Browsing by Author "Olaosebikan, A."

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    Modeling of Tuberculosis and Tuberculosis Co-infected with Human Immunodeficiency Virus Patents using some Parametric Survival Models
    (The Pacific Journal of Science and Technology, 2020) Olaosebikan, A.; Aderoju, S.A.; Balogun, O.S.
    In this study, the authors examined age and gender as some of the challenges of modeling infectious diseases using data comprising of patients with Tuberculosis (TB) and TB coinfected Human Immunodeficiency Virus (HIV) as a case study. The TB and TB co-infected with HIV are some of the common health problems in the world. Time-to-event outcomes are common data types in medical research. The data examined time until a patient is cured of the disease having some patients right-censored. With the nature of the data, the appropriate analysis is the survival analysis method. The study aims at fitting appropriate models to the TB and TB/HIV coinfection data examining age and gender as factors influencing the survival of the disease. Hence, Kaplan-Meier estimation, Cox PH and some parametric models were adopted in the study. The result shows that among the parametric models, generalized gamma fit TB data best and there is a significant difference in the survival rate of male and female while Cox model fit TB coinfected with HIV best and there is a significant difference in the male and female patient. The median survival times are 17 and 18 months for TB patients and TB/HIV co-infected patients respectively.
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    On Poisson-Samade Distribution: Its Applications in Modelling Count Data
    (Earthline Journal of Mathematical Sciences, 2023-05-07) Aderoju, S.A.; Adeniyi, I.; Olaifa, J.B.; Olaosebikan, A.
    A new mixed Poisson model is proposed as a better alternative for modelling count data in the presence of overdispersion and/or heavy-tail. The mathematical properties of the model were derived. The maximum likelihood estimation method is employed to estimate the model’s parameters and its applications to the three real data sets discussed. The model is used to model sets of frequencies that have been used in different literature on the subject. The results of the new model were compared with Poisson, Negative Binomial and Generalized Poisson-Sujatha distributions (POD, NBD and GPSD, respectively). The parameter estimates expected frequencies and the goodness-of-fit statistics under each model are computed using R software. The results show that the proposed PSD fits better than POD, NBD and GPSD for all the data sets considered. Hence, PSD is a better alternative provided to model count data exhibiting overdispersion property.