Evidence map›Paper›PMID 42693475›Full record

ArticleInfectious diseases of poverty2026

Climate change reshapes the potential transmission risk of human alveolar and cystic echinococcosis in western China: a projection based on MaxEnt models.

Xu Wang, Quzhen Gongsang, Shijie Yang, Ying Wang, Sha Liao, Guangzhong Shi, Jia Liu, Fang Yan, Xiaojuan Zhang, Baixue Liu and 5 more

Abstract read
In one paragraph

Article in Infectious diseases of poverty, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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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

15 authors.

Xu WangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory on Parasite and Vector Biology; World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, 200025, China.
Quzhen GongsangXizang Autonomous Regional Center for Disease Control and Prevention, National Health Commission (NHC) Key Laboratory of Echinococcosis Prevention and Control, Lhasa, 850000, China.
Shijie YangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory on Parasite and Vector Biology; World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, 200025, China.
Ying WangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory on Parasite and Vector Biology; World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, 200025, China.
Sha LiaoSichuan Provincial Center for Disease Control and Prevention, Chengdu, 610041, China.
Guangzhong ShiXinjiang Uygur Autonomous Regional Center for Disease Control and Prevention, Urumqi, 830002, China.
Jia LiuQinghai Provincial Institute for Endemic Diseases Prevention and Control, Xining, 811602, China.
Fang YanNingxia Hui Autonomous Regional Center for Disease Control and Prevention, Yinchuan, 750011, China.
Xiaojuan ZhangGansu Provincial Center for Disease Control and Prevention, Lanzhou, 730020, China.
Baixue LiuNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory on Parasite and Vector Biology; World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, 200025, China.
Yan KuiNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory on Parasite and Vector Biology; World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, 200025, China.
Yixi ZhangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory on Parasite and Vector Biology; World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, 200025, China.
Chuizhao XueNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory on Parasite and Vector Biology; World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, 200025, China.
Liying WangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory on Parasite and Vector Biology; World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, 200025, China.
Shuai HanNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory on Parasite and Vector Biology; World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, 200025, China. hanshuai@nipd.chinacdc.cn.

Funding

National Natural Science Foundation of China 82404325NHC Key Laboratory of Echinococcosis Prevention and Control 2023WZK1002Research Projects of Shanghai Municipal Health Commission 20254Y0176
6 · The paper itself

Abstract

backgroundEchinococcosis, a severe zoonotic parasitic disease, imposes a significant health burden in western China, primarily as alveolar (AE) and cystic (CE) forms caused by Echinococcus multilocularis (Em) and E. granulosus sensu lato (Egsl), respectively. Its transmission, tied to a complex host cycle, is strongly influenced by climatic factors. This study aimed to model the ecological niches of these parasites, validate predictions with epidemiological data, and project the impact of climate change on future transmission risk geography.

methodsUtilizing location data of 1247 human cases diagnosed in western China from 1981 to 2016, obtained from the National Echinococcosis Control Program, and contemporary climate data, we developed optimized Maximum Entropy (MaxEnt) models. This involved defining an appropriate spatial resolution, screening 19 bioclimatic variables to select key predictors (7 for Em, 6 for Egsl), and identifying optimal model parameters. Model performance was evaluated using the Area Under the Curve (AUC) and AUC of 10% Receiver Operating Characteristic (AUCpROC) curve, and validity was assessed via correlation with county-level incidence. Future habitat suitability was projected for multiple periods (2011-2100) under three climate scenarios (SSP-126, SSP-370, SSP-585) generated by 5 global climate models.

resultsThe models demonstrated high predictive accuracy (AUC > 0.92; AUCpROC: 0.084 for Em, 0.070 for Egsl) and a significant positive correlation with incidence (ρ > 0.6, P < 0.0001). Em and Egsl exhibited distinct climatic niches: Em prefers cool, stable climates with moderate summer moisture, while Egsl spreads in areas with dry-cold winters, warm-humid summers, and moderate seasonality. Future projections reveal a spatially heterogeneous reshaping of risk. While overall suitability increases, Em's suitable area is projected to shift toward higher altitudes (e.g., expanding in Qinghai and Xizang but contracting in Ningxia and Gansu). In contrast, Egsl shows widespread outward expansion from current endemic margins, particularly across the Tibetan Plateau. Qinghai and Xizang are identified as key expansion hotspots for both Em and Egsl.

conclusionThe findings confirm distinct ecological drivers for the suitability of Em and Egsl, and indicate that climate change will potentially shift the endemic areas of AE and expand those of CE in western China. This underscores the necessity for forward-looking, spatially targeted surveillance and control strategies, informed by a One Health approach, to mitigate the evolving burden of echinococcosis.

Indexed as

Climate ChangeEchinococcosisEchinococcosis, HepaticAnimalsChinaEchinococcus granulosusEchinococcus multilocularisHumansIncidenceClimate changeEchinococcosisEchinococcusMaxEntOne HealthWestern China

Identifiers

PMID42693475
PMCPMC13543363

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