中国媒介生物学及控制杂志 ›› 2021, Vol. 32 ›› Issue (4): 503-508.DOI: 10.11853/j.issn.1003.8280.2021.04.024
• 综述 • 上一篇
孙燕群1,2, 张守刚1, 赵姗姗1, 陆墨原1, 张艳1, 王冲1, 李成国1
收稿日期:
2021-02-22
出版日期:
2021-08-20
发布日期:
2021-08-20
作者简介:
孙燕群,男,主管医师,主要从事蚊虫及蚊媒病监测与防制工作,E-mail:sunyq@njcdc.cn
基金资助:
SUN Yan-qun1,2, ZHANG Shou-gang1, ZHAO Shan-shan1, LU Mo-yuan1, ZHANG Yan1, WANG Chong1, LI Cheng-guo1
Received:
2021-02-22
Online:
2021-08-20
Published:
2021-08-20
Supported by:
摘要: 该文主要介绍机器学习在全球蚊虫及蚊媒传染病研究中的应用进展,系统搜索国内外数据库进行文献调研,简单回顾了机器学习的主要方法,对机器学习在蚊虫及蚊媒传染病研究中的几大应用进行了系统总结,主要聚集在蚊虫和蚊媒传染病预测预警、蚊虫图像和声音识别、蚊虫生物学等研究领域,为国内蚊虫及蚊媒传染病防制研究提供新的视角。
中图分类号:
孙燕群, 张守刚, 赵姗姗, 陆墨原, 张艳, 王冲, 李成国. 机器学习在蚊虫及蚊媒传染病研究中的应用进展[J]. 中国媒介生物学及控制杂志, 2021, 32(4): 503-508.
SUN Yan-qun, ZHANG Shou-gang, ZHAO Shan-shan, LU Mo-yuan, ZHANG Yan, WANG Chong, LI Cheng-guo. Application progress of machine learning in mosquito and mosquito-borne disease research[J]. Chines Journal of Vector Biology and Control, 2021, 32(4): 503-508.
[1] 徐承龙, 姜志宽. 蚊虫防制(一):蚊虫的危害与形态分类[J]. 中华卫生杀虫药械, 2006, 12(4):289-293. DOI:10.3969/j.issn.1671-2781.2006.04.027.Xu CL, Jiang ZK. Mosquito control (1): The damage and morphological classification of mosquitoes[J]. Chin J Hyg Insect Equip, 2006, 12(4):289-293. DOI:10.3969/j.issn.1671-2781.2006.04.027. [2] 瞿逢伊. 我国蚊虫种质资源现状及其共享利用[J]. 中国寄生虫学与寄生虫病杂志, 2006, 24增刊:13-16. DOI:10.3969/j.issn.1000-7423.2006.z1.003.Qu FY. Current status, utilization and sharing of mosquito germplasm resources in China[J]. Chin J Parasit Parasit Dis, 2006, 24 Suppl:S13-16. DOI:10.3969/j.issn.1000-7423.2006.z1.003. [3] 张菊仙, 龚正达. 中国蚊类研究概况[J]. 中国媒介生物学及控制杂志, 2008, 19(6):595-599. DOI:10.3969/j.issn.1003-4692.2008.06.047.Zhang JX, Gong ZD. Survey of mosquito research in China[J]. Chin J Vector Biol Control, 2008, 19(6):595-599. DOI:10.3969/j.issn.1003-4692.2008.06.047. [4] 张仪. 新发媒传疾病及其防控[J]. 中国血吸虫病防治杂志, 2012, 24(5):501-504. DOI:10.3969/j.issn.1005-6661.2012.05.001.Zhang Y. Emerging vector-borne diseases and control[J]. Chin J Schisto Control, 2012, 24(5):501-504. DOI:10.3969/j.issn. 1005-6661.2012.05.001. [5] 边长玲, 龚正达. 我国蚊类及其与蚊媒病关系的研究概况[J]. 中国病原生物学杂志, 2009, 4(7):545-551. DOI:10.13350/j.cjpb.2009.07.010.Bian CL, Gong ZD. Mosquitoes and mosquito-borne diseases in China[J]. J Parasit Biol, 2009, 4(7):545-551. DOI:10.13350/j.cjpb.2009.07.010. [6] 郑学礼. 我国蚊媒研究概况[J]. 中国病原生物学杂志, 2014, 9(2):183-187. DOI:10.13350/j.cjpb.140222.Zheng XL. Advances in research on mosquitoes in China[J]. J Parasit Biol, 2014, 9(2):183-187. DOI:10.13350/j.cjpb. 140222. [7] 王梦蕾, 苏昊, 吴焜, 等. 中国蚊媒病流行现状及防治进展[J]. 热带医学杂志, 2012, 12(10):1280-1285.Wang ML, Su H, Wu K, et al. Current status of mosquito-borne diseases in China[J]. J Trop Med, 2012, 12(10):1280-1285. [8] 周志华. 机器学习[M]. 北京:清华大学出版社, 2016:1-14.Zhou ZH. Machine learning[M]. Beijing:Tsinghua University Press, 2016:1-14. [9] Pirracchio R, Cohen MJ, Malenica I, et al. Big data and targeted machine learning in action to assist medical decision in the ICU[J]. Anaesth Crit Care Pain Med, 2019, 38(4):377-384. DOI:10.1016/j.accpm.2018.09.008. [10] 汤胜男, 辛学刚. 机器学习在生物信息学领域的应用与研究进展[J]. 