 02/15/2015] [Indexed: 11/23/2022] Open
Abstract
Haemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease that poses a serious public health threat in China. HFRS is caused by hantaviruses, mainly Seoul virus in urban areas and Hantaan virus in agricultural areas. Although preventive measures including vaccination programs and rodent control measures have resulted in a decline in cases in recent years, there has been an increase in incidence in some areas and new endemic areas have emerged. This review summarises the recent literature relating to the effects of climatic factors on the incidence of HFRS in China and discusses future research directions. Temperature, precipitation and humidity affect crop yields, rodent breeding patterns and disease transmission, and these can be influenced by a changing climate. Detailed surveillance of infections caused by Hantaan and Seoul viruses and further research on the viral agents will aid in interpretation of spatiotemporal patterns and a better understanding of the environmental and ecological drivers of HFRS amid China's rapidly urbanising landscape and changing climate.
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Li S, Ren H, Hu W, Lu L, Xu X, Zhuang D, Liu Q. Spatiotemporal heterogeneity analysis of hemorrhagic fever with renal syndrome in China using geographically weighted regression models. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2014; 11:12129-47. [PMID: 25429681 PMCID: PMC4276605 DOI: 10.3390/ijerph111212129] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2014] [Revised: 11/17/2014] [Accepted: 11/18/2014] [Indexed: 11/24/2022]
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in China. The identification of the spatiotemporal pattern of HFRS will provide a foundation for the effective control of the disease. Based on the incidence of HFRS, as well as environmental factors, and social-economic factors of China from 2005–2012, this paper identified the spatiotemporal characteristics of HFRS distribution and the factors that impact this distribution. The results indicate that the spatial distribution of HFRS had a significant, positive spatial correlation. The spatiotemporal heterogeneity was affected by the temperature, precipitation, humidity, NDVI of January, NDVI of August for the previous year, land use, and elevation in 2005–2009. However, these factors did not explain the spatiotemporal heterogeneity of HFRS incidences in 2010–2012. Spatiotemporal heterogeneity of provincial HFRS incidences and its relation to environmental factors would provide valuable information for hygiene authorities to design and implement effective measures for the prevention and control of HFRS in China.
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Affiliation(s)
- Shujuan Li
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China.
| | - Hongyan Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China.
| | - Wensheng Hu
- Center for Health Statistics and Information, National Health and Family Planning Commission, No.38 Beilishi Road, Xicheng District, Beijing 100044, China.
| | - Liang Lu
- State Key Laboratory for Infectious Diseases Prevention and Control, National Institute for Communicable Disease Control and Prevention, China CDC, 5 Changbai Road, Changping, Beijing 102206, China.
| | - Xinliang Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China.
| | - Dafang Zhuang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China.
| | - Qiyong Liu
- State Key Laboratory for Infectious Diseases Prevention and Control, National Institute for Communicable Disease Control and Prevention, China CDC, 5 Changbai Road, Changping, Beijing 102206, China.
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Zhang WY, Wang LY, Liu YX, Yin WW, Hu WB, Magalhaes RJS, Ding F, Sun HL, Zhou H, Li SL, Haque U, Tong SL, Glass GE, Bi P, Clements ACA, Liu QY, Li CY. Spatiotemporal transmission dynamics of hemorrhagic fever with renal syndrome in China, 2005-2012. PLoS Negl Trop Dis 2014; 8:e3344. [PMID: 25412324 PMCID: PMC4239011 DOI: 10.1371/journal.pntd.0003344] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2014] [Accepted: 10/14/2014] [Indexed: 12/30/2022] Open
Abstract
Background Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by many serotypes of hantaviruses. In China, HFRS has been recognized as a severe public health problem with 90% of the total reported cases in the world. This study describes the spatiotemporal dynamics of HFRS cases in China and identifies the regions, time, and populations at highest risk, which could help the planning and implementation of key preventative measures. Methods Data on all reported HFRS cases at the county level from January 2005 to December 2012 were collected from Chinese Center for Disease Control and Prevention. Geographic Information System-based spatiotemporal analyses including Local Indicators of Spatial Association and Kulldorff's space-time scan statistic were performed to detect local high-risk space-time clusters of HFRS in China. In addition, cases from high-risk and low-risk counties were compared to identify significant demographic differences. Results A total of 100,868 cases were reported during 2005–2012 in mainland China. There were significant variations in the spatiotemporal dynamics of HFRS. HFRS cases occurred most frequently in June, November, and December. There was a significant positive spatial autocorrelation of HFRS incidence during the study periods, with Moran's I values ranging from 0.46 to 0.56 (P<0.05). Several distinct HFRS cluster areas were identified, mainly concentrated in northeastern, central, and eastern of China. Compared with cases from low-risk areas, a higher proportion of cases were younger, non-farmer, and floating residents in high-risk counties. Conclusions This study identified significant space-time clusters of HFRS in China during 2005–2012 indicating that preventative strategies for HFRS should be particularly focused on the northeastern, central, and eastern of China to achieve the most cost-effective outcomes. Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne viral disease caused by many serotypes of hantaviruses. In China, HFRS has been recognized as a severe public health problem and accounts for 90% of the reported cases in the world. We examined the spatiotemporal dynamics of HFRS cases in China during 2005–2012 and compared characteristics between cases from high-risk and low-risk counties. Several distinct HFRS cluster areas were identified, concentrated in northeastern, central, and eastern of China. Compared with cases from low-risk areas, a higher proportion of cases were younger, non-farmer, and floating residents in high-risk counties. These findings suggest preventative strategies for HFRS should be focused on the identified clusters in order to achieve the most cost-effective outcomes.
