Automatic identification of knowledge related to dengue cases in the state of Piauí in public databases using Filtered-Association Rules Networks

Joan Davi Santos Silva, Jâina Carolina Meneses Calçada, Solange Oliveira Rezende, Dario Brito Calçada

Abstract


Dengue is an endemic disease in Brazil since the 1980s and since 1996 in Piau ́ı. The number of cases increases each year, with the incidence of more severe symptoms. This research aimed to evaluate the use of an automatic knowledge identification technique in factors related to the number of dengue occurrences. We built a dataset formed by data available in the Information System for Notifiable Diseases (SINAN) and meteorological data of the municipalities of the coastal plain of Piau ́ı. The technique used was that of Filtered Association Rules Networks, which allows visual analysis of knowledge through the use of network structures and rules filtering. As a main result, we confirmed the understanding that the most significant number of cases occurs in May, as it is the moment when the rainfall indexes are decreasing, besides that socio-cultural and race factors do not interfere in the identification of the population of higher risk. This research presents the innovation of the use of a computational technique of automatic knowledge discovery that can assist in the elaboration of prevention actions by epidemiological surveillance.


Keywords


Association Rules;Dengue;Epidemiological surveillance;Knowledge Discovery;Networks

Full Text:

PDF

References


WHO. Dengue and severe dengue. 2019. Disponível em: https://www.who.int/en/news-room/fact-sheets/detail/dengue-and-severe-dengue.

FARES, R. C. G. et al. Epidemiological Scenario of Dengue in Brazil. BioMed Research International, Hindawi Limited, v. 2015, p. 1–13, 2015. Disponível em: https://doi.org/10.1155/2015/321873.

BRAGA, I. A.; VALLE, D. Aedes aegypti: histórico do controle no Brasil. Epidemiologia e Serviços de Saúde, SciELO, v. 16, p. 113 – 118, 06 2007. Disponível em: http://scielo.iec.gov.br/scielo.php?script=sci_arttext&pid=S1679-49742007000200006&nrm=iso.

MONTEIRO, E. S. C. et al. Aspectos epidemiológicos e vetoriais da dengue na cidade de Teresina, Piauí-Brasil, 2002 a 2006. Epidemiologia e Serviços de Saúde, SciELO, v. 18, p. 365 – 374, 12 2009. Disponível em: http://scielo.iec.gov.br/scielo.php?script=sci_arttext&pid=S1679-49742009000400006&nrm=iso.

HII, Y. L. et al. Forecast of dengue incidence using temperature and rainfall. PLOS Neglected Tropical Diseases, Public Library of Science, v. 6, n. 11, p. 1–9, 11 2012. Disponível em: https://doi.org/10.1371/journal.pntd.0001908.

THOMSON, M. C. et al. Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana. The American journal of tropical medicine and hygiene, ASTMH, v. 73, n. 1, p. 214–221, 2005.

DEGALLIER, N. et al. Toward an early warning system for dengue prevention: modeling climate impact on dengue transmission. Climatic Change, Springer, v. 98, n. 3-4, p. 581–592, 2010.

WANG, J.; OGDEN, N. H.; ZHU, H. The impact of weather conditions on culex pipiens and culex restuans (diptera: Culicidae) abundance: a case study in peel region. Journal of medical entomology, Oxford University Press Oxford, UK, v. 48, n. 2, p. 468–475, 2011.

SINAN. Sistema de Informação de Agravos de Notificação. 2016. Disponível em: http://www.portalsinan.saude.gov.br.

TRINDADE, C. M. Identificação do Comportamento das Hepatites Virais a partir da exploração de bases de dados de Saúde Pública. 2005, 139f. Tese (Doutorado) — Dissertação (Mestrado em Tecnologia em Saúde)-Pontifícia Universidade Católica do Paraná, PUCPR, 2005., 2005.

ANGUERA, A. et al. Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry. Computational and Structural Biotechnology Journal, Elsevier BV, v. 14, p. 185–199, 2016. Disponível em: https://doi.org/10.1016/j.csbj.2016.05.002.

