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A critical barrier to targeted and effective control lies in the lack of high-resolution national risk maps and limited understanding of how climate change modulates transmission dynamics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Here, we address these gaps by integrating multi-source epidemiological and environmental data to model and map high-resolution echinococcosis transmission risk across China. We further project its spatiotemporal evolution under four climate change scenarios (SSP126, SSP245, SSP370, SSP585) from 2040 to 2100, employing a One Health framework to assess echinococcosis transmission risk. Our model exhibits robust predictive performance, identifying elevation, annual precipitation, precipitation seasonality, isothermality, and average monthly precipitation in January as key driving factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Results reveal concentrated high-risk regions in western and northern China, including Sichuan, Qinghai, Tibet, Xinjiang, and Gansu provinces, which are characterized by pastoral economies, socioeconomic underdevelopment, and constrained healthcare access. Future projections show a concerning expansion of high and very high transmission risk regions across all scenarios, with the most significant increase under the high-emission SSP585 pathway by the late twenty-first century.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings clarify the current echinococcosis risk landscape and its environmental determinants while providing forward-looking, spatially explicit evidence. This work establishes a science-based foundation for optimizing resource allocation, designing adaptive prevention strategies, and enhancing health equity within a One Health framework, particularly for climate-vulnerable and resource-limited settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13071-026-07518-7","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T02:44:35Z","timestamp":1781750675000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Risk prediction of echinococcosis in China under climate change: a One Health approach"],"prefix":"10.1186","volume":"19","author":[{"given":"Tengyue","family":"Diao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"La","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunyi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4656-7152","authenticated-orcid":false,"given":"Bo","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"7518_CR1","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1016\/S0140-6736(03)14573-4","volume":"362","author":"DP McManus","year":"2003","unstructured":"McManus DP, Zhang W, Li J, Bartley PB. 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