| Campo DC | Valor | Idioma |
|---|
| dc.contributor.author | Costa, Nicollas Stefan Soares da | - |
| dc.contributor.author | Lima, Maria do Carmo Soares de | - |
| dc.contributor.author | Cordeiro, Gauss Moutinho | - |
| dc.date.accessioned | 2026-11-05T15:06:30Z | - |
| dc.date.available | 2026-11-05T15:06:30Z | - |
| dc.date.issued | 2024-10-29 | - |
| dc.identifier.citation | COSTA, Nicollas Stefan Soares da; Lima, Maria do Carmo Soares de; Cordeiro, G.M. A bimodal exponential regression model for analyzing dengue fever case rates in the Federal District of Brazil. Mathematics, [S. l.], v. 12, n. 21, 3386, 2024. DOI: https://doi.org/10.3390/math12213386. Disponível em: https://www.mdpi.com/2227-7390/12/21/3386. Acesso em: 5 nov. 2026. | pt_BR |
| dc.identifier.uri | http://repositorio.unb.br/handle/10482/57377 | - |
| dc.language.iso | eng | pt_BR |
| dc.publisher | MDPI | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | A bimodal exponential regression model for analyzing dengue fever case rates in the Federal District of Brazil | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.subject.keyword | Dengue | pt_BR |
| dc.subject.keyword | Epidemiologia | pt_BR |
| dc.subject.keyword | Simulação de Monte Carlo | pt_BR |
| dc.subject.keyword | Modelo de regressão | pt_BR |
| dc.rights.license | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | pt_BR |
| dc.identifier.doi | https://doi.org/10.3390/math12213386 | pt_BR |
| dc.description.abstract1 | Dengue fever remains a significant epidemiological challenge globally, particularly in Brazil,
where recurring outbreaks strain healthcare systems. Traditional statistical models often struggle to
accurately capture the complexities of dengue case distributions, especially when data exhibit bimodal
patterns. This study introduces a novel bimodal regression model based on the log-generalized
odd log-logistic exponential distribution, offering enhanced flexibility and precision for analyzing
epidemiological data. By effectively addressing multimodal distributions, the proposed model
overcomes the limitations of unimodal models, making it well suited for public health applications.
Through regression analysis of dengue case data from the Federal District of Brazil during the
epidemiological weeks of 2022, the model demonstrates its capacity to improve the fit of the disease
rate. The model’s parameters are estimated using maximum likelihood estimation, and Monte
Carlo simulations validate their accuracy. Additionally, local influence measures and residual
analysis ensure the proposed model’s goodness-of-fit. While this innovative regression model offers
substantial advantages, its effectiveness depends on the availability of high-quality data, and further
validation is necessary to confirm its applicability across diverse diseases and regions with varying
epidemiological characteristics. | pt_BR |
| dc.contributor.affiliation | Universidade de Brasília, Decanato de Gestão de Pessoas, Coordenadoria Estratégica de Dados de Pessoal | pt_BR |
| dc.contributor.affiliation | Universidade Federal de Pernambuco, Centro de Ciências Exatas e da Natureza, Departamento de Estatística | pt_BR |
| dc.contributor.affiliation | Universidade Federal de Pernambuco, Centro de Ciências Exatas e da Natureza, Departamento de Estatística | pt_BR |
| dc.description.unidade | Instituto de Ciências Exatas (IE) | pt_BR |
| dc.description.unidade | Departamento de Matemática (IE MAT) | pt_BR |
| Aparece nas coleções: | Artigos publicados em periódicos e afins
|