FATHIMA, A. S.; MANIMEGALAI, D.; HUNDEWALE, N. A review of data mining classification techniques applied for diagnosis and prognosis of the arbovirus-dengue. International Journal of Computer Science Issues (IJCSI), Citeseer, v. 8, n. 6, p. 322, 2011.

CALÇADA, D. B.; PADUA, R. de; REZENDE, S. O. Asymmetric Objective Measures applied to Filter Association Rules Networks. In: XLIV Latin American Computer Conference (CLEI) Asymmetric. São Paulo: [s.n.], 2018. p. 258–267.

WENG, C.-H. Identifying association rules of specific later-marketed products. Applied Soft Computing, Elsevier, v. 38, p. 518–529, 2016.

CALÇADA, D. B. Redes de regras de associação filtradas e multialvo. 199 p. Tese (Doutorado) — Universidade de São Paulo, 2019.

BUCZAK, A. L. et al. Prediction of High Incidence of Dengue in the Philippines. PLoS Neglected Tropical Diseases, Public Library of Science (PLoS), v. 8, n. 4, p. e2771, abr. 2014. Disponível em: https://doi.org/10.1371/journal.pntd.0002771.

STOLERMAN, L. M.; MAIA, P. D.; KUTZ, J. N. Forecasting dengue fever in Brazil: An assessment of climate conditions. PLOS ONE, Public Library of Science (PLoS), v. 14, n. 8, p. e0220106, ago. 2019. Disponível em: https://doi.org/10.1371/journal.pone.0220106.

SANTOS, C. A. G. et al. Correlation of dengue incidence and rainfall occurrence using wavelet transform for João Pessoa city. Science of The Total Environment, Elsevier BV, v. 647, p. 794–805, jan. 2019. Disponível em: https://doi.org/10.1016/j.scitotenv.2018.08.019.

XU, H.-Y. et al. Statistical Modeling Reveals the Effect of Absolute Humidity on Dengue in Singapore. PLoS Neglected Tropical Diseases, Public Library of Science (PLoS), v. 8, n. 5, p. e2805, maio 2014. Disponível em: https://doi.org/10.1371/journal.pntd.0002805.

VALDEZ, L.; SIBONA, G.; CONDAT, C. Impact of rainfall on Aedes aegypti populations. Ecological Modelling, Elsevier BV, v. 385, p. 96–105, out. 2018. Disponível em: https://doi.org/10.1016/j.ecolmodel.2018.07.003.

FAYYAD, U.; PIATETSKY-SHAPIRO, G.; SMYTH, P. From data mining to knowledge discovery in databases. AI magazine, v. 17, n. 3, p. 37–37, 1996.

OLARU, C.; GEURTS, P.; WEHENKEL, L. Data mining tools and application in power system engineering. In: TRONDHEIM, NORWAY. Proceedings of the 13th Power System Computation Conference, PSCC99. [S.l.], 1999. p. 324–330.

AGRAWAL, R.; SRIKANT, R. et al. Fast algorithms for mining association rules. In: Proc. 20th int. conf. very large data bases, VLDB. [S.l.: s.n.], 1994. v. 1215, p. 487–499.

PANDEY, G. et al. Association Rules Network: Definition and Applications. Statistical Analysis and Data Mining, v. 1, n. 4, p. 260–179, 2009.

SAHAR, S. What is interesting: studies on interestingness in knowledge discovery. Phd Thes, Tel-Aviv University The, Citeseer, 2003.

FUKUDA, T. et al. Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization. Acm Sigmod Record, ACM, v. 25, n. 2, p. 13–23, 1996.

BASTIAN, M.; HEYMANN, S.; JACOMY, M. Gephi: an open source software for exploring and manipulating networks. In: Third international AAAI conference on weblogs and social media. [S.l.: s.n.], 2009.




DOI: https://doi.org/10.22456/2175-2745.99849

Copyright (c) 2020 Dario Brito Calçada, Joan Davi Santos da Silva, Jâina Carolina Meneses Calçada, Solange Oliveira Rezende

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Indexing databases:
        

Acknowledgments: