Forecasting number of dengue patients using cellular automata model

Soemsap, Trirat; Wongthanavasu, Sartra; Satimai, Wichai

doi:10.1109/ieecon.2014.6925876

Cited by 13 publications

(17 citation statements)

References 5 publications

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“…Hybrid models have been used in outbreak prediction research. A hybrid model is an example of an integrated model, and many models based on genetic algorithms are available to determine the weight in a neural network model [11,13,14,20,26]. In Singapore, researchers found significant correlated dengue cases with climatic variables by using a Poisson regression model [27].…”

Section: Related Workmentioning

confidence: 99%

“…Thus, establishing related risk factors is critical for prediction systems [12]. Given that climate factors play a key role in this disease, identifying the relation between weather information and dengue outbreak incidence is a major task in establishing an accurate prediction system for future outbreaks [13,14]. In the current study, important climatic risk factors, such as temperature, relative humidity and rainfall amount, were examined.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Nejad

Varathan

2019

Preprint

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Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. These factors were used as input parameters for machine learning models. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Nejad

Varathan

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, establishing related risk factors is critical for prediction systems [16]. Given that climate factors play a key role in this disease, identifying the relation between weather information and dengue outbreak incidence is a major task in establishing an accurate prediction system for future outbreaks [17,18,19]. In the current study, important climatic risk factors, such as temperature, relative humidity and rainfall amount, were examined.…”

Section: Introductionmentioning

confidence: 99%

“…Hybrid models have been used in outbreak prediction research. A hybrid model is an example of an integrated model, and many models based on genetic algorithms are available to determine the weight in a neural network model [14,17,18,19,25,26]. In Singapore, researchers found signi cant correlated dengue cases with climatic variables by using a Poisson regression model [27].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Nejad

Varathan

2020

Preprint

View full text Add to dashboard Cite

Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks. Conclusions: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

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Prediction of Seasonal Ailments Using Big Data

Indhumathi

Kumar

2023

Mathematics and Computer Science Volume 1

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Forecasting number of dengue patients using cellular automata model

Cited by 13 publications

References 5 publications

Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Prediction of Seasonal Ailments Using Big Data

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