The Markov Bernoulli Lomax with Applications Censored and COVID-19 Drought Mortality Rate Data

In this article, we present a Markov Bernoulli Lomax (MB-L) model, which is obtained by a countable mixture of Markov Bernoulli and Lomax distributions, with decreasing and unimodal hazard rate function (HRF). The new model contains Marshall- Olkin Lomax and Lomax distributions as a special case. The mathematical properties, as behavior of probability density function (PDF), HRF, rth moments, moment generating function (MGF) and minimum (maximum) Markov-Bernoulli Geometric (MBG) stable are studied. Moreover, the estimates of the model parameters by maximum likelihood are obtained. The maximum likelihood estimation (MLE), bias and mean squared error (MSE) of MB-L parameters are inspected by simulation study. Finally, a MB-L distribution was fitted to the randomly censored and COVID-19 (complete) data. © 2023 by the authors.

Inverse Unit Teissier Distribution: Theory and Practical Examples

In this paper, we emphasize a new one-parameter distribution with support as (Formula presented.). It is constructed from the inverse method applied to an understudied one-parameter unit distribution, the unit Teissier distribution. Some properties are investigated, such as the mode, quantiles, stochastic dominance, heavy-tailed nature, moments, etc. Among the strengths of the distribution are the following: (i) the closed-form expressions and flexibility of the main functions, and in particular, the probability density function is unimodal and the hazard rate function is increasing or unimodal;(ii) the manageability of the moments;and, more importantly, (iii) it provides a real alternative to the famous Pareto distribution, also with support as (Formula presented.). Indeed, the proposed distribution has different functionalities but also benefits from the heavy-right-tailed nature, which is demanded in many applied fields (finance, the actuarial field, quality control, medicine, etc.). Furthermore, it can be used quite efficiently in a statistical setting. To support this claim, the maximum likelihood, Anderson–Darling, right-tailed Anderson–Darling, left-tailed Anderson–Darling, Cramér–Von Mises, least squares, weighted least-squares, maximum product of spacing, minimum spacing absolute distance, and minimum spacing absolute-log distance estimation methods are examined to estimate the unknown unique parameter. A Monte Carlo simulation is used to compare the performance of the obtained estimates. Additionally, the Bayesian estimation method using an informative gamma prior distribution under the squared error loss function is discussed. Data on the COVID mortality rate and the timing of pain relief after receiving an analgesic are considered to illustrate the applicability of the proposed distribution. Favorable results are highlighted, supporting the importance of the findings. © 2023 by the authors.

Exploring the Dynamics of COVID-19 with a Novel Family of Models

Much effort has recently been expended in developing efficient models that can depict the true picture for COVID-19 mortality data and help scientists choose the best-fit models. As a result, this research intends to provide a new G family for both theoretical and practical scientists that solves the concerns typically encountered in both normal and non-normal random events. The new-G distribution family is able to generate efficient continuous univariate and skewed models that may outperform the baseline model. The analytic properties of the new-G family and its sub-model are investigated and described, as well as a theoretical framework. The parameters were estimated using a classical approach along with an extensive simulation study to assess the behaviour of the parameters. The efficiency of the new-G family is discussed using one of its sub-models on COVID-19 mortality data sets. © 2023 by the authors.

General two-parameter distribution: Statistical properties, estimation, and application on COVID-19.

In this paper, we introduced a novel general two-parameter statistical distribution which can be presented as a mix of both exponential and gamma distributions. Some statistical properties of the general model were derived mathematically. Many estimation methods studied the estimation of the proposed model parameters. A new statistical model was presented as a particular case of the general two-parameter model, which is used to study the performance of the different estimation methods with the randomly generated data sets. Finally, the COVID-19 data set was used to show the superiority of the particular case for fitting real-world data sets over other compared well-known models.

Influence of COVID-19 vaccination on the dynamics of new infected cases in the world.

The initial COVID-19 vaccinations were created and distributed to the general population in 2020 thanks to emergency authorization and conditional approval. Consequently, numerous countries followed the process that is currently a global campaign. Taking into account the fact that people are being vaccinated, there are concerns about the effectiveness of that medical solution. Actually, this study is the first one focusing on how the number of vaccinated people might influence the spread of the pandemic in the world. From the Global Change Data Lab "Our World in Data", we were able to get data sets about the number of new cases and vaccinated people. This study is a longitudinal one from 14/12/2020 to 21/03/2021. In addition, we computed Generalized log-Linear Model on count time series (Negative Binomial distribution due to over dispersion in data) and implemented validation tests to confirm the robustness of our results. The findings revealed that when the number of vaccinated people increases by one new vaccination on a given day, the number of new cases decreases significantly two days after by one. The influence is not notable on the same day of vaccination. Authorities should increase the vaccination campaign to control well the pandemic. That solution has effectively started to reduce the spread of COVID-19 in the world.