人工智能, 2020(1):84-93. DOI:10.16453/j.cnki.issn2096-5036.2020.01.009.Tang SN, Xin XG. Application and research progress of machine learning in the field of bioinformatics[J]. AI-View, 2020(1):84-93. DOI:10.16453/j.cnki.issn2096-5036.2020.01.009. [11] 吴亚飞, 方亚. 机器学习方法在慢性病研究中的应用进展[J]. 中国卫生统计, 2020, 37(4):624-628.Wu YF, Fang Y. Progress in the application of machine learning methods in the research of chronic diseases[J]. Chin J Health Stat, 2020, 37(4):624-628. [12] 杜唯佳, 徐振东, 刘志强. 人工智能在麻醉学领域的应用进展[J]. 国际麻醉学与复苏杂志, 2020, 41(8):800-803. DOI:10.3760/cma.j.cn321761-20190620-00099.Du WJ, Xu ZD, Liu ZQ. Advances of artificial intelligence in anesthesiology[J]. Int J Anesthesiol Resusc, 2020, 41(8):800-803. DOI:10.3760/cma.j.cn321761-20190620-00099. [13] 中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB/T 23797-2009病媒生物密度监测方法蚊虫[S]. 北京:中国标准出版社, 2009.General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Administration Standardization. GB/T 23797-2009Surveillance methods for vector density-Mosquito[S]. Beijing:Standards Press of China, 2009. [14] 肖冰. 城市水绿复合系统的蚊虫孳生现状及成因分析:以上海和池州为例[D]. 上海:华东师范大学, 2019.Xiao B. Mosquito breeding status and cause analysis of the urban water-green combined system:A case study of Shanghai and Chizhou[D]. Shanghai:East China Normal University, 2019. [15] 黄建华, 石挺丽, 陈远源, 等. 多变量灰色模型MGM(1, n)在白纹伊蚊密度预测中的应用[J]. 中华疾病控制杂志, 2016, 20(1):87-90. DOI:10.16462/j.cnki.zhjbkz.2016.01.022.Huang JH, Shi TL, Chen YY, et al. Application of multivariable grey model (1, n) in prediction of Aedes albopictus density[J]. Chin J Dis Control Prev, 2016, 20(1):87-90. DOI:10.16462/j.cnki.zhjbkz.2016.01.022. [16] 李卫红, 陈业滨, 闻磊. 基于GA-BP神经网络模型的登革热时空扩散模拟[J]. 中国图象图形学报, 2015, 20(7):981-991. DOI:10.11834/jig.20150715.Li WH, Chen YB, Wen L. Simulation of spatio-temporal diffusion of dengue fever based on the GA-BP neural network model[J]. J Image Graph, 2015, 20(7):981-991. DOI:10.11834/jig.20150715. [17] 周毅彬, 冷培恩, 顾君忠, 等. 上海市白纹伊蚊密度与气象因素关系的研究[J]. 中国媒介生物学及控制杂志, 2014, 25(5):405-407. DOI:10.11853/j.issn.1003.4692.2014.05.005.Zhou YB, Leng PE, Gu JZ, et al. Study on relationship between population density of Aedes albopictus and meteorological factors in Shanghai, China[J]. Chin J Vector Biol Control, 2014, 25(5):405-407. DOI:10.11853/j.issn.1003.4692.2014.05.005. [18] 于德宪, 林立丰, 罗雷, 等. 人工神经网络模型用于分析气候因素对白纹伊蚊密度影响的初步探讨[J]. 南方医科大学学报, 2010, 30(7):1604-1605, 1609. DOI:10.12122/j.issn.1673-4254.2010.07.030.Yu DX, Lin LF, Luo L, et al. Establishment of an artificial neural network model for analysis of the influence of climate factors on the density of Aedes albopictus[J]. J South Med Univ, 2010, 30(7):1604-1605, 1609. DOI:10.12122/j.issn.1673-4254.2010.07.030. [19] Früh L, Kampen H, Kerkow A, et al. Modelling the potential distribution of an invasive mosquito species:comparative evaluation of four machine learning methods and their combinations[J]. Ecol Modell, 2018, 388:136-144. DOI:10.1016/j.ecolmodel.2018.08.011. [20] Kerkow A, Wieland R, Koban MB, et al. What makes the Asian bush mosquito Aedes japonicus japonicus feel comfortable in Germany? A fuzzy modelling approach[J]. Parasit Vectors, 2019, 