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Affiliation(s)
- Wen-Yi Zhang
- Institute of Disease Control and Prevention, Academy of Military Medical Science, Beijing, People's Republic of China
| | - Li-Ya Wang
- Institute of Disease Control and Prevention, Academy of Military Medical Science, Beijing, People's Republic of China
| | - Yun-Xi Liu
- Department of Infection Management and Disease Control, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Wen-Wu Yin
- Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China
| | - Wen-Biao Hu
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Ricardo J. Soares. Magalhaes
- School of Veterinary Science, The University of Queensland, Brisbane, Australia
- WHO Collaborating Centre for Children Environmental Health, Queensland Children's Medical Research Institute, University of Queensland, Brisbane, Australia
| | - Fan Ding
- Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China
| | - Hai-Long Sun
- Institute of Disease Control and Prevention, Academy of Military Medical Science, Beijing, People's Republic of China
| | - Hang Zhou
- Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China
| | - Shen-Long Li
- Institute of Disease Control and Prevention, Academy of Military Medical Science, Beijing, People's Republic of China
| | - Ubydul Haque
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
- Department of Geography, University of Florida, Gainesville, Florida, United States of America
| | - Shi-Lu Tong
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Gregory E. Glass
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
- Department of Geography, University of Florida, Gainesville, Florida, United States of America
| | - Peng Bi
- Discipline of Public Health, University of Adelaide, Adelaide, Australia
| | - Archie C. A. Clements
- Research School of Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Qi-Yong Liu
- State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China
- * E-mail: (QL)
| | - Cheng-Yi Li
- Institute of Disease Control and Prevention, Academy of Military Medical Science, Beijing, People's Republic of China
- * E-mail: (QL)
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Lin H, Zhang Z, Lu L, Li X, Liu Q. Meteorological factors are associated with hemorrhagic fever with renal syndrome in Jiaonan County, China, 2006-2011. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2014; 58:1031-1037. [PMID: 23793957 DOI: 10.1007/s00484-013-0688-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2013] [Revised: 05/26/2013] [Accepted: 05/28/2013] [Indexed: 06/02/2023]
Abstract
This study examined the effect of meteorological factors on the occurrence of hemorrhagic fever with renal syndrome (HFRS) using a generalized additive model with penalized smoothing splines in Jiaonan, China, from 2006 to 2011. The dose-response relationship was first examined, and then the association between daily meteorological variables and HFRS occurrence was investigated according to the dose-response curves. There were two linear segments in the temperature-HFRS relationship curve. When daily temperature was lower than 17 °C, a positive association was found [with excessive risk (ER) for 1 °C increase on the current day being 2.56 %, 95 % confidence interval (CI): 0.36 % to 4.80 %]. An inverse association was found when daily temperature was higher than 17 °C [ER for 1 °C increase on the current day was -12.82 % (95 % CI: -17.51 % to -7.85 %)]. Inverse associations were observed for relative humidity [ER for 1 % increase on lag day 4 was -1.21 % (95 % CI: -1.63 % to -0.79 %)] and rainfall [ER for 1 mm increase on lag day 1 was -2.20 % (95 % CI: -3.56 % to -0.82 %)]. Meteorological factors might be important predictor of HFRS epidemics in Jiaonan County.
