[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1434":3,"related-1434":77},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":22,"classification":23,"search_phrases":39,"slug":40,"view_count":41,"doi":42,"paper":43,"created_at":76},1434,"Predicting the potential distribution of hemorrhagic fever with renal syndrome in Southwest China using the ecological niche modeling","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13071-026-07631-7","Abstract Background Hemorrhagic fever with renal syndrome (HFRS) severely burdens China's public health. With its complex topography and rich biodiversity, Southwest China has historically incubated this natural focal disease. Currently, traditional environmental drivers of transmission here are increasingly eclipsed by anthropogenic forces, notably population agglomeration. Driven by these shifting dynamics, HFRS incidence and endemic ranges are expanding, yet systematic, regional-scale assessments remain scarce. Consequently, this study employs ecological niche modeling to map potential high-risk zones. Ultimately, we aim to explore the core mechanisms driving HFRS prevalence amid the complex interplay of natural environments and human activities. Methods Surveillance data of HFRS cases in Southwest China from 2014 to 2023 were obtained from the China Information System for Disease Control and Prevention (CISDCP). A Maximum Entropy (MaxEnt) model was constructed by integrating occurrence records with multisource environmental variables, including meteorological, socioeconomic, and land cover factors. Model performance was evaluated using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the Jackknife test was employed to quantify the contribution of each variable. Results The MaxEnt model demonstrated robust predictive performance with a mean AUC of 0.902. Population density was identified as the predominant predictor (73.5% contribution), followed by the Normalized Difference Vegetation Index (NDVI) in May (9.2%) and annual mean temperature (5.8%). The spatial distribution of risk exhibited a core aggregation with sporadic dispersion pattern, with high-risk zones concentrated in the Chengdu-Chongqing urban agglomeration and localized clusters in Yunnan Province. Response curves revealed a sigmoidal positive correlation between population density and disease risk. Meteorological factors, such as temperature and precipitation, exhibited non-linear inverted U-shaped or U-shaped relationships, constraining the spatial boundaries of transmission. Conclusions The spatial heterogeneity of HFRS in Southwest China is jointly driven by anthropogenic activities and natural environmental constraints. Human population density acts as the primary amplifier of transmission risk, supporting the human behavior amplification effect in natural focal diseases. These findings suggest a strategic shift from blanket prevention to precision control, prioritizing active surveillance in densely populated urban fringes and areas undergoing infrastructure development.","背景 肾综合征出血热（HFRS）严重威胁中国公共卫生安全。西南地区地形复杂、生物多样性丰富，历来是该自然疫源性疾病的温床。当前，该地区传统的环境传播驱动因素日益被人为力量——尤其是人口集聚——所掩盖。在此动态变化驱动下，HFRS发病率和疫源地范围持续扩大，但系统性的区域尺度评估仍较为匮乏。为此，本研究采用生态位模型绘制潜在高风险区域图，旨在探索自然环境与人类活动复杂交互作用下HFRS流行的核心机制。方法 2014至2023年西南地区HFRS病例监测数据来源于中国疾病预防控制信息系统（CISDCP）。将发生记录与多源环境变量（包括气象、社会经济和土地覆盖因子）整合，构建最大熵（MaxEnt）模型。采用受试者工作特征（ROC）曲线下面积（AUC）评估模型性能，并运用刀切法（Jackknife test）量化各变量的贡献率。结果 MaxEnt模型表现出稳健的预测性能，平均AUC为0.902。人口密度被确定为主导预测因子（贡献率73.5%），其次为5月归一化植被指数（NDVI）（9.2%）和年均温（5.8%）。风险空间分布呈现核心聚集与零星散布并存格局，高风险区域集中于成渝城市群及云南省局部地区。响应曲线显示，人口密度与疾病风险呈S形正相关关系。气象因子（如温度和降水）则呈现非线性倒U形或U形关系，约束了传播的空间边界。结论 西南地区HFRS的空间异质性由人类活动与自然环境约束共同驱动。人口密度是传播风险的主要放大器，支持了自然疫源性疾病中的人类行为放大效应。上述发现提示防控策略应从全面覆盖式预防转向精准化防控。",null,"Parasites & Vectors","2026-09-01T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦肾综合征出血热的生态位建模，属于公共卫生领域，与三农、农业信息化等主题无直接关联。",[19],{"name":10,"url":6},[21],"医学地理",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":27},"needs_review","2026-10-22.2","rules",{"candidates":28,"agricultural_evidence":14,"reason":38},[29],{"code":30,"score":31,"supported":32,"title":33,"abstract":34,"tags":37},"H6",6,true,[],[35,36],"生物多样性","biodiversity",[],"农业相关性需要复核",[],"Predictingthepotentialdistributionofhemo-1434",1,"10.1186\u002Fs13071-026-07631-7",{"doi":42,"openalex_id":44,"authors":45,"venue":10,"cited_by_count":15,"oa_url":6,"card":68,"direction":74,"ingested_from":75},"W7140237415",[46,48,50,52,54,56,58,60,63,66],{"name":47,"orcid":9},"Danjie Zhang",{"name":49,"orcid":9},"Zhiruo Zhu",{"name":51,"orcid":9},"Yanding Wang",{"name":53,"orcid":9},"Linyuan Zhu",{"name":55,"orcid":9},"Zitong Zheng",{"name":57,"orcid":9},"Ruidong Qu",{"name":59,"orcid":9},"Feng Shao",{"name":61,"orcid":62},"Qiulan Chen","https:\u002F\u002Forcid.org\u002F0000-0001-6711-2304",{"name":64,"orcid":65},"Yuanyong Xu","https:\u002F\u002Forcid.org\u002F0009-0009-9709-6881",{"name":67,"orcid":9},"Wenyi Zhang",{"tldr":69,"method":70,"finding":71,"direction":72,"opportunity":73},"利用生态位模型预测中国西南地区肾综合征出血热的潜在分布，识别高风险区及关键驱动因素。","使用MaxEnt模型，整合气象、社会经济和土地覆盖等多源环境变量，结合2014-","人口密度是主要预测因子（贡献73.5%），高风险区集中于成渝城市群，人口密度与风险呈S型正相关。","其他","可探索将人类行为动态（如人口流动）纳入模型，或结合气候情景预测未来风险变化，以优化精准防控策略。","农业遥感与作物表型","openalex","2026-09-02T23:30:51.258632Z",{"total":15,"page":41,"page_size":41,"items":78},[]]