中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Mapping the transmission risk of Zika virus using machine learning models

文献类型:期刊论文

作者Jiang, Dong1,2; Hao, Mengmeng1,2; Ding, Fangyu1,2; Fu, Jingying1,2; Li, Meng1,2
刊名ACTA TROPICA
出版日期2018-09-01
卷号185页码:391-399
关键词Zika virus Transmission risk Machine learning Significant differences Prediction uncertainty
ISSN号0001-706X
DOI10.1016/j.actatropica.2018.06.021
通讯作者Ding, Fangyu(dingfy.17b@igsnrr.ac.cn)
英文摘要Zika virus, which has been linked to severe congenital abnormalities, is exacerbating global public health problems with its rapid transnational expansion fueled by increased global travel and trade. Suitability mapping of the transmission risk of Zika virus is essential for drafting public health plans and disease control strategies, which are especially important in areas where medical resources are relatively scarce. Predicting the risk of Zika virus outbreak has been studied in recent years, but the published literature rarely includes multiple model comparisons or predictive uncertainty analysis. Here, three relatively popular machine learning models including backward propagation neural network (BPNN), gradient boosting machine (GBM) and random forest (RF) were adopted to map the probability of Zika epidemic outbreak at the global level, pairing high-dimensional multidisciplinary covariate layers with comprehensive location data on recorded Zika virus infection in humans. The results show that the predicted high-risk areas for Zika transmission are concentrated in four regions: Southeastern North America, Eastern South America, Central Africa and Eastern Asia. To evaluate the performance of machine learning models, the 50 modeling processes were conducted based on a training dataset. The BPNN model obtained the highest predictive accuracy with a 10-fold cross-validation area under the curve (AUC) of 0.966 [95% confidence interval (CI) 0.965-0.967], followed by the GBM model (10-fold cross-validation AUC = 0.964[0.963-0.965]) and the RF model (10-fold cross-validation AUC = 0.963[0.962-0.964]). Based on training samples, compared with the BPNN-based model, we find that significant differences (p = 0.0258* and p = 0.0001***, respectively) are observed for prediction accuracies achieved by the GBM and RF models. Importantly, the prediction uncertainty introduced by the selection of absence data was quantified and could provide more accurate fundamental and scientific information for further study on disease transmission prediction and risk assessment.
WOS关键词AEDES-AEGYPTI DIPTERA ; VECTOR-BORNE DISEASE ; SPATIAL-DISTRIBUTION ; ALBOPICTUS ; OUTBREAK ; HISTORY ; BRAZIL ; MICRONESIA ; ISOLATIONS ; CULICIDAE
资助项目Ministry of Science and Technology of China[2016YFC1201300]
WOS研究方向Parasitology ; Tropical Medicine
语种英语
WOS记录号WOS:000440126000054
出版者ELSEVIER SCIENCE BV
资助机构Ministry of Science and Technology of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/54447]  
专题中国科学院地理科学与资源研究所
通讯作者Ding, Fangyu
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Jiang, Dong,Hao, Mengmeng,Ding, Fangyu,et al. Mapping the transmission risk of Zika virus using machine learning models[J]. ACTA TROPICA,2018,185:391-399.
APA Jiang, Dong,Hao, Mengmeng,Ding, Fangyu,Fu, Jingying,&Li, Meng.(2018).Mapping the transmission risk of Zika virus using machine learning models.ACTA TROPICA,185,391-399.
MLA Jiang, Dong,et al."Mapping the transmission risk of Zika virus using machine learning models".ACTA TROPICA 185(2018):391-399.

入库方式: OAI收割

来源:地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。