12(1):106. DOI:10.1186/s13071-019-3368-0. [21] Demertzis K, Iliadis L, Anezakis VD. Commentary:Aedes albopictus and Ae. japonicus-two invasive mosquito species with different temperature niches in Europe[J]. Front Environ Sci, 2017, 5:85. DOI:10.3389/fenvs.2017.00085. [22] Zheng XL, Zhong DB, He YL, et al. Seasonality modeling of the distribution of Aedes albopictus in China based on climatic and environmental suitability[J]. Infect Dis Poverty, 2019, 8(1):98. DOI:10.1186/s40249-019-0612-y. [23] Scavuzzo JM, Trucco F, Espinosa M, et al. Modeling dengue vector population using remotely sensed data and machine learning[J]. Acta Trop, 2018, 185:167-175. DOI:10.1016/j.actatropica.2018.05.003. [24] Ding FY, Fu JY, Jiang D, et al. Mapping the spatial distribution of Aedes aegypti and Ae. albopictus[J]. Acta Trop, 2018, 178:155-162. DOI:10.1016/j.actatropica.2017.11.020. [25] 裘炯良, 孙志, 王军, 等. 人工神经网络在外来医学媒介生物输入风险评估中的应用研究[J]. 中华卫生杀虫药械, 2016, 22(5):456-460. DOI:10.19821/j.1671-2781.2016.05.013.Qiu JL, Sun Z, Wang J, et al. Application of back propagation neural network on the risk assessment of exotic medical-vector[J]. Chin J Hyg Insect Equip, 2016, 22(5):456-460. DOI:10.19821/j.1671-2781.2016.05.013. [26] Wieland R, Kerkow A, Früh L, et al. Automated feature selection for a machine learning approach toward modeling a mosquito distribution[J]. Ecol Modell, 2017, 352:108-112. DOI:10.1016/j.ecolmodel.2017.02.029. [27] 洪铭鸿. 基于边缘计算和深度学习之病媒蚊分类系统[D].中国台北:台湾师范大学, 2019.Hong MH. A vector mosquitoes classification system based on edge computing and deep learning[D]. Taipei, China:National Taiwan Normal University, 2019. [28] Sanchez-Ortiz A, Fierro-Radilla A, Arista-Jalife A, et al. Mosquito larva classification method based on convolutional neural networks[C] //Proceedings of 2017 International Conference on Electronics, Communications and Computers. Cholula, Mexico:IEEE, 2017. DOI:10.1109/CONIELECOMP. 2017.7891835. [29] Amarasinghe A, Suduwella C, Elvitigala C, et al. A machine learning approach for identifying mosquito breeding sites via drone images[C] //Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. Delft, The Netherlands:ACM, 2017. DOI:10.1145/3131672.3136986. [30] Park J, Kim DI, Choi B, et al. Classification and morphological analysis of vector mosquitoes using deep convolutional neural networks[J]. Sci Rep, 2020, 10(1):1012. DOI:10.1038/s41598-020-57875-1. [31] Motta D, AÁBSantos, Winkler I, et al. Application of convolutional neural networks for classification of adult mosquitoes in the field[J]. PLoS One, 2019, 14(1):e0210829. DOI:10.1371/journal.pone.0210829. [32] Case E, Shragai T, Harrington L, et al. Evaluation of unmanned aerial vehicles and neural networks for integrated mosquito management of Aedes albopictus (Diptera:Culicidae)[J]. J Med Entomol, 2020, 57(5):1588-1595. DOI:10.1093/jme/tjaa078. [33] Genoud AP, Gao YP, Williams GM, et al. A comparison of supervised machine learning algorithms for mosquito identification from backscattered optical signals[J]. Ecol Inform, 2020, 58:101090. DOI:10.1016/j.ecoinf.2020.101090. [34] 李振宇. 