Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review
Abstract
:1. Introduction
2. Methods and Materials
2.1. General Framework of Dengue Risk Forecasting
2.2. Literature Selection and Information Analysis
2.3. Data Analysis
3. Current Status of Dengue Risk Forecasting
3.1. Dengue Predictors and Data Sources
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- Historical time series of dengue cases are becoming increasingly important for future dengue risk prediction as they can provide the temporal characteristics of the dengue transmission of the specific study area: the important basis for future dengue transmission [12,16]. In addition, the time series of dengue cases in a typical dengue epidemic area can be used as one of the covariates for risk forecasting in its neighboring cities due to the proximity of the two cities and the spatial and temporal relationship between the dengue epidemics in the two cities [16,67];
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- Internet search indices (e.g., the Baidu search index and Google search index) were utilized for dengue tracking as they are widely accessible worldwide, and represent the real-time attention of the population to a popular event, providing it strong potential to supplement current epidemiological methods [9,27,69];
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- Social media indices, mainly including tweet counts and Sina Weibo posts, were widely used for dengue risk forecasting as social media has become the key communication tool for governments, organizations, and individuals to disseminate valuable information to the public during dengue epidemics. In addition, individual users can also post their concerns and awareness of the dengue epidemics [29];
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- Extreme weather events (e.g., El Niño and La Niña) can drive climate variability at both seasonal and inter-annual intervals [21].
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- The official meteorological agencies provide data collected from meteorological stations that are mainly used for identifying local climatic factors;
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- Satellite-based datasets, including multi-source remote sensing images and ready-to-use products, can be used for computing the local climate factors (e.g., temperature and rainfall) and vegetation indices;
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- Public websites, such as search engines and social media sites, can provide internet search indices and social media indices;
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- Both the department of health services and the national electronic diseases surveillance systems (e.g., the Brazilian Notifiable Diseases Information System (SINAN) and the nowcasting surveillance system of arboviruses in Brazil, namely, infodengue [68]) provide historical dengue cases. It should be noted that some dengue projects (e.g., NOAA dengue forecasting project [69]) can also provide historical dengue epidemiological data;
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- Official statistics provide data related to population density and building age in recent studies of dengue risk forecasting;
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- Public transport data, such as bus transportation data and incoming and outgoing travel volumes, were used to characterize human mobility.
3.2. Spatial and Temporal Scales
3.3. Models
3.3.1. Time Series Models
3.3.2. Machine Learning Approaches
3.3.3. Deep Learning Approaches
3.3.4. Ensemble Learning
3.3.5. Hybrid Learning
3.4. Evaluation Metrics
4. Discussion
4.1. Potentials of Big Geospatial Data in Identification of More Dengue Risk Predictors
4.2. Potential of Geospatial Data Cloud Computing in Dengue Risk Prediction
4.3. Potentials of Deep Learning Models in Dengue Risk Forecasting
4.4. Limitations of the Use of Big Geospatial Data and Deep Learning in Dengue Risk Forecasting
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
ID | Authors | Year | Spatial Scales | Temporal Scales | Types of Predictors | Types of Data Sources | Models | Evaluation Metrics | Task |
---|---|---|---|---|---|---|---|---|---|
J1 | Li et al. [12] | 2022 | City-level | Weekly | Temperature | Satellite-based dataset | ARIMA LSTM | RMSE MAE | R |