A Flexible Extension of Reduced Kies Distribution: Properties, Inference, and Applications in Biology

The extended reduced Kies distribution (ExRKD), which is an asymmetric flexible extension of the reduced Kies distribution, is the subject of this research. Some of its most basic mathematical properties are deduced from its formal definitions. We computed the ExRKD parameters using eight well-known methods. A full simulation analysis was done that allows the study of these estimators’ asymptotic behavior. The efficiency and applicability of the ExRKD are investigated via the modeling of COVID-19 and milk data sets, which demonstrates that the ExRKD delivers a better match to the data sets when compared to competing models.

Exponentiated Gull Alpha Exponential Distribution with Application to COVID-19 Data

In this paper, the main aim is to define a statistical distribution that can be used to model COVID-19 data in Mexico and Canada. Using the method of exponentiation on the gull alpha exponential distribution introduces a new distribution with three parameters called the exponentiated gull alpha power exponential (EGAPE) distribution. The distribution has the benefit of being able to represent monotonic and nonmonotonic failure rates, both of which are often seen in dependability issues. It is possible to determine the quantile function as well as the skewness, kurtosis, and order statistics of the suggested distribution. The approach of maximum likelihood is used in order to calculate the parameters of the model, and the RMSE and average bias are utilised in order to evaluate how successful the strategy is. In conclusion, the flexibility of the new distribution is demonstrated by modeling COVID-19 data. From the practical application, we can conclude that the proposed model outperformed the competing models and therefore can be used as a better option for modeling COVID-19 and other related datasets.

The Power XLindley Distribution: Statistical Inference, Fuzzy Reliability, and COVID-19 Application

The power XLindley (PXL) distribution is introduced in this study. It is a two-parameter distribution that extends the XLindley distribution established in this paper. Numerous statistical characteristics of the suggested model were determined analytically. The proposed model's fuzzy dependability was statistically assessed. Numerous estimation techniques have been devised for the purpose of estimating the proposed model parameters. The behaviour of these factors was examined using randomly generated data and developed estimation approaches. The suggested model seems to be superior to its base model and other well-known and related models when applied to the COVID-19 data set. [ FROM AUTHOR] Copyright of Journal of Function Spaces 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 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 . (Copyright applies to all s.)

A new versatile modification of the Rayleigh distribution for modeling COVID-19 mortality rates.

The aim of this paper is to specify a new flexible statistical model to analyze COVID-19 mortality rates in Italy and Canada. A new versatile lifetime distribution with four parameters is proposed by using the exponentiated generalized class of distributions and the gull alpha power Rayleigh distribution to form the exponentiated generalized gull alpha power Rayleigh (EGGAPR) distribution. This new distribution is characterized by a tractable cumulative distribution function. To estimate the unknown parameters of the proposed distribution the maximum likelihood estimation method is used. In evaluating the effectiveness of the MLE method graphical displays of the Monte Carlo simulation are presented. The EGGAPR distribution is compared to its sub-models which include the exponentiated gull alpha Rayleigh distribution, the gull alpha Rayleigh distribution, exponentiated generalized Rayleigh distribution, exponentiated Rayleigh distribution and the Rayleigh distribution. Different measures of goodness-of-fit are used to investigate whether the EGGAPR distribution is more flexible and fit than its sub-models in modeling COVID-19 mortality rates.

A New Approach in Identifying the Psychological Impact of COVID-19 on University Student’s Academic Performance

COVID-19 was first discovered in Wuhan, China on December 2019. It is one of the worst pandemics in human history. The education sector is one of the sectors most affected by the COVID-19 pandemic. Educators and students worldwide were forced to shift to online learning. Students, in particular, have suffered from the psychological impacts and learning difficulties caused by the lockdowns imposed by governments to control the pandemic. In this study, we used statistics and machine learning approaches to study the impact of COVID-19 pandemic on educations systems especially on university students’ psychological health. For this purpose, a questionnaire was created,which consisted of two primary parts. The first part collects the participant’s demographic and educational characteristics, while the second part gathers information about five primary dimensions: the use of digital devices, sleep habits, social communication, emotional mental state, and academic performance. The questionnaire was distributed to university students in three Arab countries: Egypt, Jordan, and Saudi Arabia. A total of 1766 responses were returned and analyzed using statistical and machine learning approaches. The results showed an evident correlation between student’s psychological health and the use of online education during the time COVID-19. The results also showed that there is a positive relationship between digital tools used for online education and the academic performance of students during the time of COVID-19. Finally, the results highlighted the harmful impacts of COVID-19 on the education systems. The study ends by presenting suggestions and recommendations needed to improve the current online education system. Moreover, it is warranted for universities to have a role in helping students respond to the pandemic in a healthier psychological performance.

