Dengue Vector Population Forecasting Using Multisource Earth Observation Products and Recurrent Neural Networks

Mudele, Oladimeji; Frery, Alejandro C.; Zanandrez, Lucas; Eiras, Álvaro Eduardo; Gamba, Paolo

doi:10.1109/jstars.2021.3073351

Cited by 15 publications

(11 citation statements)

References 36 publications

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“…AI is attractive because it offers powerful predictive capabilities for comparatively little effort and allows many disparate predictors to be easily incorporated into a model. However, some approaches, such as the novel use of street view image data by [ 98 ], cannot be done without ML techniques because of the difficulty in automatically selecting predictors from many features in an image.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the authors in [ 98 ] implemented a recurrent neural network to forecast Aedes aegypti mosquito counts locally, using Earth Observation data inputs as proxies to environmental variables. The model was validated using in situ data from Vila Velha and Serra in Espírito Santo, Brazil and compared with RF and k-Nearest Neighbor (kNN) models, which showed a confidence adjustment of 95%.…”

Section: Machine Learning For Dengue Predictive Purposes In Latin Ame...mentioning

confidence: 99%

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Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review

et al. 2022

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Dengue fever is a serious and growing public health problem in Latin America and elsewhere, intensified by climate change and human mobility. This paper reviews the approaches to the epidemiological prediction of dengue fever using the One Health perspective, including an analysis of how Machine Learning techniques have been applied to it and focuses on the risk factors for dengue in Latin America to put the broader environmental considerations into a detailed understanding of the small-scale processes as they affect disease incidence. Determining that many factors can act as predictors for dengue outbreaks, a large-scale comparison of different predictors over larger geographic areas than those currently studied is lacking to determine which predictors are the most effective. In addition, it provides insight into techniques of Machine Learning used for future predictive models, as well as general workflow for Machine Learning projects of dengue fever.

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Section: Discussionmentioning

confidence: 99%

Section: Machine Learning For Dengue Predictive Purposes In Latin Ame...mentioning

confidence: 99%

Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review

et al. 2022

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“…According to this study, low humidity in September and October is generally followed by a dengue outbreak early the following year. As a result, it's highly likely that if seasonal circumstances vary as a result of climate change, seasonal dengue outbreaks will shift as well ( 34 ).…”

Section: Analytical Review Of Predicting Dengue Outbreakmentioning

confidence: 99%

A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions

Bhatia

Bansal

Patil

et al. 2022

Front. Public Health

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Climate change is unexpected weather patterns that can create an alarming situation. Due to climate change, various sectors are affected, and one of the sectors is healthcare. As a result of climate change, the geographic range of several vector-borne human infectious diseases will expand. Currently, dengue is taking its toll, and climate change is one of the key reasons contributing to the intensification of dengue disease transmission. The most important climatic factors linked to dengue transmission are temperature, rainfall, and relative humidity. The present study carries out a systematic literature review on the surveillance system to predict dengue outbreaks based on Machine Learning modeling techniques. The systematic literature review discusses the methodology and objectives, the number of studies carried out in different regions and periods, the association between climatic factors and the increase in positive dengue cases. This study also includes a detailed investigation of meteorological data, the dengue positive patient data, and the pre-processing techniques used for data cleaning. Furthermore, correlation techniques in several studies to determine the relationship between dengue incidence and meteorological parameters and machine learning models for predictive analysis are discussed. In the future direction for creating a dengue surveillance system, several research challenges and limitations of current work are discussed.

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“…They concluded that SVR with a linear kernel provides better results than SVR with a radial kernel. Recently Mudele et al (5) proposed a technique that uses a recurrent neural network (RNN) for forecasting the dengue mosquito vector population. This model is compared with random forest and k nearest neighbor for two Brazilian cities.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models

Patil

Pandya

2021

Front. Public Health

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For forecasting the spread of dengue, monitoring climate change and its effects specific to the disease is necessary. Dengue is one of the most rapidly spreading vector-borne infectious diseases. This paper proposes a forecasting model for predicting dengue incidences considering climatic variability across nine cities of Maharashtra state of India over 10 years. The work involves the collection of five climatic factors such as mean minimum temperature, mean maximum temperature, relative humidity, rainfall, and mean wind speed for 10 years. Monthly incidences of dengue for the same locations are also collected. Different regression models such as random forest regression, decision trees regression, support vector regress, multiple linear regression, elastic net regression, and polynomial regression are used. Time-series forecasting models such as holt's forecasting, autoregressive, Moving average, ARIMA, SARIMA, and Facebook prophet are implemented and compared to forecast the dengue outbreak accurately. The research shows that humidity and mean maximum temperature are the major climate factors and exhibit strong positive and negative correlation, respectively, with dengue incidences for all locations of Maharashtra state. Mean minimum temperature and rainfall are moderately positively correlated with dengue incidences. Mean wind speed is a less significant factor and is weakly negatively correlated with dengue incidences. Root mean square error (RMSE), mean absolute error (MAE), and R square error (R2) evaluation metrics are used to compare the performance of the prediction model. Random Forest Regression is the best-fit regression model for five out of nine cities, while Support Vector Regression is for two cities. Facebook Prophet Model is the best fit time series forecasting model for six out of nine cities. Based on the prediction, Mumbai, Thane, Nashik, and Pune are the high-risk regions, especially in August, September, and October. The findings exhibit an effective early warning system that would predict the outbreak of other infectious diseases. It will help the relevant authorities to take accurate preventive measures.

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Dengue Vector Population Forecasting Using Multisource Earth Observation Products and Recurrent Neural Networks

Cited by 15 publications

References 36 publications

Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review

Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review

A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions

Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models

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