Modelo preditivo para a proliferação do Aedes aegypti em Itajaí (Santa Catarina): Uma abordagem integrando fatores climáticos locais e globais

Eduardo Ribeiro; Cleusa Matiola; Mario Quadro; Matheus Souza; Isabel Bohn; João Fuck; Raul Guimarães; Thiago Alves

doi:10.53455/re.v5i1.207

Authors

Eduardo Augusto Werneck Ribeiro Instituto Federal Catarinense https://orcid.org/0000-0003-3313-6783
Cleusa Matiola Secretaria de Educação do estado de Santa Catarina https://orcid.org/0000-0002-6902-1307
Mario Francisco Leal de Quadro Instituto Federal de Educação, Ciência e Tecnologia de Santa Catarina
Matheus Ferreira de Souza Instituto Federal de Educação, Ciência e Tecnologia de Santa Catarina https://orcid.org/0000-0002-9539-5695
Isabel Cristina Bohn Secretaria Municipal de Educação de Navegantes https://orcid.org/0000-0003-2338-9322
João Augusto Brancher Fuck Secretaria de Estado da Saúde de Santa Catarina https://orcid.org/0000-0001-8716-3432
Raul Borges Guimarães Universidade Estadual Paulista (Unesp)
Thiago Pereira Alves Instituto Federal de Educação, Ciência e Tecnologia de Santa Catarina https://orcid.org/0000-0002-0939-1306

DOI:

https://doi.org/10.53455/re.v5i1.207

Keywords:

Random Forest, Predictive model, Climatic variability, Aedes aegypti

Abstract

Context: Itajai, a coastal city in Santa Catarina, faces unique challenges related to the proliferation of Aedes aegypti, a vector of several diseases. This article presents a predictive model developed to forecast Aedes aegypti hotspots in the region, considering the complex interaction between local climatic variables and global phenomena such as El Niño and La Niña. Methodology: Using a Random Forest algorithm, the model is capable of capturing non-linear relationships in the data, providing insights into the influence of climatic factors on mosquito activity. The choice of this algorithm is due to its robustness and ability to consider the multiplicity of factors influencing mosquito proliferation. By grouping data by season, the model incorporates seasonal nuances, reflecting climatic variations in Itajai. Considerations: The integration of broader climatic patterns highlights the interconnection between local and global factors. This model offers a valuable tool for Itajai's health authorities, enabling proactive actions and resource optimization in the fight against Aedes aegypti. In short, this study proposes an innovative and practical approach to the prevention of mosquito-borne diseases, with potential to positively impact public health in similar regions.

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References

Alexander J, Wilke ABB, Mantero A, Vasquez C, Petrie W, Kumar N, et al. (2022) Using machine learning to understand microgeographic determinants of the Zika vector, Aedes aegypti. PLoS ONE 17(12): e0265472. https://doi.org/10.1371/journal.pone.0265472 DOI: https://doi.org/10.1371/journal.pone.0265472

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ. Computer science, 7, e623. https://doi.org/10.7717/peerj-cs.623 DOI: https://doi.org/10.7717/peerj-cs.623

Guerra, C., & Quadro, M. (2021). Estudo da variabilidade diurna da precipitação região sul do Brasil. Estrabão, 2(1), 205–209. https://doi.org/10.53455/re.v2i.56 DOI: https://doi.org/10.53455/re.v2i.56

Jupyter. (s.d.). Recuperado de https://jupyter.org/

Matiola, C. Avaliação da relação entre focos de Aedes aegypti com a distribuição espacial da temperatura nomunicípio de Itajaí/SC, por geoprocessamento. Metodologias e Aprendizado, 1, 30 – 35, 2019. https://doi.org/10.21166/metapre.v1i0.645 DOI: https://doi.org/10.21166/metapre.v1i0.645

Matiola, C. et al. O uso de dados de temperatura e precipitação deMERRA2 para compreender a dinâmica ecológica do A. aegypti no município de Chapecó/SC - 2007 a 2017. Revista Brasileira de Geografia Física, v. 12, n. 4, p. 1385-1398, nov. 2019.https://doi.org/10.26848/rbgf.v12.4.p1385-1398 DOI: https://doi.org/10.26848/rbgf.v12.4.p1385-1398

Ochida, N., Mangeas, M., Dupont‐Rouzeyrol, M., Dutheil, C., Forfait, C., Peltier, A., … & Menkès, C. E. (2022). Modeling present and future climate risk of dengue outbreak, a case study in new caledonia. Environmental Health, 21(1). https://doi.org/10.1186/s12940-022-00829-z DOI: https://doi.org/10.1186/s12940-022-00829-z

Rahman, M. S., Pientong, C., Zafar, S., Ekalaksananan, T., Paul, R. E., Haque, U., Rocklöv, J., & Overgaard, H. J. (2021). Mapping the spatial distribution of the dengue vector Aedes aegypti and predicting its abundance in northeastern Thailand using machine-learning approach. One Health, 13(7446), 100358. https://doi.org/10.1016/j.onehlt.2021.100358 DOI: https://doi.org/10.1016/j.onehlt.2021.100358

Ribeiro, E., Matiola, C., Mattedi, M., Bohn, I., Fuck, J., Guimaraes, R., Quadro, M., & Alves, T. (2023). Desafios e Aprendizados na Aplicação do Projeto Índice de Positividade de Armadilhas (IPA): Um Estudo sobre o Controle do Aedes aegypti em Santa Catarina, Brasil. Metodologias E Aprendizado, 6, 740–743. https://doi.org/10.21166/metapre.v6i.4174 DOI: https://doi.org/10.21166/metapre.v6i.4174

Santos, J. M., Capinha, C., Rocha, J., & Sousa, C. A. (2022). The current and future distribution of the yellow fever mosquito (Aedes aegypti) on Madeira Island. PLOS Neglected Tropical Diseases, 16(9), e0010715. https://doi.org/10.1371/journal.pntd.0010715 DOI: https://doi.org/10.1371/journal.pntd.0010715