ABSTRACT
Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using the Reynolds-averaged Navier-Stokes equations coupled with the realizable k-model and the discrete random walk model, respectively. Via simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection, which shows itself by the multi-modal distribution as a weighted sum of normal and Weibull distributions. Furthermore, it is shown that the turbulent MC channel is characterized via multi-modal, i.e., sum of weighted normal distributions, or stable distributions, depending on the air velocity. Crown
ABSTRACT
A proposed truncated Rayleigh odd Weibull-G family with one ofits sub-models, truncated Rayleigh odd Weibull exponential distribution, are introduced. Several statistical properties along with linear representation, reliability measures, entropies, and reliability stress strength model are presented. The unknown parameters are estimated through the maximum likelihood method. Simulation and real application are used to evaluate the new distribution's flexibility and usefulness. For different compared information criteria, the results clearly show that the proposed distribution has superior output relative to other competitive distributions demonstrating its consistent and flexible performance, as well as representing an optimal statistical model for the distribution of COVID-19 death cases in Iraq. © 2022 IEEE.
ABSTRACT
The two-parameter classical Weibull distribution is commonly implemented to cater for the product's reliability, model the failure rates, analyze lifetime phenomena, etc. In this work, we study a novel version of the Weibull model for analyzing real-life events in the sports and medical sectors. The newly derived version of the Weibull model, namely, a new cosine-Weibull (NC -Weibull) distribution. The importance of this research is that it suggests a novel version of the Wei-bull model without adding any additional parameters. Different distributional properties of the NC-Weibull distribution are obtained. The maximum likelihood approach is implemented to esti-mate the parameters of the NC-Weibull distribution. Finally, three applications are analyzed to prove the superiority of the NC-Weibull distribution over some other existing probability models considered in this study. The first and second applications, respectively, show the mortality rates of COVID-19 patients in Italy and Canada. Whereas, the third data set represents the injury rates of the basketball players collected during the 2008-2009 and 2018-2019 national basketball associ-ation seasons. Based on four selection criteria, it is observed that the NC-Weibull distribution may be a more suitable model for considering the sports and healthcare data sets.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).
ABSTRACT
Multivariate distributions are helpful in the simultaneous modeling of several dependent random variables. The development of a unique multivariate distribution has been a difficult task and different multivariate versions of the same distribution are available. The need is, therefore, to suggest a method of obtaining a multivariate distribution from the univariate marginals. In this paper, we have proposed a new method of generating the multivariate families of distributions when information on univariate marginals is available. Specifically, we have proposed a multivariate family of distributions which provides a univariate transmuted family of distributions as marginal. The proposed family is a re-parameterization of the Cambanis (1977) family. Some properties of the proposed family of distributions have been studied. These properties include marginal and joint marginal distributions, conditional distributions, and marginal and conditional moments. We have also obtained the dependence measures alongside the maximum likelihood estimation of the parameters. The proposed multivariate family of distributions is studied for the Weibull baseline distributions giving rise to the multivariate transmuted Weibull (MTW) distribution. Real data application of the proposed MTW distribution is given in the context of modeling the daily COVID-19 cases of the World. It is observed that the proposed MTW distribution is a suitable fit for the joint modeling of the COVID-19 data. © 2022, National Science Foundation. All rights reserved.
ABSTRACT
Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using Reynolds-averaged Navier-Stokes equations coupled with realizable k-epsilon model and the discrete random walk model, respectively. Via the simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection which shows itself by the multimodal distributions as a weighted sum of normal and Weibull distributions. © 2022 IEEE.
ABSTRACT
Since December 2019, the world is confronted with the COVID-19 pandemic, caused by the Coronavirus SARS-CoV-2. The COVID-19 pandemic with its incredible spreading speed shows the vulnerability of a globalized and networked world. The first two years of the pandemic were characterized by several infection waves, described by length, peak, and speed. The infection waves caused a heavy burden on health systems and severe restrictions on public life, like educational system shutdown, travel restrictions, limitations regarding public life, or a comprehensive lockdown within a lot of countries. The goal of the presented research study is the analysis of the development of the six dominant infection waves in Germany within the first two years of the COVID-19 pandemic (February 2020 - February 2022). The analyses are focusing on the occurrence of infection and spreading behavior, in detail on attributes like length, peak, and speed of each wave. Furthermore, various impacts of lockdown strategies (hard, soft) or virus variants are considered. The analyses of the infection waves are based on a transfer and application of methods - especially the Weibull distribution model and statistical hypothesis tests - used in reliability engineering for analyzing the upcoming failure development within product fleets in the field. The spreading behavior of a COVID-19 infection wave can be described by the Weibull distribution model in a sound way, related to a short time interval. The interpretation of the Weibull model parameters allows the assessment of the COVID-19 infection wave characteristics and generates additional information to classical infection analysis models like the SIR model [10]. Finally, the characteristics of the COVID-19 infection waves are analyzed in the context of other common infectious diseases in Germany like Influenza or Norovirus. This study continues previous research;cf. [1-3,11,12]. © 2022 Probabilistic Safety Assessment and Management, PSAM 2022. All rights reserved.
ABSTRACT
Following the first COVID-19 infected cases, Malta rapidly imposed strict lockdown measures, including restrictions on international travel, together with national social distancing measures, such as prohibition of public gatherings and closure of workplaces. The study aimed to elucidate the effect of the intervention and relaxation of the social distancing measures upon the infection rate by means of a trendline analysis of the daily case data. In addition, the study derived a predictive model by fitting historical data of the SARS-CoV-2 positive cases within a two-parameter Weibull distribution, whilst incorporating swab-testing rates, to forecast the infection rate at minute computational expense. The trendline analysis portrayed the wave of infection to fit within a tri-phasic pattern, where the primary phase was imposed with social measure interventions. Following the relaxation of public measures, the two latter phases transpired, where the two peaks resolved without further escalation of national measures. The derived forecasting model attained accurate predictions of the daily infected cases, attaining a high goodness-of-fit, utilising uncensored government-official infection-rate and swabbing-rate data within the first COVID-19 wave in Malta.
ABSTRACT
The two-parameter classical Weibull distribution is commonly implemented to cater for the product’s reliability, model the failure rates, analyze lifetime phenomena, etc. In this work, we study a novel version of the Weibull model for analyzing real-life events in the sports and medical sectors. The newly derived version of the Weibull model, namely, a new cosine-Weibull (NC-Weibull) distribution. The importance of this research is that it suggests a novel version of the Weibull model without adding any additional parameters. Different distributional properties of the NC-Weibull distribution are obtained. The maximum likelihood approach is implemented to estimate the parameters of the NC-Weibull distribution. Finally, three applications are analyzed to prove the superiority of the NC-Weibull distribution over some other existing probability models considered in this study. The first and second applications, respectively, show the mortality rates of COVID-19 patients in Italy and Canada. Whereas, the third data set represents the injury rates of the basketball players collected during the 2008–2009 and 2018–2019 national basketball association seasons. Based on four selection criteria, it is observed that the NC-Weibull distribution may be a more suitable model for considering the sports and healthcare data sets.
ABSTRACT
Statistical distributions have great applicability for modeling data in almost every applied sector. Among the available classical distributions, the inverse Weibull distribution has received considerable attention. In the practice of distribution theory, numerous methods have been studied and suggested/introduced to increase the flexibility level of the traditional probability distributions. In this paper, we implement different distribution methods to obtain five new different versions of the inverse Weibull model. The new modifications of the inverse Weibull model are called the logarithm transformed-inverse Weibull, a flexible reduced logarithmic-inverse Weibull, the weighted TX-inverse Weibull, a new generalized-inverse Weibull, and the alpha power transformed extended-inverse Weibull distributions. To illustrate the flexibility and applicability of the new modifications of the inverse Weibull model, a biomedical data set is analyzed. The data set consists of 108 observations and represents the mortality rate of the COVID-19-infected patients. The practical application shows that the new generalized-inverse Weibull is the best modification of the inverse Weibull distribution.
ABSTRACT
The design of a new adaptive version of the multiple dependent state (AMDS) sampling plan is presented based on the time truncated life test under the Weibull distribution. We achieved the proposed sampling plan by applying the concept of the double sampling plan and existing multiple dependent state sampling plans. A warning sign for acceptance number was proposed to increase the probability of current lot acceptance. The optimal plan parameters were determined simultaneously with nonlinear optimization problems under the producer’s risk and consumer’s risk. A simulation study was presented to support the proposed sampling plan. A comparison between the proposed and existing sampling plans, namely multiple dependent state (MDS) sampling plans and a modified multiple dependent state (MMDS) sampling plan, was considered under the average sampling number and operating characteristic curve values. In addition, the use of two real datasets demonstrated the practicality and usefulness of the proposed sampling plan. The results indicated that the proposed plan is more flexible and efficient in terms of the average sample number compared to the existing MDS and MMDS sampling plans.
