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The Use of Spaceborne and Oceanic Sensors to Model Dengue Incidence in the Outbreak Surveillance System

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Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11619))

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Abstract

This research focuses on the development of a computational data-driven modeling method to be used in the dengue outbreak surveillance system. The outbreak-level forecasting is based on the estimation of dengue fever cases in Thailand using both statistical and data mining techniques. Major statistical techniques used in this research are linear regression and generalized linear model. The data mining algorithms used in our study are chi-squared automatic interaction detection (CHAID), classification and regression tree, artificial neural network, and support vector machine. The input data are from four sources, which are remotely sensed indices from the NOAA satellite to represent vegetation health and other related weather conditions, rainfall, the oceanic Niño index (ONI) for justifying climate variability affecting amount of rainfall, and historical dengue cases in Thailand to be used as the modeling target. In the modeling process, these data are lagged from 1 up to 24 months to observe time-series effect. On comparing performances of models built from different algorithms, we found that CHAID is the best one yielding the least error on estimating dengue cases. From the CHAID models to forecast dengue cases in Bangkok metropolitan and Nakhon Ratchasima in the northeast of Thailand, the high level of ONI is the most important factor. The large amount of rainfall is significant factor contributing to dengue outbreak in Chiang Mai in the north and Songkhla in the south.

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Acknowledgment

This work was financially supported by grants from the Thailand Toray Science Foundation, the National Research Council of Thailand, and Suranaree University of Technology through the funding of the Data and Knowledge Engineering Research Unit.

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Authors and Affiliations

  1. Data and Knowledge Engineering Research Unit, Suranaree University of Technology, Nakhon Ratchasima, Thailand

    Kittisak Kerdprasop & Nittaya Kerdprasop

  2. School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand

    Kittisak Kerdprasop, Nittaya Kerdprasop & Kacha Chansilp

  3. Department of Environmental Science, Thammasat University, Pathumthani, Thailand

    Paradee Chuaybamroong

Authors
  1. Kittisak Kerdprasop
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  2. Nittaya Kerdprasop
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  3. Kacha Chansilp
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  4. Paradee Chuaybamroong
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Corresponding author

Correspondence to Nittaya Kerdprasop .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Kerdprasop, K., Kerdprasop, N., Chansilp, K., Chuaybamroong, P. (2019). The Use of Spaceborne and Oceanic Sensors to Model Dengue Incidence in the Outbreak Surveillance System. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11619. Springer, Cham. https://doi.org/10.1007/978-3-030-24289-3_33

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  • DOI: https://doi.org/10.1007/978-3-030-24289-3_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24288-6

  • Online ISBN: 978-3-030-24289-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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