Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review
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 pred...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/19/5052 |
| _version_ | 1827653066206216192 |
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| author | Zhichao Li Jinwei Dong |
| author_facet | Zhichao Li Jinwei Dong |
| author_sort | Zhichao Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:11:50Z |
| format | Article |
| id | doaj.art-2c04813fff4e49babf3d92bf687047a8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:11:50Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2c04813fff4e49babf3d92bf687047a82023-11-23T21:43:07ZengMDPI AGRemote Sensing2072-42922022-10-011419505210.3390/rs14195052Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A ReviewZhichao Li0Jinwei Dong1Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaWith 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.https://www.mdpi.com/2072-4292/14/19/5052denguerisk forecastingbig geospatial datadata-driven modelsreview |
| spellingShingle | Zhichao Li Jinwei Dong Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review Remote Sensing dengue risk forecasting big geospatial data data-driven models review |
| title | Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review |
| title_full | Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review |
| title_fullStr | Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review |
| title_full_unstemmed | Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review |
| title_short | Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review |
| title_sort | big geospatial data and data driven methods for urban dengue risk forecasting a review |
| topic | dengue risk forecasting big geospatial data data-driven models review |
| url | https://www.mdpi.com/2072-4292/14/19/5052 |
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