Rainfall | |||||||||
Vegetation index | |||||||||
Humidity | |||||||||
Dengue | Department of health services and national electronic disease surveillance system | ||||||||
C2 | Pham et al. [13] | 2018 | City-level | Daily | Temperature | Official meteorological agency | LR DT LSTM | RMSE MAE | R, C |
Rainfall | |||||||||
Humidity | |||||||||
Wind | |||||||||
Vegetation index | Satellite-based dataset | ||||||||
J3 | Mussumeci et al. [14] | 2020 | City-level | Weekly | Temperature | Official meteorological agency | LASSO RF LSTM | MSE MSLE | R |
Temperature | |||||||||
Humidity | |||||||||
Pressure | |||||||||
Social media index | Public website | ||||||||
C4 | Saleh et al. [15] | 2021 | City-level | Weekly | Rainfall | Public website | SVR LSTM | R2RMSEMAEMSE | R |
Humidity | |||||||||
Temperature | |||||||||
Temperature | |||||||||
Vegetation index | Satellite-based dataset | ||||||||
J5 | Xu et al. [16] | 2020 | City-level | Monthly | Pressure | Official meteorological agency | GAM SVR GBM BPNN LSTM LSTM-TL | RMSE RRSE | R |
Temperature | |||||||||
Rainfall | |||||||||
Humidity | |||||||||
J6 | Liu et al. [17] | 2021 | Town-level | Weekly | Landscape | Public website | MLP SVM | Pearson correlation Hit rate | R, C |
Temperature | Official meteorological agency | ||||||||
Rainfall | |||||||||
Population | Satellite-based dataset | ||||||||
C7 | Chovatiya et al. [18] | 2019 | City-level | Monthly | Humidity | Public website | LSTM | RMSE | R |
Temperature | |||||||||
Pressure | |||||||||
Rainfall | Official meteorological agency | ||||||||
Air quality | |||||||||
C8 | Mustaffa et al. [19] | 2018 | City-level | Weekly | Rainfall | Official meteorological agency | Hybrid FPA-LSSVM | MSE RMSPE | R |
Temperature | |||||||||
Humidity | |||||||||
J9 | Liu et al. [20] | 2019 | City-level | Monthly | Temperature | Official meteorological agency | GAMM GAM | RMSE | R |
Humidity | |||||||||
Rainfall | |||||||||
Internet | Public website | ||||||||
J10 | Carvajal et al. [21] | 2018 | City-level | Weekly | Temperature | Official meteorological agency | GAM SARIMA RF GB | RMSEMAE | R |
Rainfall | |||||||||
Humidity | |||||||||
Wind | |||||||||
El Niño/La Niña | Official meteorological agency | ||||||||
Social media index | Public website | ||||||||
J11 | Appice et al. [22] | 2020 | State-level | Monthly | Temperature | Official meteorological agency | VAR ARIMA AutoTiC-NN M5 SVR kNN | RMSE Cluster analysis | R |
J12 | Zhao et al. [23] | 2020 | Country-level/state-level | Weekly | Vegetation index | Satellite-based dataset | ARIMA RF ANN | MAE RMAE | R |
Landscape | |||||||||
Rainfall | |||||||||
Population | Official meteorological agency | ||||||||
Economic index | |||||||||
Educational index | |||||||||
Time | — | ||||||||
J13 | Benedum et al. [24] | 2020 | City-level | Weekly | Dengue | Department of health services and national electronic disease surveillance system | PR Logistic regression ARIMA SARIMA RF | nMAE MCC | R, C |
Population | Official Statistics | ||||||||
Temperature | Official meteorological agency | ||||||||
Humidity | |||||||||
Rainfall | Satellite-based dataset | ||||||||
J14 | Bomfim et al. [25] | 2020 | Neighborhood-level | Weekly | Human mobility | Public transport data | ARIMA LSTM | Precision Recall F-score MAE RMSE | R, C |
Time | — | ||||||||
J15 | Withanage et al. [26] | 2018 | District-level | Monthly | Rainfall | Official meteorological agency | LR | RMSE MAE MAPE PSS | R |
Temperature | |||||||||
Humidity | |||||||||
Dengue | Department of health services and national electronic disease surveillance system | ||||||||
J16 | Guo et al. [27] | 2019 | City-level | Weekly | Temperature | Official meteorological agency | Ensemble learning | RMSE MAE Pearson correlation | R |
Humidity | |||||||||
Rainfall | |||||||||
Internet | Public website | ||||||||
Social media index | |||||||||
J17 | Fakhruddin et al. [28] | 2019 | City-level | Weekly | Rainfall | Official meteorological agency | GLM | MSE R2 Adjusted R2 p-value | R |
Humidity | |||||||||
J18 | Ramadona et al. [29] | 2019 | Neighborhood-level | Monthly | Social media index | Public website | PR | BIC R2 SRMSE | R |
Human mobility | |||||||||
J19 | Chakraborty et al. [30] | 2019 | City-level, Country-level | Weekly/monthly | Dengue | Public website | ARIMA ANN NNAR Hybrid ARIMA-ANN Hybrid ARIMA-NNAR | RMSE MAE SMAPE | R |
