Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning:a newly data-driven modeling attempt

作     者：Yan-Feng Gong Ling-Qian Zhu Yin-Long Li Li-Juan Zhang Jing-Bo Xue Shang Xia Shan Lv Jing Xu Shi-Zhu Li 

作者机构：National Institute of Parasitic DiseasesChinese Center for Disease Control and PreventionChinese Center for Tropical Diseases ResearchHC Key Laboratory of Parasite and Vector BiologyWHO Collaborating Centre for Tropical DiseasesNational Center for International Research on Tropical DiseasesShanghai 200025China 

出 版 物：《Infectious Diseases of Poverty》 (贫困所致传染病（英文）)

年 卷 期：2021年第10卷第3期

页      面：113-114页

核心收录：

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

基　　金：National Special Science and Technology Project for Major Infection Diseases of China Three-Year Public Health Action Plan of Shanghai, (GWV-10.1-XK13) National Major Science and Technology Projects of China, (2016ZX10004222-004) National Major Science and Technology Projects of China 

主　　题：Schistosomiasis Risk prediction Information value Machine learning China 

摘      要：Background:Schistosomiasis control is striving forward to transmission interruption and even elimination,evidence-lead control is of vital importance to eliminate the hidden dan gers of *** study attempts to ide ntify high risk areas of schistosomiasis in China by using in formation value and machine learni ng.