Bayesian Survival Analysis for Recovery Time of Corona Virus (Covid-19) Patients And Associated Factors: A Case Study At Debre Berhan Referal Hospital North Shewa, Ethiopia

Asfere Lakew

dc.contributor.author	Asfere Lakew
dc.date.accessioned	2022-08-22T10:38:24Z
dc.date.available	2022-08-22T10:38:24Z
dc.date.issued	2022-08-16
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/14034
dc.description.abstract	Corona virus is one of the major pathogens that primarily target the human respiratory system, which started in Wuhan, China in December 2019, has emerged as a global health and economic security threat with an overwhelming growing incidence worldwide. When the World Health Organization (WHO) declared the disease a global public health emergency, different stakeholders stepped up efforts to convince the world that the disease is a serious problem that needs strong containment measures. The main objective of the study is to identify the determinant risk factors for the recovery of corona virus(covid-19) patients. A study population of 389 corona virus(covid-19) patients who start their treatment in Debere Berhan Referral Hospital between August 2020 -November 2021 was included in the study. Descriptive statistics and Kaplan-Meier survival curves were used to estimate and compare the recovery time of corona virus(covid-19) patients among different categorical characteristics of the patients. We used classical and Bayesian accelerated failure time model to analyze the data. The result showed that of a total of 389 corona virus (covid-19) patients considered 87.7% recovered from covid-19. From the result age (TR=1.25, p-value=0.033), high fever (TR=1.309, p-value=0.07), Occupational status (TR=0.678, P-value=0.001), shortness of breath (TR=3.109, p-value=0.003), comorbidity (TR=1.117, p-value=0.037), severe headache (TR=1.146, p value=0.038) and place of residence (TR=0.259, p-value=0.021) were the significant factors for the corona virus(covid-19) patients accelerated failure time model. The accelerated failure time model showed that the major factors that affect the recovery time of corona-virus (covid-19) and see the associations factors among patients. The Weibull accelerated failure time model better fits the recovery time of corona virus(covid-19) than the exponential AFT model, log-logistic AFT model and log-normal AFT model. From the MC error accuracy, the model in Bayesian approach better than classical approach. Patient’s comorbidities have a major impact on CVID-19; So, health profession should close follow up is required for client admitted with comorbidity and create great awareness about the risk factors the corona virus (covid-19). Key words: corona virus(covid-19), proportional hazard, accelerated failure time, Bayesian survival analysis, winBUGS	en_US
dc.language.iso	en_US	en_US
dc.subject	Statistics	en_US
dc.title	Bayesian Survival Analysis for Recovery Time of Corona Virus (Covid-19) Patients And Associated Factors: A Case Study At Debre Berhan Referal Hospital North Shewa, Ethiopia	en_US
dc.type	Thesis	en_US