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Modeling Time-To-Cure From Covid-19: A Comparison Of Various Parametric Frailty Models: A Case Study At Jimma University and Shenen Gibe Covid-19 Care Center, Jimma Zone, South West Ethiopia

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dc.contributor.author Meseret Mesfin
dc.contributor.author Geremew Muleta
dc.contributor.author Abiy Disasa
dc.date.accessioned 2022-02-01T08:06:05Z
dc.date.available 2022-02-01T08:06:05Z
dc.date.issued 2021-07-06
dc.identifier.uri https://repository.ju.edu.et//handle/123456789/6126
dc.description.abstract Novel Corona viruses are a viral family that causes Severe Acute Metabolic Syndrome. The Virus was strictly increasing throughout in the World. From the total cases(91.5%) were cured in the world.In Africa(60%) were cured and In Ethiopia (98.61%) were cured from march 2020 to March 2021. Many scholars conducts prognostic factors of Covid-19 by using coxph,non parametric, and logistic regration model.But loglogistic regration does not account censoring observation and Coxph model and non parametric models were used in the independent and identically distributed covariates.For those model hetrogeneity does not considered. The objective of this thesis was de velop various parametric frailty model to detect random effect on time–to-cure of covid-19 in the two treatment care center. Data were collected in Jimma university and Shenen Gibe covid-19 Center. Appropriate model that describes the Covid-19 data were Gamma and Inverse Gaussian frailty with exponential, log-logistic, log-normal, and Weibull baseline function were compared. Based on Akaike information criterion criteria all models were compared.Data were analyzed by using R version 4.0.5 software.298 covid-19 patients 246(82.65%)were cured with the median cur ing time of 19 days.The log-logstic model with Gamma frailty distribution has the smallest Akaike information criterion(1609.625) value compared with the others. Clustering effect is significant on the modeling time to cure from covid-19 with in test of unobserved hetr0geneity in all models. From this finding, age group, severity ,co morbidity , diabetics, lung-cancer, and oxygen are prog nostic (significant) factors for time to cure of covid-19. From this thesis almost of patient were cured in two treatment care center around Jimma zone.log-logistic Gamma frailty model was best fit of Covid-19 dataset.There is heterogeneity between the two treatment center for time to cure of covid-19. Researcher recommend that there is some limitation of parametric frailty model.For further study who have interest to compare parametric model the simulation is appropriate. en_US
dc.language.iso en_US en_US
dc.subject AIC en_US
dc.subject Frailty en_US
dc.subject Heterogeneity en_US
dc.subject Parametric Model en_US
dc.subject Time to cure en_US
dc.title Modeling Time-To-Cure From Covid-19: A Comparison Of Various Parametric Frailty Models: A Case Study At Jimma University and Shenen Gibe Covid-19 Care Center, Jimma Zone, South West Ethiopia en_US
dc.type Thesis en_US


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