Risk classification in complex networks: the case of COVID-19 in Rio Grande do Sul

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DOI:

https://doi.org/10.5902/2179460X66864

Keywords:

Phytotherapy, Health, Pharmaceutical preparations, Clinical trial

Abstract

Peixoto et al. (2020) developed a methodology that uses data from the mobility of people to classify municipalities in a state into three risk zones (low, medium andhigh) in relation to airborne contagious disease. The authors applied the model to the states of São Paulo and Rio de Janeiro, before the COVID-19 virus was present in a large number of municipalities in the state. Our objective in this article is to evaluate how this risk classification methodology applies to Rio Grande do Sul and how it relates to the actual evolution of COVID-19 in the state. Using the methodology, we obtained a classification of the municipalities in the state into three risk groups. According to this classification, with rare exceptions, municipalities closer to Porto Alegre have been classified as high risk. The mountain region, the coast and some municipalities in the west of the state have been classified as medium risk. The other municipalities have been classified as low risk. In comparison with official data on the spread of the disease in the state, we found that the risk attributed was consistent with the evolution of COVID-19. From a methodological point of view, we found evidence, via spectral clustering, that dividing the municipalities into three groups is the best choice for our data.

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Author Biographies

Lucas Siviero Sibemberg, Universidade Federal do Rio Grande do Sul, Rio Grande do Sul, RS

Possui graduação em Matemática - Licenciatura pela Universidade Federal do Rio Grande do Sul (2016 - 2019). Atualmente é Mestrando do Programa de Pós-Graduação em Matemática Aplicada pela Universidade Federal do Rio Grande do Sul com bolsa de Dedicação Exclusiva do Cnpq, orientado pelos Prof. Dr. Luiz Emilio Allem e pelo Prof. Dr. Carlos Hoppen.

Luiz Emilio Allem, Universidade Federal do Rio Grande do Sul, Rio Grande do Sul, RS

É Bacharel em Matemática pela Universidade Federal do Rio Grande do Sul (2002), Mestre em Matemática Aplicada pela Universidade Federal do Rio Grande do Sul (2005) e Doutor em Matemática Aplicada pela Universidade Federal do Rio Grande do Sul (2010) com período sanduíche em Clemson University. Pós-doutorado no Instituto de Matemática da UFRGS. Professor da Universidade Federal do Rio Grande do Sul no Instituto de Matemática. As áreas de pesquisa são Teoria Espectral de Grafos e Álgebra Computacional. 

Carlos Hoppen, Universidade Federal do Rio Grande do Sul, Rio Grande do Sul, RS

Carlos Hoppen é Professor Associado do Departamento de Matemática Pura e Aplicada na Universidade Federal do Rio Grande do Sul. Sua formação é de bacharel em Matemática, com ênfase em Matemática Aplicada e Computacional, pela Universidade Federal do Rio Grande do Sul (2002), tem mestrado em Matemática Aplicada pela Universidade Federal do Rio Grande do Sul (2004) e doutorado em Combinatória e Otimização pela Universidade de Waterloo (University of Waterloo, Canadá, 2008). Foi pós-doutorando do Departamento de Ciência da Computação da Universidade de São Paulo entre os anos de 2008 e 2010. Tem experiência na área de Matemática, com ênfase em Combinatória, bem como na área de Ciência da Computação, com ênfase em algoritmos algébricos e probabilísticos. Desde 2017, é Secretário Geral da Diretoria da Sociedade Brasileira de Matemática Aplicada e Computacional.

Pedro da Silva Peixoto, Universidade de São Paulo, São Paulo, SP

Professor Associado do Departamento de Matemática Aplicada da Universidade de São Paulo (ingresso em 2014). Livre Docência pela Universidade de São Paulo (USP) em 2019. Pós-doutorado na Universidade de Exeter, Reino Unido (2015). Doutor em Matemática Aplicada pela USP (2013) com estágio sanduíche na Universidade de Cambridge, Reino Unido (2012). Mestre em Matemática Aplicada (2009) e Especialista em Modelagem do Nível do Mar (Instituto Oceanográfico - 2007) pela USP. Graduado com honras Bacharel em Matemática Aplicada e Computacional pela USP em 2006. Atua principalmente na área de análise numérica, métodos numéricos para equações diferenciais, dinâmica dos fluidos computacional e modelagem atmosférica global, mas possui trabalhos também na área de modelagem matemática e análises estatísticas para biotecnologia. Bolsista Jovem Pesquisador FAPESP. Editor associado do Monthly Weather Review (2015-2020). 

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Published

2021-11-08 — Updated on 2022-06-27

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How to Cite

Sibemberg, L. S., Allem, L. E., Hoppen, C., & Peixoto, P. da S. (2022). Risk classification in complex networks: the case of COVID-19 in Rio Grande do Sul. Ciência E Natura, 43, e1. https://doi.org/10.5902/2179460X66864 (Original work published April 6, 2022)

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Vol. 43 (2021): Special Edition: X ERMAC RS

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Special Edition

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