Evidence map›Paper›PMID 40616187›Full record

ArticleInfectious diseases of poverty2025

Zoonotic diseases in China: epidemiological trends, incidence forecasting, and comparative analysis between real-world surveillance data and Global Burden of Disease 2021 estimates.

Yun-Fei Zhang, Shi-Zhu Li, Shi-Wen Wang, Di Mu, Xi Chen, Sheng Zhou, Hai-Jian Zhou, Tian Qin, Qin Liu, Shan Lv and 11 more

Abstract readComparative Study
In one paragraph

Article in Infectious diseases of poverty, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Yun-Fei Zhang *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, Beijing, 102206, China.
Shi-Zhu Li *National 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 Prevention, Shanghai, 200025, China.
Shi-Wen Wang *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Di MuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Xi ChenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Sheng ZhouNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Hai-Jian ZhouNational 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, Beijing, 102206, China.
Tian QinNational 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, Beijing, 102206, China.
Qin LiuNational 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 Prevention, Shanghai, 200025, China.
Shan LvNational 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 Prevention, Shanghai, 200025, China.
Yan LuNational 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 Prevention, Shanghai, 200025, China.
Ji-Chun WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Yu QinNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Guo-Bing YangGansu Provincial Center for Disease Control and Prevention, Gansu Provincial Academy of Preventive Medicine, Lanzhou, 730000, Gansu, China.
Yong-Jun LiGansu Provincial Center for Disease Control and Prevention, Gansu Provincial Academy of Preventive Medicine, Lanzhou, 730000, Gansu, China.
Jian-Yun SunGansu Provincial Center for Disease Control and Prevention, Gansu Provincial Academy of Preventive Medicine, Lanzhou, 730000, Gansu, China.
Xiao-Nong ZhouNational 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 Prevention, Shanghai, 200025, China.
Mai-Geng ZhouNational Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100050, China.
Can-Jun ZhengNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China. zhengcj@chinacdc.cn.
Biao KanNational 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, Beijing, 102206, China. kanbiao@icdc.cn.
Shun-Xian ZhangLonghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China. zhangshunxian110@163.com.ORCID http://orcid.org/0000-0002-8929-4433

Funding

Excellent Academic Leaders Program of Shanghai Science and Technology Commission 22XD1423500International Joint Laboratory on Tropical Diseases Control in Greater Mekong Subregion from Shanghai Municipality Government 21410750200Multidisciplinary Innovation Team of Traditional Chinese Medicine of China ZYYCXTD-D-202208National Parasite Resource Bank NPRC-2019-194-30Science and technology development project of Shanghai University of traditional Chinese medicine 24BZH07Shanghai Natural Science Foundation 22Y11920200Shanghai Natural Science Foundation 23ZR1464000the National Key Research and Development Program of China 2024YFC2310902Three-Year Initiative Plan for Strengthening Public Health System Construction in Shanghai GWVI-11.1-12
6 · The paper itself

Abstract

backgroundZoonotic 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.

methodsThis 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.

resultsFrom 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).

conclusionThe 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.

Indexed as

ZoonosesAdolescentAdultAgedAnimalsChildChild, PreschoolChinaFemaleForecastingGlobal Burden of DiseaseHumansIncidenceInfantInfant, NewbornMaleChinaEpidemiological trendIncidence rateOne healthZoonotic diseases

Identifiers

PMID40616187
PMCPMC12231708

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