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- W2991621398 abstract "In this study, we examined the challenges of modeling infectious diseases using tuberculosis (TB) as a case study. The tuberculosis and tuberculosis co-infected with Human Immuno Virus (HIV) is one of the common health problems in the world. Time-to-event outcomes are common data type 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 survival analysis method. The study aims at fitting appropriate models to the TB and TB/HIV co-infection data examining age and gender as factors influencing the cure rate 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 no significant difference in the survival rate of male and female while gamma fit TB co-infected with HIV best and there is a significant difference in the male and female patient. However, Cox PH model (having smaller AIC) performs better than all the parametric models considered (for both data) in this study though with the same conclusion." @default.
- W2991621398 created "2019-12-05" @default.
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- W2991621398 date "2019-01-01" @default.
- W2991621398 modified "2023-09-27" @default.
- W2991621398 title "Modeling Cure Rate of Infectious Disease with or Without Co-Infection: An Application to Tuberculosis / Human Immuno Virus" @default.
- W2991621398 doi "https://doi.org/10.28933/ajsre-2019-06-1406" @default.
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