Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling
文献类型：期刊论文
| 作者 | Li, Zhichao1; Gurgel, Helen2; Xu, Lei3; Yang, Linsheng1; Dong, Jinwei1 |
| 刊名 | BIOLOGY-BASEL
 |
| 出版日期 | 2022-02-01 |
| 卷号 | 11期号:2页码:14 |
| 关键词 | dengue Google Earth Engine LSTM geospatial big data risk forecasting |
| DOI | 10.3390/biology11020169 |
| 通讯作者 | Dong, Jinwei(dongjw@igsnrr.ac.cn) |
| 英文摘要 | Simple Summary Forecasting dengue cases often face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without historical dengue cases. With the advance of the geospatial big data cloud computing in Google Earth Engine and deep learning, this study proposed an efficient framework of dengue prediction at an epidemiological week basis using geospatial big data analysis in Google Earth Engine and Long Short Term Memory modeling. We focused on the dengue epidemics in the Federal District of Brazil during 2007-2019. Based on Google Earth Engine and epidemiological calendar, we computed the weekly composite for each dengue driving factor, and spatially aggregated the pixel values into dengue transmission areas to generate the time series of driving factors. A multi-step-ahead Long Short Term Memory modeling was used, and the time-differenced natural log-transformed dengue cases and the time series of driving factors were considered as outcomes and explantary factors, respectively, with two modeling scenarios (with and without historical cases). The performance is better when historical cases were used, and the 5-weeks-ahead forecast has the best performance. Timely and accurate forecasts of dengue cases are of great importance for guiding disease prevention strategies, but still face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation capability due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without the application of historical case information. Geospatial big data, cloud computing platforms (e.g., Google Earth Engine, GEE), and emerging deep learning algorithms (e.g., long short term memory, LSTM) provide new opportunities for advancing these efforts. Here, we focused on the dengue epidemics in the urban agglomeration of the Federal District of Brazil (FDB) during 2007-2019. A new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis. We first defined a buffer zone around an impervious area as the main area of dengue transmission by considering the impervious area as a human-dominated area and used the maximum distance of the flight range of Aedes aegypti and Aedes albopictus as a buffer distance. Those zones were used as units for further attribution analyses of dengue epidemics by aggregating the pixel values into the zones. The near weekly composite of potential driving factors was generated in GEE using the epidemiological weeks during 2007-2019, from the relevant geospatial data with daily or sub-daily temporal resolution. A multi-step-ahead LSTM model was used, and the time-differenced natural log-transformed dengue cases were used as outcomes. Two modeling scenarios (with and without historical dengue cases) were set to examine the potential of historical information on dengue forecasts. The results indicate that the performance was better when historical dengue cases were used and the 5-weeks-ahead forecast had the best performance, and the peak of a large outbreak in 2019 was accurately forecasted. The proposed framework in this study suggests the potential of the GEE platform, the LSTM algorithm, as well as historical information for dengue risk forecasting, which can easily be extensively applied to other regions or globally for timely and practical dengue forecasts. |
| WOS关键词 | AEDES-AEGYPTI ; CLIMATE ; PRECIPITATION ; DYNAMICS |
| 资助项目 | Strategic Priority Research Program[XDA19040301] ; National Natural Science Foundation of China[41801336] ; National Natural Science Foundation of China[42061134019] ; Key Research Program of Frontier Sciences[QYZDB-SSW-DQC005] ; Chinese Academy of Sciences (CAS)[E0V00110YZ] ; Institute of Geographic Sciences and Natural Resources Research |
| WOS研究方向 | Life Sciences & Biomedicine - Other Topics |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000768304000001 |
| 资助机构 | Strategic Priority Research Program ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences ; Chinese Academy of Sciences (CAS) ; Institute of Geographic Sciences and Natural Resources Research |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/171488]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Dong, Jinwei |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 2.Univ Brasilia UnB, Dept Geog, BR-70910900 Brasilia, DF, Brazil 3.Tsinghua Univ, Vanke Sch Publ Hlth, Beijing 100084, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Zhichao,Gurgel, Helen,Xu, Lei,et al. Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling[J]. BIOLOGY-BASEL,2022,11(2):14. |
| APA | Li, Zhichao,Gurgel, Helen,Xu, Lei,Yang, Linsheng,&Dong, Jinwei.(2022).Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling.BIOLOGY-BASEL,11(2),14. |
| MLA | Li, Zhichao,et al."Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling".BIOLOGY-BASEL 11.2(2022):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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