Mapping the transmission risk of Zika virus using machine learning models

doi:10.1016/j.actatropica.2018.06.021

	Mapping the transmission risk of Zika virus using machine learning models
	Jiang, Dong 1,2; Hao, Mengmeng 1,2; Ding, Fangyu 1,2; Fu, Jingying 1,2; Li, Meng 1,2
刊名	ACTA TROPICA
	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
DOI	10.1016/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.
资助项目	Ministry of Science and Technology of China[2016YFC1201300]
WOS关键词	AEDES-AEGYPTI DIPTERA ; VECTOR-BORNE DISEASE ; SPATIAL-DISTRIBUTION ; ALBOPICTUS ; OUTBREAK ; HISTORY ; BRAZIL ; MICRONESIA ; ISOLATIONS ; CULICIDAE
WOS研究方向	Parasitology ; Tropical Medicine
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000440126000054
资助机构	Ministry of Science and Technology of China
内容类型	期刊论文
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.