Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review
文献类型：期刊论文
| 作者 | Li, Zhichao; Dong, Jinwei |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-10-01 |
| 卷号 | 14期号:19页码:22 |
| 关键词 | dengue risk forecasting big geospatial data data-driven models review |
| DOI | 10.3390/rs14195052 |
| 通讯作者 | Li, Zhichao(lizc@igsnrr.ac.cn) |
| 英文摘要 | With advancements in big geospatial data and artificial intelligence, multi-source data and diverse data-driven methods have become common in dengue risk prediction. Understanding the current state of data and models in dengue risk prediction enables the implementation of efficient and accurate prediction in the future. Focusing on predictors, data sources, spatial and temporal scales, data-driven methods, and model evaluation, we performed a literature review based on 53 journal and conference papers published from 2018 to the present and concluded the following. (1) The predominant predictors include local climate conditions, historical dengue cases, vegetation indices, human mobility, population, internet search indices, social media indices, landscape, time index, and extreme weather events. (2) They are mainly derived from the official meteorological agency satellite-based datasets, public websites, department of health services and national electronic diseases surveillance systems, official statistics, and public transport datasets. (3) Country-level, province/state-level, city-level, district-level, and neighborhood-level are used as spatial scales, and the city-level scale received the most attention. The temporal scales include yearly, monthly, weekly, and daily, and both monthly and weekly are the most popular options. (4) Most studies define dengue risk forecasting as a regression task, and a few studies define it as a classification task. Data-driven methods can be categorized into single models, ensemble learning, and hybrid learning, with single models being further subdivided into time series, machine learning, and deep learning models. (5) Model evaluation concentrates primarily on the quantification of the difference/correlation between time-series observations and predicted values, the ability of models to determine whether a dengue outbreak occurs or not, and model uncertainty. Finally, we highlighted the importance of big geospatial data, data cloud computing, and other deep learning models in future dengue risk forecasting. |
| WOS关键词 | GOOGLE EARTH ENGINE ; MODEL ; DISEASES ; BURDEN |
| 资助项目 | Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (CAS)[QYZDBSSW-DQC005] ; CAS[XDA19040301] ; Institute of Geographic Sciences and Natural Resources Research of the CAS[E0V00110YZ] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000867038500001 |
| 资助机构 | Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (CAS) ; CAS ; Institute of Geographic Sciences and Natural Resources Research of the CAS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/185504]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Zhichao |
| 作者单位 | Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Zhichao,Dong, Jinwei. Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review[J]. REMOTE SENSING,2022,14(19):22. |
| APA | Li, Zhichao,&Dong, Jinwei.(2022).Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review.REMOTE SENSING,14(19),22. |
| MLA | Li, Zhichao,et al."Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review".REMOTE SENSING 14.19(2022):22. |
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。