RETRACTED: Remdesivir Efficacy in COVID-19 Treatment: A Randomized Controlled Trial.

RETRACTED ARTICLE: To date, no antiviral therapy has shown proven clinical effectiveness in treating patients with COVID-19. We assessed the efficacy of remdesivir in hospitalized Egyptian patients with COVID-19. Patients were randomly assigned at a 1:1 ratio to receive either remdesivir (200 mg on the first day followed by 100 mg daily for the next 9 days intravenously infused over 30-60 minutes) in addition to standard care or standard care alone. The primary outcomes were the length of hospital stay and mortality rate. The need for mechanical ventilation was assessed as a secondary outcome. Two hundred patients (100 in each group) completed the study and were included in the final analysis. The remdesivir group showed a significantly lower median duration of hospital stay (10 days) than the control group (16 days; P < 0.001). Eleven of the patients in the remdesivir group needed mechanical ventilation compared with eight patients in the control group (P = 0.469). The mortality rate was comparable between the two groups (P = 0.602). Mortality was significantly associated with older age, elevated C-reactive protein levels, elevated D-dimer, and the need for mechanical ventilation (P = 0.039, 0.003, 0.001, and < 0.001 respectively). Remdesivir had a positive influence on length of hospital stay, but it had no mortality benefit in Egyptian patients with COVID-19. Its use, in addition to standard care including dexamethasone, should be considered, particularly in low- and middle-income countries when other effective options are scarce.

Seroprevalence of IgG Anti-SARS-CoV-2 among Voluntary Blood Donors in Dubai: Demographic and Risk Factors

Background and Objectives: During the pandemic of CO-VID-19, additional blood donor eligibility criteria have been added to ensure safety of donors and donations. The aim of this study is to estimate the seroprevalence of SARS-CoV-2 antibody in eligible donors and to study the demography and lifestyle of asymptomatic cases. Materials and Methods: One thousand four hundred eighteen eligible donors were enrolled in this study in the Dubai Blood Donation Center from August to December 2020. Blood samples were tested for SARS-CoV-2 antibody. Donors' demographic characteristics and lifestyle were studied further. The χ test was used to analyze the data association. Results: 88.4% of the studied group were males, 85% were from age 17 to 45 years, and 53% were regular donors. The seroprevalence of SARS-CoV-2 antibody was 13.5% and significantly related to gender, frequency of donation, collection site, type of donation, and more frequent when donors shared transport and accommodation. Blood group and age had no significant association. 5.5% were found to have cough/fever or a history of contact with positive COVID-19 patients. Conclusions: The seroprevalence of SARS-CoV-2 antibody was 13.5% among eligible blood donors, and hence the current eligibility criteria allow asymptomatic COVID-19 patients to donate blood. The seroprevalence was seen more in first time donors, suggesting that regular blood donors are safer. Additional risk factors related to lifestyle can be identified to ensure safety of transfusion during COVID-19 pandemic.

The impact of COVID-19 pandemic on blood supplies and transfusion services in Eastern Mediterranean Region.

BACKGROUND: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spurred a global health crisis. The safety and supply of blood during this pandemic has been a concern of blood banks and transfusion services as it is expected to adversely affect blood system activities. We aim to assess the situation in the Eastern Mediterranean Region (EMR) during the first months of the pandemic. MATERIALS AND METHODS: A survey was designed to address blood supply, transfusion demand, and donor management during the coronavirus disease-19 (COVID-19) pandemic. Medical directors of different blood banks were invited to participate. RESULTS: A total of 16 centers participated with representation from 15/19 countries in the region. In total, 75% were from national blood banks. Most centres had a decrease in the blood supply, ranging from 26-50%. Representatives from 14 countries (93.3%) believed that public fear has contributed to a decrease in donations. Most centres (n=12, 75%) had a reduction in transfusion demand, while those who did not, reported heavy involvement in treating patients with underlying haemoglobinopathies and haematological malignancies. Half of the centres activated their contingency plans. Four centres had to alter the blood donor eligibility criteria to meet demands. All centres implemented donor deferral criteria in relation to SARS-CoV-2, but were variable in measures to mitigate the risk of donor and staff exposure. CONCLUSION: Blood services in the region faced variable degrees of blood shortages. We summarize lessons learnt during this pandemic for the blood banks to consider to plan, assess, and respond proportionately to future similar pandemics.