Zoonotic diseases in China: epidemiological trends, incidence forecasting, and comparative analysis between real-world surveillance data and Global Burden of Disease 2021 estimates
Abstract Background Zoonotic diseases remain a significant public health challenge in China. This study examines the temporal trends, disease burden, and demographic patterns of major zoonoses from 2010 to 2023. Methods This study analyzed data from China’s National Notifiable Infectious Disease Rep...
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2025-07-01
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| Series: | Infectious Diseases of Poverty |
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| Online Access: | https://doi.org/10.1186/s40249-025-01335-3 |
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| author | Yun-Fei Zhang Shi-Zhu Li Shi-Wen Wang Di Mu Xi Chen Sheng Zhou Hai-Jian Zhou Tian Qin Qin Liu Shan Lv Yan Lu Ji-Chun Wang Yu Qin Guo-Bing Yang Yong-Jun Li Jian-Yun Sun Xiao-Nong Zhou Mai-Geng Zhou Can-Jun Zheng Biao Kan Shun-Xian Zhang |
| author_facet | Yun-Fei Zhang Shi-Zhu Li Shi-Wen Wang Di Mu Xi Chen Sheng Zhou Hai-Jian Zhou Tian Qin Qin Liu Shan Lv Yan Lu Ji-Chun Wang Yu Qin Guo-Bing Yang Yong-Jun Li Jian-Yun Sun Xiao-Nong Zhou Mai-Geng Zhou Can-Jun Zheng Biao Kan Shun-Xian Zhang |
| author_sort | Yun-Fei Zhang |
| collection | DOAJ |
| description | Abstract Background Zoonotic diseases remain a significant public health challenge in China. This study examines the temporal trends, disease burden, and demographic patterns of major zoonoses from 2010 to 2023. Methods This study analyzed data from China’s National Notifiable Infectious Disease Reporting System (NNIDRS, 2010–2023) on nine major zoonoses, including echinococcosis, brucellosis, leptospirosis, anthrax, leishmaniasis, encephalitis (Japanese encephalitis), hemorrhagic fever, rabies, and schistosomiasis. Joinpoint regression was applied to assess annual trends in incidence rates, while autoregressive integrated moving average (ARIMA) and exponential smoothing models were used to forecast incidence trends from 2024 to 2035. To assess the performance of the Global Burden of Disease (GBD) 2021 model in China, disease-specific multipliers—defined as the ratio of GBD estimates to national surveillance data—along with their corresponding 95% confidence intervals (CIs) were calculated to quantify discrepancies and evaluate the consistency between modeled estimates and empirical observations. Results From 2010 to 2023, the incidence rates of leptospirosis [average annual percent change (AAPC) = − 5.527%, 95% CI: − 11.054, − 0.485], encephalitis (AAPC = − 16.934%, 95% CI: − 23.690, − 11.245), hemorrhagic fever (AAPC = − 5.384%, 95% CI: − 7.754, − 2.924), rabies (AAPC = − 20.428%, 95% CI: − 21.076, − 19.841), and schistosomiasis (AAPC = − 28.378%, 95% CI: − 40.688, − 15.656) showed a declining trend in China. In contrast, brucellosis exhibited a modest but statistically significant increase (AAPC = 0.151%, 95% CI: 0.031, 0.272). For most diseases, incidence rates were consistently higher in males than females. Children aged 0–5 years accounted for a substantial proportion of encephalitis and leishmaniasis cases, while adults aged 14–65 years represented the primary affected group across the majority of diseases. Occupationally, farmers and herders were the most affected populations. Compared to national surveillance data, the GBD 2021 model substantially overestimated the burden of zoonotic diseases in China, particularly for echinococcosis (by 3.611–7.409 times) and leishmaniasis (by 3.054–10.500 times). Conclusion The study revealed significant decline in several major zoonoses in China, while brucellosis showed a continued upward trend. These findings highlight the urgent need for a One Health-based prevention and control system to interrupt cross-species transmission and reduce long-term public health risks. |
| format | Article |
| id | doaj-art-e38bb4a99d0c4bde8a78a01a7afc96af |
| institution | Kabale University |
| issn | 2049-9957 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
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| series | Infectious Diseases of Poverty |
| spelling | doaj-art-e38bb4a99d0c4bde8a78a01a7afc96af2025-08-20T03:42:02ZengBMCInfectious Diseases of Poverty2049-99572025-07-0114112110.1186/s40249-025-01335-3Zoonotic diseases in China: epidemiological trends, incidence forecasting, and comparative analysis between real-world surveillance data and Global Burden of Disease 2021 estimatesYun-Fei Zhang0Shi-Zhu Li1Shi-Wen Wang2Di Mu3Xi Chen4Sheng Zhou5Hai-Jian Zhou6Tian Qin7Qin Liu8Shan Lv9Yan Lu10Ji-Chun Wang11Yu Qin12Guo-Bing Yang13Yong-Jun Li14Jian-Yun Sun15Xiao-Nong Zhou16Mai-Geng Zhou17Can-Jun Zheng18Biao Kan19Shun-Xian Zhang20National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research On Tropical Diseases, National Institute of Parasitic, Diseases of Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research On Tropical Diseases, National Institute of Parasitic, Diseases of Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research On Tropical Diseases, National Institute of Parasitic, Diseases of Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research On Tropical Diseases, National Institute of Parasitic, Diseases of Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and PreventionGansu Provincial Center for Disease Control and Prevention, Gansu Provincial Academy of Preventive MedicineGansu Provincial Center for Disease Control and Prevention, Gansu Provincial Academy of Preventive MedicineGansu Provincial Center for Disease Control and Prevention, Gansu Provincial Academy of Preventive MedicineNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research On Tropical Diseases, National Institute of Parasitic, Diseases of Chinese Center for Disease Control and PreventionNational Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and PreventionNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and PreventionLonghua Hospital, Shanghai University of Traditional Chinese MedicineAbstract Background Zoonotic diseases remain a significant public health challenge in China. This study examines the temporal trends, disease burden, and demographic patterns of major zoonoses from 2010 to 2023. Methods This study analyzed data from China’s National Notifiable Infectious Disease Reporting System (NNIDRS, 2010–2023) on nine major zoonoses, including echinococcosis, brucellosis, leptospirosis, anthrax, leishmaniasis, encephalitis (Japanese encephalitis), hemorrhagic fever, rabies, and schistosomiasis. Joinpoint regression was applied to assess annual trends in incidence rates, while autoregressive integrated moving average (ARIMA) and exponential smoothing models were used to forecast incidence trends from 2024 to 2035. To assess the performance of the Global Burden of Disease (GBD) 2021 model in China, disease-specific multipliers—defined as the ratio of GBD estimates to national surveillance data—along with their corresponding 95% confidence intervals (CIs) were calculated to quantify discrepancies and evaluate the consistency between modeled estimates and empirical observations. Results From 2010 to 2023, the incidence rates of leptospirosis [average annual percent change (AAPC) = − 5.527%, 95% CI: − 11.054, − 0.485], encephalitis (AAPC = − 16.934%, 95% CI: − 23.690, − 11.245), hemorrhagic fever (AAPC = − 5.384%, 95% CI: − 7.754, − 2.924), rabies (AAPC = − 20.428%, 95% CI: − 21.076, − 19.841), and schistosomiasis (AAPC = − 28.378%, 95% CI: − 40.688, − 15.656) showed a declining trend in China. In contrast, brucellosis exhibited a modest but statistically significant increase (AAPC = 0.151%, 95% CI: 0.031, 0.272). For most diseases, incidence rates were consistently higher in males than females. Children aged 0–5 years accounted for a substantial proportion of encephalitis and leishmaniasis cases, while adults aged 14–65 years represented the primary affected group across the majority of diseases. Occupationally, farmers and herders were the most affected populations. Compared to national surveillance data, the GBD 2021 model substantially overestimated the burden of zoonotic diseases in China, particularly for echinococcosis (by 3.611–7.409 times) and leishmaniasis (by 3.054–10.500 times). Conclusion The study revealed significant decline in several major zoonoses in China, while brucellosis showed a continued upward trend. These findings highlight the urgent need for a One Health-based prevention and control system to interrupt cross-species transmission and reduce long-term public health risks.https://doi.org/10.1186/s40249-025-01335-3Zoonotic diseasesIncidence rateEpidemiological trendOne healthChina |
| spellingShingle | Yun-Fei Zhang Shi-Zhu Li Shi-Wen Wang Di Mu Xi Chen Sheng Zhou Hai-Jian Zhou Tian Qin Qin Liu Shan Lv Yan Lu Ji-Chun Wang Yu Qin Guo-Bing Yang Yong-Jun Li Jian-Yun Sun Xiao-Nong Zhou Mai-Geng Zhou Can-Jun Zheng Biao Kan Shun-Xian Zhang Zoonotic diseases in China: epidemiological trends, incidence forecasting, and comparative analysis between real-world surveillance data and Global Burden of Disease 2021 estimates Infectious Diseases of Poverty Zoonotic diseases Incidence rate Epidemiological trend One health China |
| title | Zoonotic diseases in China: epidemiological trends, incidence forecasting, and comparative analysis between real-world surveillance data and Global Burden of Disease 2021 estimates |
| title_full | Zoonotic diseases in China: epidemiological trends, incidence forecasting, and comparative analysis between real-world surveillance data and Global Burden of Disease 2021 estimates |
| title_fullStr | Zoonotic diseases in China: epidemiological trends, incidence forecasting, and comparative analysis between real-world surveillance data and Global Burden of Disease 2021 estimates |
| title_full_unstemmed | Zoonotic diseases in China: epidemiological trends, incidence forecasting, and comparative analysis between real-world surveillance data and Global Burden of Disease 2021 estimates |
| title_short | Zoonotic diseases in China: epidemiological trends, incidence forecasting, and comparative analysis between real-world surveillance data and Global Burden of Disease 2021 estimates |
| title_sort | zoonotic diseases in china epidemiological trends incidence forecasting and comparative analysis between real world surveillance data and global burden of disease 2021 estimates |
| topic | Zoonotic diseases Incidence rate Epidemiological trend One health China |
| url | https://doi.org/10.1186/s40249-025-01335-3 |
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