Abstract
Leprosy is still a public health problem in the world and in Brazil. The environment conducive to the proliferation of this disease has a close relationship with precarious socioeconomic conditions, such as housing, sanitation, schooling and income. The present work analyzed an approach to combat leprosy through the modeling of a vulnerability index for the disease based on the socioeconomic information of the microregions defined by the census tracts belonging to the municipality of Santarém. Through the use of the information that compose this index, the process of creating clusters was carried out using the Kohonen self organizing maps of the census tracts analyzed according to their vulnerability to leprosy. Four clusters were found representing very high, low, and very low vulnerability for leprosy among the census tracts. We analyzed the 240 sectors that make up the urban area of Santarém, where 132 were classified in clusters with high vulnerability and 108 in clusters with low vulnerability. The study demonstrated that the use of organized socioeconomic information in an index that could express the vulnerability of census tracts to leprosy and the organization of these sectors in clusters are very powerful tools in the decision support process applied to the fight against leprosy in hyperendemic regions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Health Surveillance Guide, Brasil (2014). (in Portuguese)
World Health Organization (WHO): Leprosy (2016). Disponível em. https://www.who.int/lep/. Acesso em 10 de maio de 2016
Amaral, E.P.: Spatial analysis of leprosy in the microregion of Almenara - Minas Gerais: relationship between epidemiological status and socioeconomic conditions (2008)
Health Surveillance Guide, Brasil (2009). (in Portuguese)
Meireles, L.T.M.: Zoning of leprosy cases reported in the period 2003 to 2013 in the municipality of santarém using spatial analysis techniques. Dissertation (Master in Biosciences) - Post-graduation Biosciences, Federal University of West of Pará, Santarém (2015)
Hino, P., Villa, T.C.S., Cunha, T.N., Santos, C.B.: Spatial distribution of endemic diseases in the city of Ribeirão Preto, São Paulo, Brazil. Rev. Ciência e Saúde Coletiva (2011)
Pereira, C.J.A., Gerardi, L.H.O.: GAIA- Geoprocessing, Intelligent Computing and Free Software. Computação/DCET, Universidade Estadual de Santa Cruz (UESC), Ilhéus (2009)
Xu, R., Wunsch, D.: Clustering. IEEE Press, Piscataway (2009)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)
Snow, J.: On the Way of Transmission of Cholera, 2nd edn, p. 249. Hucitec, São Paulo (1999)
Soares, S.R.F., Ochi, L.S.: An evolutionary algorithm with reconnection of paths to the problem of automatic clustering (2004)
Health Surveillance Guide, Brasil (2008). (in Portuguese)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: The KDD process for extracting useful knowledge from volumes of data. Commun. ACM 39(11) 27–34 (1996a). The process of knowledge discovery in databases
Carvalho, D.R.: Data mining through introduction of rules and genetic algorithms. Masters dissertation - PUCPR, Curitiba (1999)
Barreto, J.G.: Spatial and serological epidemiology of leprosy in the State of Pará. Doctoral thesis. Universidade Federal do Pará (UFPA), Belém, Pará (2013)
Cardoso, O.N.P.: Knowledge management using data mining: a case study at the Federal University of Lavras, Rio de Janeiro (2008)
Dutra, R.M.O.: The ward method of clustering and its application in association with Kohonen’s self-organizing maps. Federal University of Santa Catarina (UFSC) (2008)
Alves, H.P.F.: Socio-environmental vulnerability in the metropolis of São Paulo: a sociodemographic analysis of the situations of spatial overlapping of social and environmental problems and risks, São Paulo (2006)
Villar, J.F.C.: Relationship between the social, economic and environmental variables with the pattern of space-time distribution of Dengue cases by municipality in Brazil: from 2008 to 2012, using SOM. New University of Lisbon – ISEGI (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
da Silva, R.E., Conde, V.M.G., da Silva Baia, M.J., Salgado, C.G., Conde, G.A.B. (2020). Modeling a Vulnerability Index for Leprosy Using Spatial Analysis and Artificial Intelligence Techniques in a Hyperendemic Municipality in the Amazon. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_60
Download citation
DOI: https://doi.org/10.1007/978-3-030-29513-4_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29512-7
Online ISBN: 978-3-030-29513-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)