Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.
Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.
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DOI：
10.3389/fpubh.2022.900077
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发表时间：
2022
影响因子：
5.2
通讯作者：
dos Santos, Wellington Pinheiro
中科院分区：
文献类型：
--
作者：
de Lima, Clarisse Lins;da Silva, Ana Clara Gomes;Moreno, Giselle Machado Magalhaes;da Silva, Cecilia Cordeiro;Musah, Anwar;Aldosery, Aisha;Dutra, Livia;Ambrizzi, Tercio;Borges, Iuri V. G.;Tunali, Merve;Basibuyuk, Selma;Yenigun, Orhan;Massoni, Tiago Lima;Browning, Ella;Jones, Kate;Campos, Luiza;Kostkova, Patty;da Silva Filho, Abel Guilhermino;dos Santos, Wellington Pinheiro
关键词：
虫媒病毒是一组通过节肢动物媒介传播的疾病。因为它们是对世界各国构成若干公共卫生挑战的被忽视热带病的一部分。虫媒病毒的动态受气候、环境和人类流动性因素的综合影响。虫媒病毒预测模型可作为公共卫生机构决策的辅助工具。在这项研究中，我们提出了一个系统的文献综述，以确定虫媒病毒的预测模型，以及它们的传播媒介动力学模型。为了进行本综述，我们检索了著名的科学基础，如IEE explore、PubMed、Science Direct、施普林格Link和Scopus。我们使用搜索字符串搜索2015年至2020年之间发表的研究。总共返回429篇文章，然而，通过排除和纳入标准筛选后，139篇文章被纳入。通过这一系统综述，可以确定虫媒病毒预测模型构建中存在的挑战，以及在时空模型构建方面存在的差距。
Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.
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影响因子：
3.2
作者：
da Cruz Ferreira DA;Degener CM;de Almeida Marques-Toledo C;Bendati MM;Fetzer LO;Teixeira CP;Eiras ÁE
通讯作者：
Eiras ÁE
影响因子：
3.8
作者：
Adde, Antoine;Roucou, Pascal;Flamand, Claude
通讯作者：
Flamand, Claude
DOI：
10.1016/j.physa.2019.121266
发表时间：
2019-08-01
影响因子：
3.3
作者：
Chakraborty, Tanujit;Chattopadhyay, Swarup;Ghosh, Indrajit
通讯作者：
Ghosh, Indrajit
DOI：
10.1007/s00477-020-01818-9
发表时间：
2020-05-31
影响因子：
4.2
作者：
Ahmad, Hammad;Ali, Asad;Shakir, Muhammad
通讯作者：
Shakir, Muhammad
影响因子：
3.2
作者：
Bennett, Kelly L.;Gomez Martinez, Carmelo;Loaiza, Jose R.
通讯作者：
Loaiza, Jose R.