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Affiliation(s)
- Hualiang Lin
- Guangdong Provincial Institute of Public Health, Guangzhou, China
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Wu J, Wang DD, Li XL, de Vlas SJ, Yu YQ, Zhu J, Zhang Y, Wang B, Yan L, Fang LQ, Liu YW, Cao WC. Increasing incidence of hemorrhagic fever with renal syndrome could be associated with livestock husbandry in Changchun, northeastern China. BMC Infect Dis 2014; 14:301. [PMID: 24894341 PMCID: PMC4050097 DOI: 10.1186/1471-2334-14-301] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2013] [Accepted: 05/29/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Since the end of the 1990s, the incidence of hemorrhagic fever with renal syndrome (HFRS) has been increasing dramatically in Changchun, northeastern China. However, it is unknown which, and how, underlying risk factors have been involved in the reemergence of the disease. METHODS Data on HFRS cases at the county scale were collected from 1998 to 2012. Data on livestock husbandry including the numbers of large animals (cattle, horses, donkeys and mules), sheep, and deer, and on climatic and land cover variables were also collected. Epidemiological features, including the spatial, temporal and human patterns of disease were characterized. The potential factors related to spatial heterogeneity and temporal trends were analyzed using standard and time-series Poisson regression analysis, respectively. RESULTS Annual incidence varied among the 10 counties. Shuangyang County in southeastern Changchun had the highest number of cases (1,525 cases; 35.9% of all cases), but its population only accounted for 5.6% of the total population. Based on seasonal pattern in HFRS incidence, two epidemic phases were identified. One was a single epidemic peak at the end of each year from 1988 to 1997 and the other consisted of dual epidemic peaks at both the end and the beginning of each year from 1998 to the end of the study period. HFRS incidence was higher in males compared to females, and most of the HFRS cases occurred in peasant populations. The results of the Poisson regression analysis indicated that the spatial distribution and the increasing incidence of HFRS were significantly associated with livestock husbandry and climate factors, particularly with deer cultivation. CONCLUSIONS Our results indicate that the re-emergence of HFRS in Changchun has been accompanied by changing seasonal patterns over the past 25 years. Integrated measures focusing on areas related to local livestock husbandry could be helpful for the prevention and control of HFRS.
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Affiliation(s)
- Jing Wu
- Department of Epidemiology and Statistics, Jilin University, Changchun, People’s Republic of China
- Changchun Center for Disease Control and Prevention, Changchun, People’s Republic of China
| | - Dan-Dan Wang
- School of Public Health, Central South University, Changsha, People’s Republic of China
- State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People’s Republic of China
| | - Xin-Lou Li
- State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People’s Republic of China
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, Rotterdam, The Netherlands
| | - Ya-Qin Yu
- Department of Epidemiology and Statistics, Jilin University, Changchun, People’s Republic of China
| | - Jian Zhu
- Department of Epidemiology and Statistics, Jilin University, Changchun, People’s Republic of China
| | - Ying Zhang
- Changchun Center for Disease Control and Prevention, Changchun, People’s Republic of China
| | - Bo Wang
- Changchun Center for Disease Control and Prevention, Changchun, People’s Republic of China
| | - Li Yan
- Department of Epidemiology and Statistics, Jilin University, Changchun, People’s Republic of China
- Changchun Center for Disease Control and Prevention, Changchun, People’s Republic of China
| | - Li-Qun Fang
- State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People’s Republic of China
| | - Ya-Wen Liu
- Department of Epidemiology and Statistics, Jilin University, Changchun, People’s Republic of China
| | - Wu-Chun Cao
- School of Public Health, Central South University, Changsha, People’s Republic of China
- State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People’s Republic of China
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Zhang YH, Ge L, Liu L, Huo XX, Xiong HR, Liu YY, Liu DY, Luo F, Li JL, Ling JX, Chen W, Liu J, Hou W, Zhang Y, Fan H, Yang ZQ. The epidemic characteristics and changing trend of hemorrhagic fever with renal syndrome in Hubei Province, China. PLoS One 2014; 9:e92700. [PMID: 24658382 PMCID: PMC3962441 DOI: 10.1371/journal.pone.0092700] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2013] [Accepted: 02/24/2014] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Hemorrhagic fever with renal syndrome (HFRS) is caused by different hantaviruses within the Bunyaviridae family. HFRS is a fulminant, infectious disease that occurs worldwide and is endemic in all 31 provinces of China. Since the first HFRS case in Hubei Province was reported in 1957, the disease has spread across the province and Hubei has become one of the seriously affected areas in China with the greatest number of reported HFRS cases in the 1980's. However, the epidemic characteristics of HFRS in Hubei are still not entirely clear and long-term, systematic investigations of this epidemic area have been very limited. METHODS The spatiotemporal distribution of HFRS was investigated using data spanning the years 1980 to 2009. The annual HFRS incidence, fatality rate and seasonal incidence between 1980 and 2009 were calculated and plotted. GIS-based spatial analyses were conducted to detect the spatial distribution and seasonal pattern of HFRS. A spatial statistical analysis, using Kulldorff's spatial scan statistic, was performed to identify clustering of HFRS. RESULTS A total of 104,467 HFRS cases were reported in Hubei Province between 1980 and 2009. Incidence of and mortality due to HFRS declined after the outbreak in 1980s and HFRS cases have been sporadic in recent years. The locations and scale of disease clusters have changed during the three decades. The seasonal epidemic pattern of HFRS was characterized by the shift from the unimodal type (autumn/winter peak) to the bimodal type. CONCLUSIONS Socioeconomic development has great influence on the transmission of hantaviruses to humans and new epidemic characteristics have emerged in Hubei Province. It is necessary to reinforce preventative measures against HFRS according to the newly-presented seasonal variation and to intensify these efforts especially in the urban areas of Hubei Province.