ABSTRACT
In December 2019, the world was confronted with the outbreak of the respiratory disease COVID-19. The COVID-19 epidemic evolved at the beginning of 2020 into a pandemic, which continues to this day. The incredible speed of the spread and the consequences of the infection had a worldwide impact on societies and health systems. Governments enforced many measures to control the COVID-19 pandemic: Restrictions (e.g. lockdown), medical care (e.g. intensive care) and medical prevention (e.g. hygiene concept). This leads to different spreading behavior of the COVID-19 pandemic, depending on measures. Furthermore, the spreading behavior is influenced by culture and geographical impacts. The spreading behavior of COVID-19 related to short time intervals can be described by Weibull distribution models, common in reliability engineering, soundly. The interpretation of the model parameters allows the assessment of the COVID-19 spreading characteristics. This paper shows the results of a research study of the COVID-19 spreading behavior depending on different pandemic time phases within Germany and Japan. Both countries are industrial nations but have many differences concerning historical development, culture and geographical conditions. Consequently, the chosen government measures have different impacts on the control of the COVID-19 pandemic. The research study contains the analyses of different pandemic time intervals in Germany and Japan: The breakout phase in spring 2020 and subsequently following waves until winter season 2020/2021. © ESREL 2021. Published by Research Publishing, Singapore.
ABSTRACT
In 2021, the COVID-19 pandemic continues to challenge the globalized world. Restrictions on public life and lockdowns of different characteristics define life in many countries. This paper focuses on the first year of the COVID-19 pandemic (01-28-2020 to 01-15-2021). As a transfer of methods used in reliability engineering for analyzing the occurrence of infection, Weibull distribution models are used to evaluate the spreading behavior of COVID-19. Key issues of this study are the differences in spreading behavior in the first and second pandemic phase and the various impacts of lockdown measures with different characteristics (hard, light). Therefore, the occurrence of infection in normed time periods with and without lockdown measures are analyzed in detail on the example of Germany representing the spreading behavior in Europe. Additional information in comparison to classical infection analyzes models like the SIR model is generated by the application of Weibull distribution models with easily interpretable parameters and the dynamic development of COVID-19 is outlined. In a further step, the occurrence of infection of COVID-19 is put into the context of other common infectious diseases in Germany like Influenza or Norovirus to evaluate the infectiousness. Differences in the spreading behavior of COVID-19 in comparison to these well-known infectious diseases are underlined for different pandemic phases. © ESREL 2021. Published by Research Publishing, Singapore.
ABSTRACT
In this paper, we introduce a new class of statistical models to deal with the data sets in the sports and health sectors. The new class is called, a novel exponent power-Y (NovEP-Y) family of distributions. By implementing the NovEP-Y approach, a new model, namely, a novel exponent power-Weibull (NovEP-Weibull) distribution is introduced. Some distributional properties of the NovEP-Y family such as identifiability, order statistics, quantile function, and moments are obtained. The maximum likelihood estimators of the parameters are also derived. Furthermore, a brief Monto Carlo simulation study is conducted to evaluate the performances of the estimators. To show the applicability of the NovEP-Weibull model, two data sets from the sports and health sciences are considered. The first data set represents the time-to-even data collected from different football matches during the period 1964-2018. Whereas, the second data set is taken from the health sector, representing the survival times of the COVID-19 infected patients. Based on some well-known statistical tests, it is observed that the NovEP-Weibull model is a very competitive dis-tribution for modeling the data sets in the sports and health sectors. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
ABSTRACT
We de_ne a new extended Weibull-H family and obtain some of its mathematical properties. It is very competitive to the beta-G and Kumaraswamy-G classes, which are highly cited in Google Scholar. The parameters of a speci_ed sub-model are estimated by eight methods and its exibility is proved in two applications to COVID-19 data.
ABSTRACT
In this study, we will look at a new flexible model known as the new double-weighted Weibull distribution. The new Weibull double-weighted distribution model is highly versatile because numerous submodels are included. The proposed model is very flexible because its density function has many shapes;it can be right skewness, decreasing, and unimodal. Also, the hazard rate function can be increasing, decreasing, up-side-down, and J-shaped. Diverse features of the novel are computed. These qualities include moments, incomplete moments, and Lorenz and Bonferroni curves and quantiles, as well as entropy and order statistics. The maximum likelihood approach is used to estimate the model's parameters. In order to evaluate the accuracy and performance of maximum likelihood estimators, simulation data are presented. The utility and adaptability of the proposed model are demonstrated by utilizing three significant datasets: daily fatalities confirmed cases of COVID-19 in Egypt and Georgia and relief times of twenty patients using an analgesic. [ FROM AUTHOR] Copyright of Discrete Dynamics in Nature & Society 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.)
