New Key Factors Discovery to Enhance Dengue Fever Forecasting Model

Phatsavee Ongruk; Padet Siriyasatien; Kraisak Kesorn

doi:10.4028/www.scientific.net/AMR.931-932.1457

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 931-932New Key Factors Discovery to Enhance Dengue Fever...

New Key Factors Discovery to Enhance Dengue Fever Forecasting Model

Abstract:

There are several factors that can be used to predict a dengue fever outbreak. Almost all existing research approaches, however, usually exploit the use of a basic set of core attributes to forecast an outbreak, e.g. temperature, humidity, wind speed, and rainfall. In contrast, this research identifies new attributes to improve the prediction accuracy of the outbreak. The experimental results are analyzed using a correlation analysis and demonstrate that the density of dengue virus infection rate in female mosquitoes and seasons have strong correlation with a dengue fever outbreak. In addition, the research constructs a forecast model using Poisson regression analysis. The result shows the proposed model obtains significantly low forecasting error rate when compared it against the conventional model using only temperature, humidity, wind speed, and rainfall parameters.

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Periodical:

Advanced Materials Research (Volumes 931-932)

Pages:

1457-1461

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.931-932.1457

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Online since:

May 2014

Authors:

Phatsavee Ongruk, Padet Siriyasatien, Kraisak Kesorn*

Keywords:

Data Mining (DM), Dengue Fever, Factor, Forecasting Model, Poisson Regression

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* - Corresponding Author

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