J20 | Cortes et al. [31] | 2018 | City-level | Monthly | Dengue | Department of health services and national electronic disease surveillance system | ARIMA SARIMA | Comparison | R |
J21 | Jayaraj et al. [32] | 2019 | District-level | Monthly | Temperature | Official meteorological agency | PR SARIMA SARIMAX | AIC MAE MSE | R |
Humidity | |||||||||
Rainfall | |||||||||
C22 | Tanawi et al. [33] | 2021 | City-level | Weekly | Humidity | Official meteorological agency | SVR | RMSE MAE | R |
Rainfall | |||||||||
Temperature | |||||||||
Dengue | Department of health services and national electronic disease surveillance system | ||||||||
J23 | Findlater et al. [34] | 2019 | Country-level | Yearly | Human mobility | Public transport data | NBR | unknown | R |
Population | Official Statistics | ||||||||
J24 | Bal et al. [35] | 2020 | City-level | Monthly | Temperature | Official meteorological agency | Zero-inflated PR | Comparison, CI | R |
Humidity | |||||||||
Rainfall | |||||||||
Dengue | Department of health services and national electronic disease surveillance system | ||||||||
J25 | Cheng et al. [36] | 2020 | City-level | Daily | Temperature | Official meteorological agency | AR | MAE RMSE | R |
Humidity | |||||||||
Mosquito index | Unknown | ||||||||
J26 | Buczak et al. [37] | 2018 | City-level | Weekly | Temperature | Satellite-based dataset | Ensemble learning SARIMA | MAE | R |
Rainfall | |||||||||
Vegetation index | |||||||||
J27 | Polwiang [38] | 2020 | City-level | Monthly | Humidity | Official meteorological agency | PR ARIMA ANN | Correlation coefficient MAE RMSE MAPE | R |
Temperature | |||||||||
Rainfall | |||||||||
J28 | Rangarajan et al. [39] | 2019 | Country-level | Weekly | Internet | Public website | ARLR AR LASSO Ensemble learning, Naïve method | RMSE MAE MAPE | R |
J29 | Gabriel et al. [40] | 2019 | City-level | Monthly | Dengue | Department of health services and national electronic disease surveillance system | SARIMA | Comparison | R |
J30 | Yuan et al. [41] | 2019 | Province-level | Yearly/weekly | Temperature | Official meteorological agency | PR | MSE | R |
Rainfall | |||||||||
J31 | Zhu et al. [42] | 2019 | Province-level | Weekly | Temperature | Official meteorological agency | Probit regression | Correlation Coefficient | R |
Pressure | |||||||||
Humidity | |||||||||
Wind | |||||||||
J32 | Chen et al. [43] | 2018 | Neighborhood-level | Weekly | Human mobility | Public transport data | LASSO | RMSE, ROC | R, C |
Landscape | Official Statistics | ||||||||
Humidity | Official meteorological agency | ||||||||
Temperature | |||||||||
Vegetation index | Satellite-based dataset | ||||||||
J33 | Shashvat et al. [44] | 2019 | City-level | Monthly | Rainfall | Official meteorological agency | LR ANN SVR ARIMA Ensemble learning | RMSE MSE MAE | R |
Humidity | |||||||||
J34 | Valencia et al. [45] | 2021 | City-level | Weekly | Temperature | Official meteorological agency | SARIMA SARIMAXLSTM | RMSE MAPE | R |
Humidity | |||||||||
Rainfall | |||||||||
C35 | Mishra et al. [46] | 2019 | City-level | Weekly | Rainfall | Official meteorological agency | LR SVR NN XGBoost BR GBR | MAE | R |
Vegetation index | Satellite-based dataset | ||||||||
C36 | Nan et al. [47] | 2018 | City-level | Daily | Temperature | Official meteorological agency | XGBoost RF LASSO AdaBoost SVM LR Gboost RR | RMSE MAE R2 | R |
Pressure | |||||||||
Wind | |||||||||
J37 | Liu et al. [48] | 2020 | Town-level | Weekly | Temperature | Official meteorological agency | SVM LASSO ANN | Pearson correlation Hit Rate | R, C |
Rainfall | |||||||||
Population | Satellite-based dataset | ||||||||
Human mobility | China Mobile Telecommunications Company | ||||||||
C38 | Kurnianingsih et al. [49] | 2020 | Province-level | Yearly | Others | Official meteorological agency | LSTM | MAE RMSE | R |
El Niño/La Niña | |||||||||
J39 | Puengpreeda et al. [50] | 2020 | Province-level | Weekly | Internet | Public website | LASSO RR RF AdaBoost Extra-Trees | MSE MAE R2 | R |
Rainfall | Official meteorological agency | ||||||||
Humidity | |||||||||
Sunshine Index | |||||||||
Temperature | |||||||||
Wind | |||||||||
J40 | Shashvat et al. [51] | 2021 | City-level | Monthly | Rainfall | Public website | ES ARIMA | MAE AIC BIC MASE | R |
Humidity | |||||||||
