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Liu Y, Liu C, Wang L, Chen X, Qiao H, Zhang Y, Cai B, Xue R, Yi C. Investigating the impact of climatic and environmental factors on HFRS prevalence in Anhui Province, China, using satellite and reanalysis data. Front Public Health 2024; 12:1447501. [PMID: 39411492 PMCID: PMC11475030 DOI: 10.3389/fpubh.2024.1447501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Accepted: 09/11/2024] [Indexed: 10/19/2024] Open
Abstract
Introduction Hemorrhagic Fever with Renal Syndrome (HFRS) is the most commonly diagnosed zoonosis in Asia. Despite taking various preventive measures, HFRS remains prevalent across multiple regions in China. This study aims to investigate the impact of climatic and environmental factors on the prevalence of HFRS in Anhui Province, China, utilizing satellite and reanalysis data. Methods We collect monthly HFRS data from Anhui Province spanning 2005 to 2019 and integrated MODIS satellite datasets and ERA5 reanalysis data, including variables such as precipitation, temperature, humidity, solar radiation, aerosol optical depth (AOD), and Normalized Difference Vegetation Index (NDVI). Continuous wavelet transform, Spearman correlation analysis, and Poisson regression analysis are employed to assess the association between climatic and environmental factors and HFRS cases. Results Our findings reveal that HFRS cases predominantly occur during the spring and winter seasons, with the highest peak intensity observed in a 9-year cycle. Notably, the monthly average relative humidity exhibits a Spearman correlation coefficient of 0.404 at a 4-month lag, taking precedence over other contributing factors. Poisson regression analysis elucidates that NDVI at a 2-month lag, mean temperature (T) and solar radiation (SR) at a 4-month lag, precipitation (P), relative humidity (RH), and AOD at a 5-month lag exhibit the most robust explanatory power for HFRS occurrence. Moreover, the developed predictive model exhibiting commendable accuracy. Discussion This study provides key evidence for understanding how climatic and environmental factors influence the transmission of HFRS at the provincial scale. Insights from this research are critical for formulating effective preventive strategies and serving as a resource for HFRS prevention and control efforts.
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Affiliation(s)
- Ying Liu
- Department of Infection, Yancheng No.1 People's Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng, China
| | - Chengyuan Liu
- Department of Infection, Yancheng No.1 People's Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng, China
| | - Liping Wang
- Department of Infectious Diseases, Xuzhou Medical University, Xuzhou, China
| | - Xian Chen
- Department of Infection, Yancheng No.1 People's Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng, China
| | - Huijie Qiao
- Department of Infection, Yancheng No.1 People's Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng, China
| | - Yan Zhang
- Department of Infection, Yancheng No.1 People's Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng, China
| | - Binggang Cai
- Department of Infection, Yancheng No.1 People's Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng, China
| | - Rongrong Xue
- Department of Infection, Yancheng No.1 People's Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng, China
| | - Chuanxiang Yi
- Yancheng Meteorological Administration, Yancheng, China
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Jiang D, Zhang J, Shen W, Sun Y, Wang Z, Wang J, Zhang J, Zhang G, Zhang G, Wang Y, Cai S, Zhang J, Wang Y, Liu R, Bai T, Sun Y, Yang S, Ma Z, Li Z, Li J, Ma C, Cheng L, Sun B, Yang K. DNA Vaccines Encoding HTNV GP-Derived Th Epitopes Benefited from a LAMP-Targeting Strategy and Established Cellular Immunoprotection. Vaccines (Basel) 2024; 12:928. [PMID: 39204051 PMCID: PMC11359959 DOI: 10.3390/vaccines12080928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Revised: 08/06/2024] [Accepted: 08/14/2024] [Indexed: 09/03/2024] Open
Abstract
Vaccines has long been the focus of antiviral immunotherapy research. Viral epitopes are thought to be useful biomarkers for immunotherapy (both antibody-based and cellular). In this study, we designed a novel vaccine molecule, the Hantaan virus (HTNV) glycoprotein (GP) tandem Th epitope molecule (named the Gnc molecule), in silico. Subsequently, computer analysis was used to conduct a comprehensive and in-depth study of the various properties of the molecule and its effects as a vaccine molecule in the body. The Gnc molecule was designed for DNA vaccines and optimized with a lysosomal-targeting membrane protein (LAMP) strategy. The effects of GP-derived Th epitopes and multiepitope vaccines were initially verified in animals. Our research has resulted in the design of two vaccines based on effective antiviral immune targets. The effectiveness of molecular therapies has also been preliminarily demonstrated in silico and in laboratory animals, which lays a foundation for the application of a vaccines strategy in the field of antivirals.
