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Identifiability analysis of an HIV-Ebola co-infection using the mathematical model and the MLE method.

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Title: Identifiability analysis of an HIV-Ebola co-infection using the mathematical model and the MLE method.
Authors: Said, Muhammad1 (AUTHOR) msaidakhunzada@gmail.com, Roh, Yunil1 (AUTHOR) dbsdlf2576@gmail.com, Jung, Il Hyo1,2,3 (AUTHOR) ilhjung@pusan.ac.kr
Source: Alexandria Engineering Journal. Jun2025, Vol. 125, p245-255. 11p.
Subjects: Ebola virus disease, Parameter estimation, Maximum likelihood statistics, Fisher information, Infectious disease transmission
Abstract: In this paper, we develop a mathematical model to analyze the identifiability of HIV-Ebola co-infection using the maximum likelihood method. By analyzing real-world data, this research assesses the accuracy of parameter estimation in the epidemic model. We consider various epidemiological factors, including disease transmission, progression, mortality, and recovery rates, to evaluate the model's identifiability. The maximum likelihood estimation (MLE) method is applied to estimate the parameters, utilizing the Fisher Information Matrix for structural identifiability and profile likelihood analysis for practical identifiability to assess the reliability of the estimated parameters. The results demonstrate that Ebola has a high transmission rate and rapid disease progression, emphasizing the urgent need for prompt and vigorous public health interventions during outbreaks. However, HIV's gradual spread and chronic nature highlight the importance of ongoing work in preventive and treatment techniques. The nature of co-infection shows synergistic effects, in which the presence of one virus increases susceptibility to the other, thereby aggravating health consequences. The results will help improve knowledge of the co-infection patterns among HIV and EVD, lead future research, and assist in evidence-based decision-making for public health interventions aimed at co-infected individuals. [ABSTRACT FROM AUTHOR]
Copyright of Alexandria Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Label: Title
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  Data: Identifiability analysis of an HIV-Ebola co-infection using the mathematical model and the MLE method.
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  Data: <searchLink fieldCode="AR" term="%22Said%2C+Muhammad%22">Said, Muhammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> msaidakhunzada@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Roh%2C+Yunil%22">Roh, Yunil</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dbsdlf2576@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Jung%2C+Il+Hyo%22">Jung, Il Hyo</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> ilhjung@pusan.ac.kr</i>
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  Data: <searchLink fieldCode="JN" term="%22Alexandria+Engineering+Journal%22">Alexandria Engineering Journal</searchLink>. Jun2025, Vol. 125, p245-255. 11p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Ebola+virus+disease%22">Ebola virus disease</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+statistics%22">Maximum likelihood statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Fisher+information%22">Fisher information</searchLink><br /><searchLink fieldCode="DE" term="%22Infectious+disease+transmission%22">Infectious disease transmission</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, we develop a mathematical model to analyze the identifiability of HIV-Ebola co-infection using the maximum likelihood method. By analyzing real-world data, this research assesses the accuracy of parameter estimation in the epidemic model. We consider various epidemiological factors, including disease transmission, progression, mortality, and recovery rates, to evaluate the model's identifiability. The maximum likelihood estimation (MLE) method is applied to estimate the parameters, utilizing the Fisher Information Matrix for structural identifiability and profile likelihood analysis for practical identifiability to assess the reliability of the estimated parameters. The results demonstrate that Ebola has a high transmission rate and rapid disease progression, emphasizing the urgent need for prompt and vigorous public health interventions during outbreaks. However, HIV's gradual spread and chronic nature highlight the importance of ongoing work in preventive and treatment techniques. The nature of co-infection shows synergistic effects, in which the presence of one virus increases susceptibility to the other, thereby aggravating health consequences. The results will help improve knowledge of the co-infection patterns among HIV and EVD, lead future research, and assist in evidence-based decision-making for public health interventions aimed at co-infected individuals. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Alexandria Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.aej.2025.03.135
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 11
        StartPage: 245
    Subjects:
      – SubjectFull: Ebola virus disease
        Type: general
      – SubjectFull: Parameter estimation
        Type: general
      – SubjectFull: Maximum likelihood statistics
        Type: general
      – SubjectFull: Fisher information
        Type: general
      – SubjectFull: Infectious disease transmission
        Type: general
    Titles:
      – TitleFull: Identifiability analysis of an HIV-Ebola co-infection using the mathematical model and the MLE method.
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            NameFull: Said, Muhammad
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            NameFull: Roh, Yunil
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            NameFull: Jung, Il Hyo
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            – D: 20
              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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              Value: 125
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