ABSTRACT
The flare-up of coronavirus disease 2019 (COVID-19) was first identified in China and then spread worldwide. The concerning articles are significant because of their increasing average infection rate. The exact scientific anatomy of this circumstance remains to be discussed. Nevertheless, it is important for governments and all responsible organizations to have sound statistics and analysis to decide on possible necessary actions. This article compares the dynamics of the COVID-19 pandemic between Saudi Arabia and Egypt. We provide an appropriate process of convergence of facts that can be useful for both administrations to implement appropriate quarantine procedures. In addition, the new generalized modified Weibull distribution is provided to provide the preferred description about COVID-19 total cases, daily new cases, total deaths, and daily death statistics of Saudi Arabia and Egypt. [ 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 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.)
ABSTRACT
In this study we draw a comparison between the trends in the growing cases of novel coronavirus after the administration of vaccine doses. We compare two scenarios where how the trends have changed after the vaccine has been administered and how the trend would have looked if there were no vaccines present. This study can be used to determine the early changes that the vaccines have brought about in the trends and how much reliability do they show in preventing the cases from rising further. The predictions are made using a Weibull based Long-Short-Term-Memory approach which is also being used by the National Health Service of the UK on a dataset that takes into account features like age groups, air traffic, developmental index of the country, average temperatures of a country, which are detrimental in determining the rate of infection and deaths accurately. The model is tested on data gathered from multiple countries and the results are drawn after analyzing the result for each country as an individual entity for the conclusion to be reliable. With an increasing market competition and not so long testing period given to these vaccines which have made it to the common masses we feel this study can help predict how effectively the vaccines will be able to improve immunity against this virus and is it a viable option to invest such large capital in development and purchase of these vaccines preferring it over the organically decreasing curve following the traditional methods and natural processes. © 2021 IEEE.
ABSTRACT
The COVID-19 epidemic has affected every aspect of daily life since December 2019 and caused massive damage to the world. The coronavirus epidemic has affected more than 150 countries around the world. Many researchers have tried to develop a statistical model which can be utilized to analyze the behavior of the COVID-19 data. This article contributes to the field of probability theory by introducing a novel family of distributions, named the novel extended exponentiated class of distributions. Explicit expressions for numerous mathematical characterizations of the proposed family have been obtained with special concentration on a three-parameter submodel of the new class of distributions, named the new extended exponentiated Weibull distribution. The unknown model parameter estimates are obtained via the maximum likelihood estimation method. To assess the performance of these estimates, a comprehensive simulation study is conducted. Three different sets of COVID-19 data are used to check the applicability of the submodel case. The submodel of the new family is compared with three well-known probability distributions. Using different analytical measures, the results demonstrate that the new extended exponentiated Weibull distribution gives promising results in terms of its flexibility and offers data modeling with increasing decreasing, unimodal, and modified unimodal shapes.
ABSTRACT
The COVID-19 pandemic has led to a decentralization of the workforce in many industries. Due to the stay-at-home orders to control the spread of the virus, many are working from home. Even though modern technological advancements have helped some companies adapt to this new norm, many others are still scrambling to find the best way to remotely manage employees and accommodate their needs. Our research shows that the current challenges organizations face in managing their human capital are like the ones they face due to workplace demographic changes. This study focuses on analyzing those challenges and how human competency can be unlocked and developed to encourage sustainable autonomous working in an office, at home, or during frequent traveling. This study investigates the challenges faced by both organizations and employees, and presents a new business model that helps with the sustainable use of human resources and improves employee efficiency. © 2021 IEEE.
ABSTRACT
The Weibull regression model is one of the most important parametric regression models. Because of the knowledge of the probability distribution of the response variable following the Weibull distribution, which facilities the possibility of estimating the regression parameters based on the baseline hazard function. It is estimated by estimating the parameters of the Weibull distribution using the maximum likelihood estimation method. The R software was used for the purpose of estimating the regression coefficients and identifying the most significant features that model the outcome. In this paper, we investigate the factors affecting the progression of Corona virus patients from Al-Shiffa Hospital in the city of Mosul. In addition, this study focused on patients who were in a critical condition, and whose cases necessitated their monitoring during their stay under the artificial respiration machine Continues Positive Airway Pressure (CPAP). The six variables were taken as the most influential on the injury case and it was found that the most influential variables were Remdesivir and O2 using some statistical criteria. © 2021 IEEE.