IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i21p3386-d1509520.html
   My bibliography  Save this article

A Bimodal Exponential Regression Model for Analyzing Dengue Fever Case Rates in the Federal District of Brazil

Author

Listed:
  • Nicollas S. S. da Costa

    (Coordenadoria Estratégica de Dados de Pessoal, Decanato de Gestão de Pessoas, Universidade de Brasília, Campus Darcy Ribeiro, Brasília 70910-900, Brazil)

  • Maria do Carmo Soares de Lima

    (Departamento de Estatística, Centro de Ciências Exatas e da Natureza, Universidade Federal de Pernambuco, Cidade Universitária, Recife 52070-040, Brazil)

  • Gauss Moutinho Cordeiro

    (Departamento de Estatística, Centro de Ciências Exatas e da Natureza, Universidade Federal de Pernambuco, Cidade Universitária, Recife 52070-040, Brazil)

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.

Suggested Citation

  • Nicollas S. S. da Costa & Maria do Carmo Soares de Lima & Gauss Moutinho Cordeiro, 2024. "A Bimodal Exponential Regression Model for Analyzing Dengue Fever Case Rates in the Federal District of Brazil," Mathematics, MDPI, vol. 12(21), pages 1-20, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3386-:d:1509520
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/21/3386/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/21/3386/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nadarajah, Saralees & Kotz, Samuel, 2006. "The beta exponential distribution," Reliability Engineering and System Safety, Elsevier, vol. 91(6), pages 689-697.
    2. Ayman Alzaatreh & Carl Lee & Felix Famoye, 2013. "A new method for generating families of continuous distributions," METRON, Springer;Sapienza Università di Roma, vol. 71(1), pages 63-79, June.
    3. Lin, Hang & Zhang, Zhengjun, 2022. "Extreme co-movements between infectious disease events and crude oil futures prices: From extreme value analysis perspective," Energy Economics, Elsevier, vol. 110(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mahmoud Aldeni & Carl Lee & Felix Famoye, 2017. "Families of distributions arising from the quantile of generalized lambda distribution," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-18, December.
    2. Abdulhakim A. Al-Babtain & Mohammed K. Shakhatreh & Mazen Nassar & Ahmed Z. Afify, 2020. "A New Modified Kies Family: Properties, Estimation Under Complete and Type-II Censored Samples, and Engineering Applications," Mathematics, MDPI, vol. 8(8), pages 1-24, August.
    3. Emrah Altun & Mustafa Ç. Korkmaz & Mahmoud El-Morshedy & Mohamed S. Eliwa, 2021. "A New Flexible Family of Continuous Distributions: The Additive Odd-G Family," Mathematics, MDPI, vol. 9(16), pages 1-17, August.
    4. Morad Alizadeh & Ahmed Z. Afify & M. S. Eliwa & Sajid Ali, 2020. "The odd log-logistic Lindley-G family of distributions: properties, Bayesian and non-Bayesian estimation with applications," Computational Statistics, Springer, vol. 35(1), pages 281-308, March.
    5. Alzaatreh, Ayman & Famoye, Felix & Lee, Carl, 2014. "The gamma-normal distribution: Properties and applications," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 67-80.
    6. Boikanyo Makubate & Fastel Chipepa & Broderick Oluyede & Peter O. Peter, 2021. "The Marshall-Olkin Half Logistic-G Family of Distributions With Applications," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(2), pages 120-120, March.
    7. Ramadan A. ZeinEldin & Christophe Chesneau & Farrukh Jamal & Mohammed Elgarhy, 2019. "Statistical Properties and Different Methods of Estimation for Type I Half Logistic Inverted Kumaraswamy Distribution," Mathematics, MDPI, vol. 7(10), pages 1-24, October.
    8. A. A. Ogunde & S. T. Fayose & B. Ajayi & D. O. Omosigho, 2020. "Properties, Inference and Applications of Alpha Power Extended Inverted Weibull Distribution," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 9(6), pages 1-90, November.
    9. Ali Genç, 2013. "A skew extension of the slash distribution via beta-normal distribution," Statistical Papers, Springer, vol. 54(2), pages 427-442, May.
    10. Abdus Saboor & Muhammad Nauman Khan & Gauss M. Cordeiro & Marcelino A. R. Pascoa & Juliano Bortolini & Shahid Mubeen, 2019. "Modified beta modified-Weibull distribution," Computational Statistics, Springer, vol. 34(1), pages 173-199, March.
    11. Alexander, Carol & Cordeiro, Gauss M. & Ortega, Edwin M.M. & Sarabia, José María, 2012. "Generalized beta-generated distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1880-1897.
    12. Gauss Cordeiro & Elizabeth Hashimoto & Edwin Ortega & Marcelino Pascoa, 2012. "The McDonald extended distribution: properties and applications," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(3), pages 409-433, July.
    13. Sajid Hussain & Mahmood Ul Hassan & Muhammad Sajid Rashid & Rashid Ahmed, 2023. "The Exponentiated Power Alpha Index Generalized Family of Distributions: Properties and Applications," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    14. Abdulhakim A. Al-Babtain & Ibrahim Elbatal & Christophe Chesneau & Farrukh Jamal, 2020. "Box-Cox Gamma-G Family of Distributions: Theory and Applications," Mathematics, MDPI, vol. 8(10), pages 1-24, October.
    15. Gauss M. Cordeiro & Giovana O. Silva & Edwin M. M. Ortega, 2016. "An extended-G geometric family," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-16, December.
    16. Yaoting Yang & Weizhong Tian & Tingting Tong, 2021. "Generalized Mixtures of Exponential Distribution and Associated Inference," Mathematics, MDPI, vol. 9(12), pages 1-22, June.
    17. Showkat Ahmad Lone & Tabassum Naz Sindhu & Marwa K. H. Hassan & Tahani A. Abushal & Sadia Anwar & Anum Shafiq, 2023. "Theoretical Structure and Applications of a Newly Enhanced Gumbel Type II Model," Mathematics, MDPI, vol. 11(8), pages 1-18, April.
    18. Zakeia A. Al-saiary & Rana A. Bakoban & Areej A. Al-zahrani, 2019. "Characterizations of the Beta Kumaraswamy Exponential Distribution," Mathematics, MDPI, vol. 8(1), pages 1-12, December.
    19. Jiong Liu & R. A. Serota, 2023. "Rethinking Generalized Beta family of distributions," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(2), pages 1-14, February.
    20. Amal S. Hassan & Said G. Nassr, 2019. "Power Lindley-G Family of Distributions," Annals of Data Science, Springer, vol. 6(2), pages 189-210, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3386-:d:1509520. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.