Bayesian and Non-Bayesian Inference for Unit-Exponentiated Half-Logistic Distribution with Data Analysis

Unit distributions are typically used in probability theory and statistics to illustrate useful quantities with values between zero and one. In this paper, we investigated an appropriate transformation to propose the unit-exponentiated half-logistic distribution (UEHLD), which is also beneficial for modelling data on the unit interval. This distribution's mathematical features are supplied, including moments, probability-weighted moments, incomplete moments, various entropy measures, and stress–strength reliability. Using well-known estimation techniques such as the maximum likelihood, maximum product of spacing, and Bayesian inference, the estimators of the parameters relevant to the proposed distribution were determined. A comprehensive simulation analysis is provided to examine the performance of parameter estimation approaches on finite samples. The proposed distribution was realistically applied to data on economic growth and data on the tensile strength of polyester fibers to provide an explanation. Furthermore, the analysis of COVID-19 data from Britain as a medical statistical dataset is provided. The experimental results demonstrate that the suggested UEHLD yields a better comparison with some new unit distributions, as well as other unbounded distributions.

Type I Half Logistic Burr X-G Family: Properties, Bayesian, and Non-Bayesian Estimation under Censored Samples and Applications to COVID-19 Data

In this paper, we present a new family of continuous distributions known as the type I half logistic Burr X-G. The proposed family's essential mathematical properties, such as quantile function (QuFu), moments (Mo), incomplete moments (InMo), mean deviation (MeD), Lorenz (Lo) and Bonferroni (Bo) curves, and entropy (En), are provided. Special models of the family are presented, including type I half logistic Burr X-Lomax, type I half logistic Burr X-Rayleigh, and type I half logistic Burr X-exponential. The maximum likelihood (MLL) and Bayesian techniques are utilized to produce parameter estimators for the recommended family using type II censored data. Monte Carlo simulation is used to evaluate the accuracy of estimates for one of the family's special models. The COVID-19 real datasets from Italy, Canada, and Belgium are analysed to demonstrate the significance and flexibility of some new distributions from the family. [ABSTRACT FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Risk factors for hospital admission among COVID-19 patients with diabetes. A study from Saudi Arabia.

OBJECTIVES: To elucidate the risk factors for hospital admission among COVID-19 patients with type 2 diabetes mellitus (T2DM). METHODS: This retrospective study was conducted at the Prince Sultan Military Medical City, Riyadh, Saudi Arabia between May 2020 and July 2020. Out of 7,260 COVID-19 patients, 920 were identified as T2DM. After the exclusion process, 806 patients with T2DM were included in this analysis. Patients' data were extracted from electronic medical records. A logistic regression model was performed to estimate the risk factors of hospital admission. Results: Of the total of 806 COVID-19 patients with T2DM, 48% were admitted in the hospital, 52% were placed under home isolation. Older age between 70-79 years (OR [odd ratio] 2.56; p=0.017), ≥80 years (OR 6.48; p=0.001) were significantly more likely to be hospitalized compared to less than 40 years. Similarly, patients with higher HbA1c level of ≥9% compared to less than 7%; (OR 1.58; p=0.047); patients with comorbidities such as, hypertension (OR 1.43; p=0.048), cardiovascular disease (OR 1.56; p=0.033), cerebrovascular disease (OR 2.38; p=0.016), chronic pulmonary disease (OR 1.51; p=0.018), malignancy (OR 2.45; p=0.025), chronic kidney disease (CKD) IIIa, IIIb, IV (OR 2.37; p=0.008), CKD V (OR 5.07; p=0.007) were significantly more likely to be hospitalized. Likewise, insulin-treated (OR 1.46; p=0.03) were more likely to require hospital admission compared to non-insulin treated patients. CONCLUSION: Among COVID-19 patients with diabetes, higher age, high HbA1c level, and presence of other comorbidities were found to be significant risk factors for the hospital admission.

Nanobiotechnology Model Arising from Coronavirus Interacting with the AQP Channels Along the Respiratory Regions and Estimating the Infectivity Rate of the COVID19 Outbreak Based on Temperature and Direct Contact Rate

Coronavirus (COVID19) is an infectious disease that attacks the human body, particularly within the respiratory regions, e.g., nasal and lung tissues. COVID19 infection is classified into three different stages: mild, moderate, and severe. Recovery from the first two stages can mostly be achieved without special treatment, but these stages can lead to death for older people and those who have underlying medical conditions, such as diabetes, chronic-respiratory disease, HIV, and cardiovascular disease. COVID19 prefers to attack the tissues and membranes of the respiratory system, especially those found in the nose, throat, and on the external surface of the lungs. Aquaporins (AQPs) are a large family of integral biomembranes that facilitate transport of water and small biomolecules between cells. The current work develops two scientific sub-models, i.e., biological and statistical. The biological model is aimed at investigating the realistic mechanism of bio-interaction between the two types of COV and different AQP protein channels. This model is obtained mathematically by evaluating the magnitude of the potential energy arising from SARSCOV and COVID19 penetrating the cavity of the AQP protein channels located on the external surface of human cells. We use an exponential function to estimate the transmission rate of COVID19 with respect to time in different territories. Additionally, we observe that temperature and direct contact play major roles in determining the number of infected cases, and consider relative humidity as a secondary factor. Our results show that AQP1, AQP3, and AQP4 are the most favorable tissues for COVID19 spread because their pH exceeds 6.5. A mathematical model is developed that describes the behavior of the COVID19 outbreak in terms of temperature (mu) and direct contact (alpha) rate.