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Mining Epidemiological Dengue Fever Data from Brazil: A Gradual Pattern Based Geographical Information System

  • Yogi Satrya Aryadinata
  • Yuan Lin
  • C. Barcellos
  • Anne Laurent
  • Therese Libourel
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 443)

Abstract

Dengue fever is the world’s fastest growing vector-borne disease. Studying such data aims at better understanding the behaviour of this disease to prevent the dengue propagation. For instance, it may be the case that the number of cases of dengue fever in cities depends on many factors, such as climate conditions, density, sanitary conditions. Experts are interested in using geographical information systems in order to visualize knowledge on maps. For this purpose, we propose to build maps based on gradual patterns. Such maps provide a solution for visualizing for instance the cities that follow or not gradual patterns.

Keywords

Epidemiological Data Data Mining Geographic Information Systems Gradual Patterns 

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References

  1. 1.
    Organization, W.H.: The World Health Report 2006: Working together for health (2006)Google Scholar
  2. 2.
    Gubler, D., Ooi, E., Vasudevan, S., Farrar, J.: Dengue and Dengue Hemorrhagic Fever. CABI (2013)Google Scholar
  3. 3.
    Gubler, D.J., et al.: Dengue, urbanization and globalization: The unholy trinity of the 21st century. Tropical Medicine and Health 39(4 suppl.), 3–11 (2011)CrossRefGoogle Scholar
  4. 4.
    Kovats, R., Campbell-Lendrum, D., McMichael, A., Woodward, A., Cox, J.: Early effects of climate change: do they include changes in vector-borne disease? Philos. Trans. R. Soc. Lond. B. Biol. Sci. 356(1411), 1057–1068 (2001)CrossRefGoogle Scholar
  5. 5.
    Souza-Santos, R.: The factors associated with the occurrence of immature forms of aedes aegypti in Ilha do Governador, Rio de Janeiro, Brazil. Rev. Soc. Bras. Med. Trop. 32(4), 373–382 (1999)CrossRefGoogle Scholar
  6. 6.
    Souza-Santos, R., Carvalho, M.: Spatial analysis of aedes aegypti larval distribution in the Ilha do Governador neighborhood of Rio de Janeiro, Brazil. Cad. Saude Publica 16(1) (2000)Google Scholar
  7. 7.
    Yang, H.M., Macoris, M.L., Galvani, K.C., Andrighetti, M.T., Wanderley, D.M.: Dinâmica da transmissao da dengue com dados entomológicos temperatura-dependentes. Tema–Tend. Mat. Apl. Comput. 8(1), 159 (2007)Google Scholar
  8. 8.
    Teurlai, M., Huy, R., Cazelles, B., Duboz, R., Baehr, C., Vong, S.: Can human movements explain heterogeneous propagation of dengue fever in Cambodia? PLoS Negl. Trop. Dis. 6(12), e1957 (2012)Google Scholar
  9. 9.
    Chowell, G., Cazelles, B., Broutin, H., Munayco, C.V.: The influence of geographic and climate factors on the timing of dengue epidemics in Peru, 1994-2008. BMC Infectious Diseases 11, 164 (2011)CrossRefGoogle Scholar
  10. 10.
    Catão, R.D.C., Guimarães, R.B.: Mapeamento da reemergência do dengue no Brasil–1981/82-2008. Hygeia 7(13) (2011)Google Scholar
  11. 11.
    McMichael, A., Lindgren, E.: Climate change: present and future risks to health, and necessary responses. J. Intern. Med. 270(5), 401–413 (2011)CrossRefGoogle Scholar
  12. 12.
    Di-Jorio, L., Laurent, A., Teisseire, M.: Mining frequent gradual itemsets from large databases. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 297–308. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yogi Satrya Aryadinata
    • 1
  • Yuan Lin
    • 2
  • C. Barcellos
    • 3
  • Anne Laurent
    • 1
  • Therese Libourel
    • 2
  1. 1.LIRMMMontpellierFrance
  2. 2.UMR ESPACE-DEV (IRD-UM2)MontpellierFrance
  3. 3.Fundaćõ Oswaldo CruzRio de JaneiroBrazil

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