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Affiliation(s)
- Dongbo Jiang
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
- Department of Microbiology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China;
| | - Junqi Zhang
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Wenyang Shen
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Yubo Sun
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Zhenjie Wang
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Jiawei Wang
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Jinpeng Zhang
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Guanwen Zhang
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Gefei Zhang
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Yueyue Wang
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Sirui Cai
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Jiaxing Zhang
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Yongkai Wang
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Ruibo Liu
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Tianyuan Bai
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Yuanjie Sun
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Shuya Yang
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Zilu Ma
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Zhikui Li
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Jijin Li
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Chenjin Ma
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
| | - Linfeng Cheng
- Department of Microbiology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China;
| | - Baozeng Sun
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
- Yingtan Detachment, Jiangxi General Hospital, Chinese People’s Armed Police Force, Nanchang 330001, China
| | - Kun Yang
- Department of Immunology, The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (D.J.); (J.Z.); (W.S.); (Y.S.); (Z.W.); (J.W.); (J.Z.); (G.Z.); (G.Z.); (Y.W.); (S.C.); (J.Z.); (Y.W.); (R.L.); (T.B.); (Y.S.); (S.Y.); (Z.M.); (Z.L.); (J.L.); (C.M.)
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Ji H, Li K, Shang M, Wang Z, Liu Q. The 2016 Severe Floods and Incidence of Hemorrhagic Fever With Renal Syndrome in the Yangtze River Basin. JAMA Netw Open 2024; 7:e2429682. [PMID: 39172449 PMCID: PMC11342140 DOI: 10.1001/jamanetworkopen.2024.29682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 06/28/2024] [Indexed: 08/23/2024] Open
Abstract
Importance Hemorrhagic fever with renal syndrome (HFRS), a neglected zoonotic disease, has received only short-term attention in postflood prevention and control initiatives, possibly because of a lack of evidence regarding the long-term association of flooding with HFRS. Objectives To quantify the association between severe floods and long-term incidence of HFRS in the Yangtze River basin and to examine the modifying role of geographical factors in this association. Design, Setting, and Participants This cross-sectional study collected data on HFRS cases between July 1, 2013, and June 30, 2019, from 58 cities in 4 provinces (Anhui, Hubei, Hunan, and Jiangxi) in the Yangtze River basin of China, with a breakpoint of flooding in July 2016, generating monthly data. The 3 years after July 2016 were defined as the postflood period, while the 3 years before the breakpoint were defined as the control period. Statistical analysis was performed from October to December 2023. Exposures City-level monthly flooding, elevation, ruggedness index, and closest distance from each city to the Yangtze River and its tributaries. Main Outcomes and Measures The primary outcomes were the number of city-level monthly HFRS cases and the number of type 1 (spring or summer) and type 2 (autumn or winter) HFRS cases. Results A total of 11 745 patients with HFRS were reported during the study period: 5216 patients (mean [SD] age, 47.1 [16.2] years; 3737 men [71.6%]) in the control period and 6529 patients (mean [SD] age, 49.8 [15.8] years; 4672 men [71.6%]) in the postflood period. The pooled effects of interrupted time series analysis indicated a long-term association between flooding and HFRS incidence (odds ratio, 1.38; 95% CI, 1.13-1.68), with type 1 cases being at highest risk (odds ratio, 1.71; 95% CI, 1.40-2.09). The metaregression results indicated that elevation and ruggedness index were negatively associated with the risk of HFRS, while the distance to rivers interacted with these associations. Conclusions and Relevance This cross-sectional study of the long-term association between flooding and HFRS incidence, as well as the modification effects of geographical factors, suggests that severe floods were associated with an increased risk of HFRS within 3 years. This study provides evidence for the development of HFRS prevention and control strategies after floods.
