Infectious disease and climate change: detecting contributing factors and predicting future outbreaks

Andrick, B.; Clark, Benjamin M.; Nygaard, K; Logar, A.; Penaloza, Manuel; Welch, Ronald M.

doi:10.1109/igarss.1997.609159

Cited by 13 publications

(10 citation statements)

References 7 publications

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“…the seasonal change of temperature, humidity and rainfall. From the literature reviewed in [2], it had been concluded that the rise in temperature does accelerate the lifecycle of the mosquito. This agrees with the findings from our model, i.e.…”

Section: Resultsmentioning

confidence: 98%

“…For example, the Aedes aegypti mosquito cannot survive below the freezing temperature of 0 ı C [2]. However, climatic conditions worldwide have displayed enormous shortterm abnormalities, which increases the likelihood of mosquitoes surviving longer and penetrating temperate regions that were previously free from mosquito-borne diseases [2].…”

Section: Introductionmentioning

confidence: 99%

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Mining Weather Information in Dengue Outbreak: Predicting Future Cases Based on Wavelet, SVM and GA

Lee

et al. 2009

Lecture Notes in Electrical Engineering

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Dengue Fever has existed throughout the contemporary history of mankind and poses an endemic threat to most tropical regions. Dengue virus is transmitted to humans mainly by the Aedes aegypti mosquito. It has been observed that there are significantly more Aedes aegypti mosquitoes present in tropical areas than in other climate regions. As such, it is commonly believed that the tropical climate suits the life-cycle of the mosquito. Thus, studying the correlation between the climatic factors and trend of dengue cases is helpful in conceptualising a more effective pre-emptive control measure towards dengue outbreaks. In this chapter, a novel methodology for forecasting the number of dengue cases based on climactic factors is presented. We proposed to use Wavelet transformation for data pre-processing before employing a Support Vector Machines (SVM)-based Genetic Algorithm to select the most important features. After which, regression based on SVM was used to perform forecasting of the model. The results drawn from this model based on dengue data in Singapore showed improvement in prediction performance of dengue cases ahead. It has also been demonstrated that in this model, prior climatic knowledge of 5 years is sufficient to produce satisfactory prediction results for up to 2 years. This model can help the health control agency to improve its strategic planning for disease control to combat dengue outbreak. The experimental result arising from this model also suggests strong correlation between the monsoon seasonality and dengue virus transmission. It also confirms previous work that showed mean temperature and monthly seasonality contribute minimally to outbreaks.

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Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Mining Weather Information in Dengue Outbreak: Predicting Future Cases Based on Wavelet, SVM and GA

Lee

et al. 2009

Lecture Notes in Electrical Engineering

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“…One of the ways in achieving this is by having a good prediction on the existence of the outbreak. This kind of predictions helps the higher authorities as well as individuals to devise plans and handle the situation in an e cient and effective manner [14,15]. The accuracy of a prediction system for outbreaks is the primary and important concern for controlling dengue fever [14].…”

Section: Introductionmentioning

confidence: 99%

“…Various early warning and monitoring systems are currently implemented to monitor dengue outbreaks worldwide. Dengue prediction models have been previously investigated, but some of these models still exhibit limitations in achieving high accuracy in dengue outbreak prediction [14,15]. Different models and techniques have been integrated into the design of several models for predicting dengue outbreaks.…”

Section: Introductionmentioning

confidence: 99%

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

Nejad

Varathan

2020

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. 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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Infectious disease and climate change: detecting contributing factors and predicting future outbreaks

Cited by 13 publications

References 7 publications

Mining Weather Information in Dengue Outbreak: Predicting Future Cases Based on Wavelet, SVM and GA

Mining Weather Information in Dengue Outbreak: Predicting Future Cases Based on Wavelet, SVM and GA

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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