基于翅振频率的人工神经网络方法在蚊虫分类中的应用研究[D]. 雅安:四川农业大学, 2005.Li ZY. Application of artificial neural network to identify species of mosquitoes (Diptera:Culicidae) based on wingbeat frequency[D]. Ya'an:Sichuan Agricultural University, 2005. [35] 罗嘉鹏. 利用组学数据检测昆虫的抗药性和入侵性[D]. 南京:南京师范大学, 2018.Luo JP. Detect the insecticide resistance and invasiveness of insects with omics data[D]. Nanjing:Nanjing Normal University, 2018. [36] 宋帅葆, 艾上杰, 关怀, 等. 冈比亚按蚊犬尿氨酸甲酰胺酶抑制剂的虚拟筛选[J]. 昆虫学报, 2018, 61(1):68-78. DOI:10.16380/j.kcxb.2018.01.008.Song SB, Ai SJ, Guan H, et al. Virtual screening of inhibitors for kynurenine formamidase of Anopheles gambiae (Diptera:Culicidae)[J]. Acta Entomol Sin, 2018, 61(1):68-78. DOI:10.16380/j.kcxb.2018.01.008. [37] 王婷婷, 郝友进, 何正波, 等. 4种重要医学媒介蚊虫离子受体基因IR8a和IR25a的特征及分类地位[J]. 昆虫学报, 2017, 60(4):379-388. DOI:10.16380/j.kcxb.2017.04.003.Wang TT, Hao YJ, He ZB, et al. Characteristics and classification position of the ionotropic receptor genes IR8a and IR25a in four vector mosquito species of medical importance[J]. Acta Entomol Sin, 2017, 60(4):379-388. DOI:10.16380/j.kcxb.2017.04.003. [38] 毛启萌, 李廷景, 付文博, 等. 林氏按蚊线粒体全基因组序列的测定及基于线粒体基因组的按蚊属系统发育分析(英文)[J]. 昆虫学报, 2019, 62(1):101-116. DOI:10.16380/j.kcxb. 2019.01.011.Mao QM, Li TJ, Fu WB, et al. Sequencing of the complete mitochondrial genome of Anopheles lindesayi and a phylogenetic analysis of the genus Anopheles (Diptera:Culicidae)based on mitochondrial genomes[J]. Acta Entomol Sin, 2019, 62(1):101-116. DOI:10.16380/j.kcxb.2019.01.011. [39] Smith HA, White BJ, Kundert P, et al. Genome-wide QTL mapping of saltwater tolerance in sibling species of Anopheles (malaria vector) mosquitoes[J]. Heredity (Edinb), 2015, 115(5):471-479. DOI:10.1038/hdy.2015.39. [40] Saxena P, Mishra S. Study of the binding pattern of HLA class I alleles of Indian frequency and cTAP binding peptide for Chikungunya vaccine development[J]. Int J Pept Res Ther, 2020, 26(2):2437-2448. DOI:10.1007/s10989-020-10038-2. [41] 黄宇琳, 赵永谦, 曹峥, 等. 基于随机森林回归模型的登革热风险评估研究[J]. 华南预防医学, 2019, 45(1):26-31. DOI:10.13217/j.scjpm.2019.0026.Huang YL, Zhao YQ, Cao Z, et al. Risk assessment of dengue fever based on random forest model[J]. South China J Prev Med, 2019, 45(1):26-31. DOI:10.13217/j.scjpm.2019.0026. [42] 任红艳, 吴伟, 李乔玄, 等. 基于反向传播神经网络模型的广东省登革热疫情预测研究[J]. 中国媒介生物学及控制杂志, 2018, 29(3):221-225. DOI:10.11853/j.issn.1003.8280.2018.03.001.Ren HY, Wu W, Li QX, et al. Prediction of dengue fever based on back propagation neural network model in Guangdong, China[J]. Chin J Vector Biol Control, 2018, 29(3):221-225. DOI:10.11853/j.issn.1003.8280.2018.03.001. [43] 赵永谦. 珠三角地区精细空间尺度的登革热风险评估模型构建研究[D]. 广州:暨南大学, 2018.Zhao YQ. Building models for risk assessment of dengue fever in the Pearl River Delta, China:a study based on the fine spatial scale[D]. Guangzhou:Jinan University, 2018. [44] 陈斌. 广东登革热防控能力评估及社区干预实验研究[D]. 北京:中国疾病预防控制中心, 2017.Chen B. Capacity assessment and community intervention study on dengue control in Guangdong, China[D]. Beijing:Chinese Center for Disease Control and Prevention, 2017. [45] 宋晓晴. 基于大规模手机定位数据的城市内部登革热防控模拟研究[D]. 