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Affiliation(s)
- Yi-Hui Zhang
- State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan, Hubei, PR China
| | - Liang Ge
- Tianjin Institute of Surveying and Mapping, Tianjin, PR China
| | - Li Liu
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, PR China
| | - Xi-Xiang Huo
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, PR China
| | - Hai-Rong Xiong
- State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan, Hubei, PR China
| | - Yuan-Yuan Liu
- State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan, Hubei, PR China
| | - Dong-Ying Liu
- State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan, Hubei, PR China
| | - Fan Luo
- State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan, Hubei, PR China
| | - Jin-Lin Li
- State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan, Hubei, PR China
| | - Jia-Xin Ling
- State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan, Hubei, PR China
| | - Wen Chen
- State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan, Hubei, PR China
| | - Jing Liu
- State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan, Hubei, PR China
| | - Wei Hou
- State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan, Hubei, PR China
| | - Yun Zhang
- Institute of Military Medical Sciences, Nanjing Command, Nanjing, PR China
| | - Hong Fan
- State Key Laboratory of Information Engineering in Survey, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, PR China
| | - Zhan-Qiu Yang
- State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan, Hubei, PR China
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Changes in diversification patterns and signatures of selection during the evolution of murinae-associated hantaviruses. Viruses 2014; 6:1112-34. [PMID: 24618811 PMCID: PMC3970142 DOI: 10.3390/v6031112] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2013] [Revised: 02/19/2014] [Accepted: 02/24/2014] [Indexed: 12/31/2022] Open
Abstract
In the last 50 years, hantaviruses have significantly affected public health worldwide, but the exact extent of the distribution of hantavirus diseases, species and lineages and the risk of their emergence into new geographic areas are still poorly known. In particular, the determinants of molecular evolution of hantaviruses circulating in different geographical areas or different host species are poorly documented. Yet, this understanding is essential for the establishment of more accurate scenarios of hantavirus emergence under different climatic and environmental constraints. In this study, we focused on Murinae-associated hantaviruses (mainly Seoul Dobrava and Hantaan virus) using sequences available in GenBank and conducted several complementary phylogenetic inferences. We sought for signatures of selection and changes in patterns and rates of diversification in order to characterize hantaviruses’ molecular evolution at different geographical scales (global and local). We then investigated whether these events were localized in particular geographic areas. Our phylogenetic analyses supported the assumption that RNA virus molecular variations were under strong evolutionary constraints and revealed changes in patterns of diversification during the evolutionary history of hantaviruses. These analyses provide new knowledge on the molecular evolution of hantaviruses at different scales of time and space.
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Ongoing spillover of Hantaan and Gou hantaviruses from rodents is associated with hemorrhagic fever with renal syndrome (HFRS) in China. PLoS Negl Trop Dis 2013; 7:e2484. [PMID: 24147168 PMCID: PMC3798614 DOI: 10.1371/journal.pntd.0002484] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Accepted: 09/06/2013] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Longquan City, Zhejiang province, China, has been seriously affected by hemorrhagic fever with renal syndrome (HFRS) since the first cases were registered in 1974. To understand the epidemiology and emergence of HFRS in Longquan, which may be indicative of large parts of rural China, we studied long-term incidence patterns and performed a molecular epidemiological investigation of the causative hantaviruses in human and rodent populations. METHOD/PRINCIPAL FINDINGS During 1974-2011, 1866 cases of HFRS were recorded in Longquan, including 20 deaths. In 2011, the incidence of HFRS remained high, with 19.61 cases/100,000 population, despite the onset of vaccination in 1997. During 1974-1998, HFRS cases in Longquan occurred mainly in winter, while in the past decade the peak of HFRS has shifted to the spring. Notably, the concurrent prevalence of rodent-borne hantaviruses in the region was also high. Phylogenetic analyses of viral sequences recovered from rodents in Longquan revealed the presence of novel genetic variants of Gou virus (GOUV) in Rattus sp. rats and Hantaan virus (HTNV) in the stripe field mice, respectively. Strikingly, viral sequences sampled from infected humans were very closely related to those from rodents. CONCLUSIONS/SIGNIFICANCE HFRS represents an important public health problem in Longquan even after years of preventive measures. Our data suggest that continual spillover of the novel genetic variant of GOUV and the new genetic lineage of HTNV are responsible for the high prevalence of HFRS in humans. In addition, this is the first report of GOUV associated with human HFRS cases, and our data suggest that GOUV is now the major cause of HFRS in this region.
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