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

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.