武汉:武汉大学, 2017.Song XQ. Simulating interventions on intra-urban dengue outbreaks using large-scale mobile phone tracking data[D]. Wuhan:Wuhan University, 2017. [46] 陈业滨, 李卫红. 支持向量机模型的登革热时空扩散预测[J]. 测绘科学, 2017, 42(2):65-70. DOI:10.16251/j.cnki.1009-2307.2017.02.013.Chen YB, Li WH. Simulation of spatio-temporal diffusion trend of dengue fever based on the SVM model[J]. Sci Surv Mapp, 2017, 42(2):65-70. DOI:10.16251/j.cnki.1009-2307.2017.02.013. [47] Zhao NZ, Charland K, Carabali M, et al. Machine learning and dengue forecasting:comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia[J]. PLoS Negl Trop Dis, 2020, 14(9):e0008056. DOI:10.1371/journal.pntd.0008056. [48] Kesorn K, Ongruk P, Chompoosri J, et al. Morbidity rate prediction of dengue hemorrhagic fever (DHF) using the support vector machine and the Aedes aegypti infection rate in similar climates and geographical areas[J]. PLoS One, 2015, 10(5):e0125049. DOI:10.1371/journal.pone.0125049. [49] Ho TS, Weng TC, Wang JD, et al. Comparing machine learning with case-control models to identify confirmed dengue cases[J]. PLoS Negl Trop Dis, 2020, 14(11):e0008843. DOI:10.1371/journal.pntd.0008843. [50] 温亮. 海南省疟疾流行预测方法及基于GIS的疟疾监测预警系统的初步构建[D]. 西安:第四军医大学, 2004.Wen L. Research on prediction of malaria epidemic and construction GIS-based malaria surveillance and early warning system in Hainan province, China[D]. Xi'an:Fourth Military Medical University, 2004. [51] 郭华. 面向主动监控的感染风险时空模式建模与挖掘[D]. 长春:吉林大学, 2014.Guo H. Active surveillance oriented spatiotemporal patterns modeling and mining of infection risk[D]. Changchun:Jilin University, 2014. [52] 高春玉. 我国疟疾流行现状及气象因素对疟疾发病影响的神经网络模型初步研究[D]. 重庆:第三军医大学, 2002.Gao CY. Study on epidemic situation of malaria in China and neural network model of disease influenced by meteorological factors[D]. Chongqing:Third Military Medical University, 2002. [53] 高文, 黄钢, 韩晓莉. 基于蚊密度差分自回归移动平均模型预测流行性乙型脑炎的贝叶斯判别分析研究[J]. 中国媒介生物学及控制杂志, 2018, 29(6):557-563. DOI:10.11853/j.issn.1003.8280.2018.06.003.Gao W, Huang G, Han XL. Application of Bayes analysis in Japanese encephalitis prediction based on multiple seasonal autoregressive integrated moving average model[J]. Chin J Vector Biol Control, 2018, 29(6):557-563. DOI:10.11853/j.issn.1003.8280.2018.06.003. [54] Vidal OM, Acosta-Reyes J, Padilla J, et al. Chikungunya outbreak (2015) in the Colombian Caribbean:latent classes and gender differences in virus infection[J]. PLoS Negl Trop Dis, 2020, 14(6):e0008281. DOI:10.1371/journal.pntd.0008281. [55] Eneanya OA, Cano J, Dorigatti I, et al. Environmental suitability for lymphatic filariasis in Nigeria[J]. Parasit Vector, 2018, 11(1):513. DOI:10.1186/s13071-018-3097-9. [56] 朱远林. 基于深度学习的疟疾自检测与分类算法研究[D]. 北京:北京交通大学, 2019.Zhu YL. Research on self-detection and classification algorithm of malaria based on deep learning methods[D]. Beijing:Beijing Jiaotong University, 2019. [57] Zhao OS, Kolluri N, Anand A, et al. Convolutional neural networks to automate the screening of malaria in low-resource countries[J]. PeerJ, 2020, 8:e9674. DOI10.7717/peerj.9674. |
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