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Affiliation(s)
- Haoqiang Ji
- Department of Vector Control, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong Province, Jinan, People’s Republic of China
- 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, Changping District, Beijing, People’s Republic of China
- World Health Organization Collaborating Centre for Vector Surveillance and Management, Changping District, Beijing, People’s Republic of China
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong Province, Jinan, People’s Republic of China
- Shandong University Climate Change and Health Center, Shandong Province, Jinan, People’s Republic of China
| | - Ke Li
- 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, Changping District, Beijing, People’s Republic of China
- World Health Organization Collaborating Centre for Vector Surveillance and Management, Changping District, Beijing, People’s Republic of China
| | - Meng Shang
- Department of Vector Control, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong Province, Jinan, People’s Republic of China
- 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, Changping District, Beijing, People’s Republic of China
- World Health Organization Collaborating Centre for Vector Surveillance and Management, Changping District, Beijing, People’s Republic of China
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong Province, Jinan, People’s Republic of China
- Shandong University Climate Change and Health Center, Shandong Province, Jinan, People’s Republic of China
| | - Zhenxu Wang
- 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, Changping District, Beijing, People’s Republic of China
- World Health Organization Collaborating Centre for Vector Surveillance and Management, Changping District, Beijing, People’s Republic of China
| | - Qiyong Liu
- Department of Vector Control, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong Province, Jinan, People’s Republic of China
- 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, Changping District, Beijing, People’s Republic of China
- World Health Organization Collaborating Centre for Vector Surveillance and Management, Changping District, Beijing, People’s Republic of China
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong Province, Jinan, People’s Republic of China
- Shandong University Climate Change and Health Center, Shandong Province, Jinan, People’s Republic of China
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Shartova N, Korennoy F, Zelikhina S, Mironova V, Wang L, Malkhazova S. Spatial and temporal patterns of haemorrhagic fever with renal syndrome (HFRS) and the impact of environmental drivers in a border area of the Russian Far East. Zoonoses Public Health 2024; 71:489-502. [PMID: 38396153 DOI: 10.1111/zph.13118] [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: 07/05/2023] [Revised: 02/08/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024]
Abstract
AIMS Haemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease transmitted by rodents. The distribution of HFRS in the European part of Russia has been studied quite well; however, much less is known about the endemic area in the Russian Far East. The mutual influence of the epidemic situation in the border regions and the possibility of cross-border transmission of infection remain poorly understood. This study aims to identify the spatiotemporal hot spots of the incidence and the impact of environmental drivers on the HFRS distribution in the Russian Far East. METHODS AND RESULTS A two-scale study design was performed. Kulldorf's spatial scan statistic was used to conduct spatiotemporal analysis at a regional scale from 2000 to 2020. In addition, an ecological niche model based on maximum entropy was applied to analyse the contribution of various factors and identify spatial favourability at the local scale. One spatiotemporal cluster that existed from 2002 to 2011 and located in the border area and one pure temporal cluster from 2004 to 2007 were revealed. The best suitability for orthohantavirus persistence was found along rivers, including those at the Chinese-Russian border, and was mainly explained by land cover, NDVI (as an indicator of vegetation density and greenness) and elevation. CONCLUSIONS Despite the stable incidence in recent years in, targeted prevention strategies are still needed due to the high potential for HRFS distribution in the southeast of the Russian Far East.
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Affiliation(s)
- Natalia Shartova
- International Laboratory of Landscape Ecology, Higher School of Economics, Moscow, Russia
| | - Fedor Korennoy
- FGBI Federal Center for Animal Health (FGBI ARRIAH), mkr. Yurevets, Vladimir, Russia
| | | | - Varvara Mironova
- Faculty of Geography, Lomonosov Moscow State University, Moscow, Russia
| | - Li Wang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
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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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Hu H, Zhan J, Chen W, Yang Y, Jiang H, Zheng X, Li J, Hu F, Yu D, Li J, Yang X, Zhang Y, Wang X, Bi Z, Liang Y, Shen H, Du H, Lian J. Development and validation of a novel death risk stratification scale in patients with hemorrhagic fever with renal syndrome: a 14-year ambispective cohort study. Clin Microbiol Infect 2024; 30:387-394. [PMID: 37952580 DOI: 10.1016/j.cmi.