Modeling and analysis of recovery time for the COVID-19 patients: a Bayesian approach

Ateeq, Kahkashan; Altaf, Saima; Aslam, Muhammad

doi:10.1080/25765299.2022.2148439

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…Dorugade [23] introduced new ridge parameters for ridge regression. The Bayesian approach was also used by Ateeq et al [24] to find the recovery time for the patients of a contagious disease. The ordinary least square (OLS) regression is an incredible asset when the variables of interest are continuous; however, in the case of dichotomous variables, OLS is not valid.…”

Section: Logistic Regressionmentioning

confidence: 99%

Estimation of Hidden Logits Using Several Randomized Response Techniques

Halim,

Arshad,

Alomair

et al. 2023

Symmetry

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In survey sampling, we aspire to obtain sound and consistent responses, which are not achieved while dealing with sensitive issues. Frequently, respondents give elusive responses to sensitive questions, so we employ randomized response techniques that facilitate finding an appropriate proportion of socially sensitive characteristics. In the present study, we proposed a hidden logit estimation method using Huang, Warner, and Mangat’s randomized response techniques. This study depicts that the estimates become closer to the standard logits as the values of p increase. We found that the hidden logit estimates obtained by the Huang randomized response technique were nearer to the parametric values, in contrast to the other existing techniques, and we demonstrate an increase in accuracy as well. The simulation-based AIC and SIC values are used to assess model performance. We found that the Huang model is the best model for the proposed hidden logit method. This paper contributes towards the application of logistic models in the case of sensitive or socially stigmatized issues.

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Section: Logistic Regressionmentioning

confidence: 99%

Estimation of Hidden Logits Using Several Randomized Response Techniques

Halim,

Arshad,

Alomair

et al. 2023

Symmetry

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“…Numerous loss functions are available in literature such as SELF and WSELF, precautionary loss function (PLF), modified squared loss function(MSELF) and K-loss function(KLF) etc. have been applied in deriving Bayes estimators (Dey et al,2017, Ghaffar andAteeq, 2022). In this study, to derive Bayes estimators and their respective posterior risks, we have applied SELF and WSELF, with SELF leading to the posterior mean estimate, and WSELF using a weighted approach.…”

Section: Bayes Estimators and Posterior Riskmentioning

confidence: 99%

“…Sultan & Ahmad (2015) investigated the estimation of the shape parameter of Kumaraswamy Distribution using normal, Lindley's Approximation, Tierney and Kadane (T-K) approximations under informative and non-informative priors. Similarly, Ateeq, Altaf, andAslam (2022, 2023).…”

Section: Introductionmentioning

confidence: 99%

Modeling the Prevalence of Anemia among the Children in Pakistan: Classical and Bayesian Estimation Approach

Tahira Bano Qasim,

Dua Israr,

Maria Ghaffar

2023

JSCIR

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Anemia remains a major public health concern in all nations, particularly affecting children worldwide. This disorder, which is defined as low hemoglobin or red blood cells in the blood, has multiple causes, the most common one being iron deficiency. The prevalence of anemia among children in Pakistan have been modeled by conducting a thorough statistical analysis emphasizing on inferential statistics and estimation techniques. The study explores the classical and non-classical essential categories of estimating methodologies. The work specifically looks into Bayesian estimation methods under squared error loss function (SELF) and weighted square error loss function (WSELF) for a new generalization of the Exponential Distribution introduced by Nadarajah Haghighi(2017) named as Nadarajah Haghighi Distribution (NHD). The Bayesian estimation of the parameters of NH distribution is run under uniform prior (Non-Informative) and exponential prior (Informative prior). The Lindley approximation method is utilized in this study to solve Bayesian integral. This methodology's practical application focuses on determining Pakistan's under-five-year-old population's anemia prevalence. Both maximum likelihood and Bayesian estimators by means of Monte Carlo simulations and practical application are used to thoroughly compared. Notably, it is empirically reveal that Bayesian estimation technique performs better than the classical estimation technique both in case of simulation and in modeling the prevalence of anemia among Pakistani children.

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“…Hospital length of stay (LOS) is a clinical metric that measures the length of time elapsed between a patient's hospital admittance and discharge. The LOS of these patients depends on several factors, including the duration from initial exposure until the appearance of the first symptoms of the disease, the duration from appearance of initial symptoms until the time of admission to the hospital, and specific background factors of each context [18][19][20]. The COVID-19 pandemic has led to a surge in hospital admissions worldwide.…”

Section: Introductionmentioning

confidence: 99%

Survival Analysis of Hospital Length of Stay of COVID-19 Patients in Ilam Province, Iran: A Retrospective Cross-Sectional Study

Momeni,

Raadabadi,

Sadeghifar

et al. 2023

JCM

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Knowledge of the length of hospitalization of patients infected with coronavirus disease 2019 (COVID-19), its characteristics, and its related factors creates a better understanding of the impact of medical interventions and hospital capacities. Iran is one of the countries with the most deaths in the world (146,321 total deaths; 5 September 2023) and ranks first among the countries of the Middle East and the EMRO. Analysis of confirmed COVID-19 patients’ hospital length of stay in Ilam Province can be informative for decision making in other provinces of Iran. This study was conducted to analyze the survival of COVID-19 patients and the factors associated with COVID-19 deaths in the hospitals of Ilam Province. This is a retrospective cross-sectional study. Data from confirmed COVID-19 cases in Ilam Province were obtained from the SIB system in 2019. The sample size was 774 COVID-19-positive patients from Ilam Province. Measuring survival and risk probabilities in one-week intervals was performed using Cox regression. Most patients were male (55.4%) and 55.3% were over 45 years old. Of the 774 patients, 87 (11.2%) died during the study period. The mean hospital length of stay was 5.14 days. The median survival time with a 95% confidence interval was four days. The probability of survival of patients was 80%, 70%, and 38% for 10, 20, and 30 days of hospital stay, respectively. There was a significant relationship between the survival time of patients with age, history of chronic lung diseases, history of diabetes, history of heart diseases, and hospitalization in ICU (p < 0.05). The risk of dying due to COVID-19 disease was higher among men, older age groups, and patients with a history of chronic lung diseases, diabetes, and heart disease. According to the results, taking preventive measures for elderly patients and those with underlying conditions to prevent the infection of COVID-19 patients is of potential interest. Efficiency in the management of hospital beds should also be considered.

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Modeling and analysis of recovery time for the COVID-19 patients: a Bayesian approach

Cited by 5 publications

References 38 publications

Estimation of Hidden Logits Using Several Randomized Response Techniques

Estimation of Hidden Logits Using Several Randomized Response Techniques

Modeling the Prevalence of Anemia among the Children in Pakistan: Classical and Bayesian Estimation Approach

Survival Analysis of Hospital Length of Stay of COVID-19 Patients in Ilam Province, Iran: A Retrospective Cross-Sectional Study

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