Título:	A bimodal exponential regression model for analyzing dengue fever case rates in the Federal District of Brazil
Autor(es):	Costa, Nicollas Stefan Soares da Lima, Maria do Carmo Soares de Cordeiro, Gauss Moutinho
Afiliação do autor:	Universidade de Brasília, Decanato de Gestão de Pessoas, Coordenadoria Estratégica de Dados de Pessoal Universidade Federal de Pernambuco, Centro de Ciências Exatas e da Natureza, Departamento de Estatística Universidade Federal de Pernambuco, Centro de Ciências Exatas e da Natureza, Departamento de Estatística
Assunto:	Dengue Epidemiologia Simulação de Monte Carlo Modelo de regressão
Data de publicação:	29-Out-2024
Editora:	MDPI
Referência:	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.
Abstract:	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.
Unidade Acadêmica:	Instituto de Ciências Exatas (IE) Departamento de Matemática (IE MAT)
Licença:	© 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/).
DOI:	https://doi.org/10.3390/math12213386
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