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 11/03/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES To develop and validate a simple and effective death risk stratification scale for hemorrhagic fever with renal syndrome (HFRS). METHODS In this ambispective cohort study, we investigated the epidemiological and clinical data of 2245 patients with HFRS (1873 enrolled retrospectively and constituting the training cohort, 372 prospectively recruited as the validation cohort) from September 2008 to December 2021, and identified independent risk factors for 30-day death of HFRS. Using logistic regression analysis, a nomogram prediction model was established and was further simplified into a novel scoring scale. Calibration plot, receiver operating characteristic curve, net reclassification index, integrated discrimination index, and decision curve analysis were used to assess the calibration, discrimination, precision, and clinical utility in both training and validation cohorts. RESULTS Of 2245 patients with HFRS, 132 (5.9%) died during hospitalization. The nomogram prediction model and scoring scale were developed using six predictors: comorbid hypertension, hypotensive shock, hypoxemia, neutrophils, aspartate aminotransferase, and activated partial thromboplastin time. Both the scale and nomogram were well calibrated (near-diagonal calibration curves) and demonstrated significant predictive values (areas under receiver operating characteristic curves >0.9, sensitivity and specificity >90% in the training cohort and >84% in the validation cohort). The simplified scoring scale demonstrated equivalent discriminative ability to the nomogram, with net reclassification index and integrated discrimination index of 0.022 and 0.007 in the training cohort, 0.126 and 0.022 in the validation cohort. Decision curve analysis graphically represented significant clinical utility and comparable net benefits of the nomogram and scoring scale across a range of threshold probabilities. DISCUSSION This evidence-based, factor-weighted, accurate score could help clinicians swiftly stratify HFRS mortality risk and facilitate the implementation of patient triage and tiered medical services during epidemic peaks.
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Affiliation(s)
- Haifeng Hu
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Jiayi Zhan
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Wenjing Chen
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China; Department of Infectious Diseases, Affiliated Hospital of Yan'an University, Yan'an, China
| | - Yali Yang
- Department of Inpatient Ultrasound, Second Affiliated Hospital of Xi'an Medical University, Xi'an, China
| | - Hong Jiang
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Xuyang Zheng
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Jiayu Li
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Fei Hu
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China; Department of Infectious Diseases, 985th Hospital of Chinese People's Liberation Army, Taiyuan, China
| | - Denghui Yu
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China; Department of Intensive Care Unit, General Hospital of Southern Theater Command, Guangzhou, China
| | - Jing Li
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Xiaofei Yang
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Ye Zhang
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Xiaoyan Wang
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Zhanhu Bi
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Yan Liang
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China; College of Life Sciences, Northwest University, Xi'an, China
| | - Huanjun Shen
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Hong Du
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Jianqi Lian
- Center for Infectious Diseases, Second Affiliated Hospital of Air Force Medical University, Xi'an, China.
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Wang Z, Yang C, Li B, Wu H, Xu Z, Feng Z. Comparison of simulation and predictive efficacy for hemorrhagic fever with renal syndrome incidence in mainland China based on five time series models. Front Public Health 2024; 12:1365942. [PMID: 38496387 PMCID: PMC10941340 DOI: 10.3389/fpubh.2024.1365942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/20/2024] [Indexed: 03/19/2024] Open
Abstract
Background Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic infectious disease commonly found in Asia and Europe, characterized by fever, hemorrhage, shock, and renal failure. China is the most severely affected region, necessitating an analysis of the temporal incidence patterns in the country. Methods We employed Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Nonlinear AutoRegressive with eXogenous inputs (NARX), and a hybrid CNN-LSTM model to model and forecast time series data spanning from January 2009 to November 2023 in the mainland China. By comparing the simulated performance of these models on training and testing sets, we determined the most suitable model. Results Overall, the CNN-LSTM model demonstrated optimal fitting performance (with Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) of 93.77/270.66, 7.59%/38.96%, and 64.37/189.73 for the training and testing sets, respectively, lower than those of individual CNN or LSTM models). Conclusion The hybrid CNN-LSTM model seamlessly integrates CNN's data feature extraction and LSTM's recurrent prediction capabilities, rendering it theoretically applicable for simulating diverse distributed time series data. We recommend that the CNN-LSTM model be considered as a valuable time series analysis tool for disease prediction by policy-makers.
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Affiliation(s)
- ZhenDe Wang
- School of Public Health, Shandong Second Medical University, Weifang, China
| | - ChunXiao Yang
- School of Public Health, Shandong Second Medical University, Weifang, China
| | - Bing Li
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - HongTao Wu
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhen Xu
- Chinese Center for Disease Control and Prevention, Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China
| | - ZiJian Feng
- Chinese Preventive Medicine Association, Beijing, 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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