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Mapping the spatial distribution of Aedes aegypti and Aedes albopictus
Ding, Fangyu1,2; Fu, Jingying1,2; Jiang, Dong1,2; Hao, Mengmeng1,2; Lin, Gang1
2018-02-01
Source PublicationACTA TROPICA
ISSN0001-706X
Volume178Pages:155-162
Corresponding AuthorJiang, Dong(jiangd@igsnrr.ac.cn)
AbstractMosquito-borne infectious diseases, such as Rift Valley fever, Dengue, Chikungunya and Zika, have caused mass human death with the transnational expansion fueled by economic globalization. Simulating the distribution of the disease vectors is of great importance in formulating public health planning and disease control strategies. In the present study, we simulated the global distribution of Aedes aegypti and Aedes albopictus at a 5 x 5 km spatial resolution with high-dimensional multidisciplinary datasets and machine learning methods Three relatively popular and robust machine learning models, including support vector machine (SVM), gradient boosting machine (GBM) and random forest (RF), were used. During the fine-tuning process based on training datasets of A. aegypti and A. albopictus, RF models achieved the highest performance with an area under the curve (AUC) of 0.973 and 0.974, respectively, followed by GBM (AUC of 0.971 and 0.972, respectively) and SVM (AUC of 0.963 and 0.964, respectively) models. The simulation difference between RF and GBM models was not statistically significant (p > 0.05) based on the validation datasets, whereas statistically significant differences (p < 0.05) were observed for RF and GBM simulations compared with SVM simulations. From the simulated maps derived from RF models, we observed that the distribution of A. albopictus was wider than that of A. aegypti along a latitudinal gradient. The discriminatory power of each factor in simulating the global distribution of the two species was also analyzed. Our results provided fundamental information for further study on disease transmission simulation and risk assessment.
KeywordGlobal distribution Aedes aegypti Aedes albopictus Multidisciplinary datasets Machine learning models
DOI10.1016/j.actatropica.2017.11.020
WOS KeywordZIKA VIRUS ; CHIKUNGUNYA VIRUS ; DIPTERA-CULICIDAE ; DENGUE ; SPREAD ; FEVER ; PATTERNS ; MOSQUITO ; CLIMATE ; VECTOR
Indexed BySCI
Language英语
Funding ProjectMinistry of Science and Technology of China[2016YFC1201300]
Funding OrganizationMinistry of Science and Technology of China
WOS Research AreaParasitology ; Tropical Medicine
WOS SubjectParasitology ; Tropical Medicine
WOS IDWOS:000423644300025
PublisherELSEVIER SCIENCE BV
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Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/56936
Collection中国科学院地理科学与资源研究所
Corresponding AuthorJiang, Dong
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Ding, Fangyu,Fu, Jingying,Jiang, Dong,et al. Mapping the spatial distribution of Aedes aegypti and Aedes albopictus[J]. ACTA TROPICA,2018,178:155-162.
APA Ding, Fangyu,Fu, Jingying,Jiang, Dong,Hao, Mengmeng,&Lin, Gang.(2018).Mapping the spatial distribution of Aedes aegypti and Aedes albopictus.ACTA TROPICA,178,155-162.
MLA Ding, Fangyu,et al."Mapping the spatial distribution of Aedes aegypti and Aedes albopictus".ACTA TROPICA 178(2018):155-162.
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