C41 | Mustaffa et al. [52] | 2019 | City-level | Monthly | Rainfall | Public website | Hybrid FPA-LSSVM, Hybrid MFO-LSSVM, Hybrid GWO-LSSVM | RMSPE, MSE | R |
Temperature | |||||||||
Humidity | |||||||||
J42 | Baquero et al. [53] | 2018 | City-level | Monthly | Temperature | Official meteorological agency | GAM ANN SARIMA Ensemble model | RMSE | R |
Humidity | |||||||||
Rainfall | |||||||||
C43 | Anggraeni et al. [54] | 2019 | City-level | Weekly | Dengue | Department of health services and national electronic disease surveillance system | Hybrid Fuzzy-ARIMA | MSE SMAPE | R |
C44 | Baker et al. [55] | 2021 | City-level | Weekly | Temperature | Public website | MLR PR NBM NB DT SVM kNN AdaBoost Ensemble learning | MAE | R |
Rainfall | |||||||||
Humidity | |||||||||
Vegetation index | |||||||||
Dengue | |||||||||
J45 | Chakraborty et al. [56] | 2020 | Country-level | Weekly | Rainfall | Official meteorological agency | GP GAM ARIMA RF | RMSE MAD | R |
Humidity | |||||||||
Temperature | |||||||||
C46 | Saptarini et al. [57] | 2018 | City-level | Monthly | Rainfall | Official meteorological agency | Hybrid GA-ERNN | MSE | R |
Humidity | |||||||||
Temperature | |||||||||
Sea level | |||||||||
C47 | Kerdprasop et al. [58] | 2020 | City-level | Monthly | Time | — | ANN MLR GLM LR SVR CART CHAID Ensemble learning | Correlation coefficient | R |
Rainfall | Satellite-based dataset | ||||||||
Vegetation index | |||||||||
Temperature | |||||||||
El Niño/La Niña | |||||||||
C48 | Raju et al. [59] | 2019 | State-level | Monthly | Dengue | Department of health services and national electronic disease surveillance system | LR KR SVR | MAE, MSE | R |
Rainfall | Official meteorological agency | ||||||||
Temperature | |||||||||
Humidity | |||||||||
C49 | Thiruchelvam et al. [60] | 2021 | District-level | Weekly | Temperature | Official meteorological agency | ARIMAX, LSE | MSE AIC | R |
Humidity | |||||||||
Rainfall | |||||||||
Dengue | Department of health services and national electronic disease surveillance system | ||||||||
C50 | Jayasani et al. [61] | 2021 | District-level | Monthly | Rainfall | Official meteorological agency | LSTM LSTM-TL VAR | RMSE MAD | R |
Dengue | Department of health services and national electronic disease surveillance system | ||||||||
Temperature | Public website | ||||||||
Humidity | |||||||||
J51 | Stolerman et al. [62] | 2019 | City-level | Yearly | Rainfall | Official meteorological agency | SVM | Accuracy | C |
Temperature | |||||||||
Dengue | Department of health services and national electronic disease surveillance system | ||||||||
J52 | Koh et al. [63] | 2018 | City-level | Weekly | Dengue | Department of health services and national electronic disease surveillance system | AR Bayesian NN | MSE | R |
Rainfall | Public website | ||||||||
J53 | Nguyen et al. [64] | 2022 | Province-level | Monthly | Rainfall | Official meteorological agency | PR SVR XGBoost SARIMA CNN Transformer LSTM LSTM-AT | MAE RMSE Accuracy Precision Sensitivity Specificity | R, C |
Temperature | |||||||||
Humidity | |||||||||
Sunshine Index | |||||||||
Evaporation |
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Predictors and Datasets | Spatial Resolution | Temporal Resolution | Period | ||
---|---|---|---|---|---|
Climate | Temperature | GLDAS-2.1 | 27,000 m | 3-hourly | 2000 to present |
Rainfall | GLDAS-2.1 | 27,000 m | 3-hourly | 2000 to present | |
Humidity | GLDAS-2.1 | 27,000 m | 3-hourly | 2000 to present | |
Environment | NDVI | MOD09GA | 500 m | Daily | 2000 to present |
EVI | MOD09GA | 500 m | Daily | 2000 to present | |
NDWI | MOD09GA | 500 m | Daily | 2000 to present | |
dLST | MOD11A1 | 1000 m | Daily | 2000 to present | |
nLST | MOD11A1 | 1000 m | Daily | 2000 to present | |
Transmission areas | Population density | GPW v4.0 | 1000 m | 5-year | 2000, 2005, 2010, 2015, 2020 |
Imperious surface | GAIA | 30 m | Annual | 1985–2018 |
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Li, Z.; Dong, J. Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review. Remote Sens. 2022, 14, 5052. https://doi.org/10.3390/rs14195052
Li Z, Dong J. Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review. Remote Sensing. 2022; 14(19):5052. https://doi.org/10.3390/rs14195052
Chicago/Turabian StyleLi, Zhichao, and Jinwei Dong. 2022. "Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review" Remote Sensing 14, no. 19: 5052. https://doi.org/10.3390/rs14195052