Grey Wolf Optimization-Based Big Data Analytics for Dengue Outbreak Prediction

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 645)

Abstract

In recent decades, dengue fever (DF) and dengue hemorrhagic fever (DHF) outbreaks have occurred frequently in many tropical and subtropical regions of Asia. Big data-driven analytics are recently facilitated on the large dataset to monitor climate-driven changes and also for the dengue outbreak prediction. Many past studies have established an association between the meteorological variables and the dengue incidence. Hence, this paper examines the effects of meteorological factors on dengue incidence in one of the climatic categories of the tropical region in Asia. The nine meteorological parameters and number of dengue cases per week were considered in this study. Subsequently, the most influencing variables of dengue incidence were selected using a new heuristic optimization algorithm such as grey wolf optimization (GWO) based on Adaptive Neuro-Fuzzy Inference System (ANFIS) followed by negative binomial regression model which was employed to evaluate various lag times between dengue incidences and meteorological variables. Therefore, the derived meteorological variables with a time lag period are utilized for the big data analytics of dengue outbreak prediction.

Keywords

Dengue Feature selection Optimization algorithm Grey wolf optimization Negative binomial regression 

References

  1. 1.
    Rajapakse, S., Rodrigo, C., Rajapakse, A.: Treatment of dengue fever. Infect Drug Resist. 5, 103–112 (2012)CrossRefGoogle Scholar
  2. 2.
    Rasgon, J.L.: Dengue fever: mosquitoes attacked from within. Nature 476, 407–408 (2011)CrossRefGoogle Scholar
  3. 3.
    Brady, O.J., Gething, P.W., Bhatt, S., Messina, J.P., Brownstein, J.S., et al.: Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl Trop Dis 6, e1760 (2012)CrossRefGoogle Scholar
  4. 4.
    WHO: Dengue guidelines for diagnosis, treatment, prevention and control: World Health Organization. 1–147 p (2009)Google Scholar
  5. 5.
    Egbendewe-Mondzozo, A., Musumba, M., McCarl, B.A., Wu, X.: Climate change and vector-borne diseases: an economic impact analysis of malaria in Africa. Int. J. Environ. Res. Public Health 8, 913–930 (2011)CrossRefGoogle Scholar
  6. 6.
    Huang, F., Zhou, S., Zhang, S., Wang, H., Tang, L.: Temporal correlation analysis between malaria and meteorological factors in Motuo County. Tibet. Malar J 10, 54 (2011)CrossRefGoogle Scholar
  7. 7.
    Haque, U., Hashizume, M., Glass, G.E., Dewan, A.M., Overgaard, H.J., et al.: The role of climate variability in the spread of malaria in Bangladeshi highlands. PLoS ONE 5, e14341 (2010)CrossRefGoogle Scholar
  8. 8.
    Traerup, S.L., Ortiz, R.A., Markandya, A.: The costs of climate change: a study of cholera in Tanzania. Int. J. Environ. Res. Public Health 8, 4386–4405 (2011)CrossRefGoogle Scholar
  9. 9.
    Xu, L., Liu, Q., Stige, L.C., Ben Ari, T., Fang, X., et al.: Nonlinear effect of climate on plague during the third pandemic in China. Proc. Natl. Acad. Sci. USA 108, 10214–10219 (2011)CrossRefGoogle Scholar
  10. 10.
    Ari, T.B., Gershunov, A., Tristan, R., Cazelles, B., Gage, K., et al.: Interannual variability of human plague occurrence in the Western United States explained by tropical and North Pacific Ocean climate variability. Am. J. Trop. Med. Hyg. 83, 624–632 (2010)CrossRefGoogle Scholar
  11. 11.
    Fairos, W.Y.W., Azaki, W.H.W., Alias, M., Wah, Y.B.: Modelling dengue fever (DF) and dengue hemorrhagic fever (DHF) outbreak using Poisson and negative binomial model. World Acad. Sci. Eng. Technol. Int. J. Math. Comput. Nat. Phys. Eng. 4(2) (2010)Google Scholar
  12. 12.
    Ahmed, S.A., Siddiqi, J.S., Quaiser, S., Kamal, S.: Using PCA, Poisson and negative binomial model to study the climatic factor and dengue fever outbreak in Lahore. J. Basic Appl. Sci. 11, 8–16 (2015)CrossRefGoogle Scholar
  13. 13.
    Siriyasatien, P., Phumee, A., Ongruk, P., Jampachaisri, K., Kesorn, K.: Analysis of significant factors for dengue fever incidence prediction. BMC Bioinform. 17, 166 (2016)CrossRefGoogle Scholar
  14. 14.
    Honda, Y., Ono, M.: Issues in health risk assessment of current and future heat extremes. Glob. Health Action 2 (2009)Google Scholar
  15. 15.
    Epidemiology Unit, Ministry of Health, Sri Lanka. http://www.epid.gov.lk/web/index.php
  16. 16.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimization. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  17. 17.
    Kira, K., Rendell, L.: The feature selection problem: traditional methods and a new algorithm. In: Proceedings of AAAI’92, San Jose, CA, AAAI (press) (1992)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ApplicationsS.A Engineering CollegeChennaiIndia
  2. 2.Department of Computer Science and EngineeringPSG College of TechnologyCoimbatoreIndia

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