SARS-CoV-2 VARIANTS' TEMPORAL AND VL DISTRIBUTIONS IN IMMUNOCOMPROMISED PATIENTS

Background: The COVID-19 pandemic has been striking for three years and, despite the regular arise of new variants, populations are now widely immune and protected from severe symptoms. However, immunocompromised patients still have worse clinical outcomes, higher mortality and rarely develop effective immunity through vaccination or infection. Here, we studied the temporal distribution of infections, viral loads (VL) as well as the viral genetic diversity among an immunocompromised patient cohort, between January 2021 and September 2022. Method(s): Overall, 478 immunocompromised patients (solid organ transplant, HIV positive, cancer, autoimmune disease) and 234 controls (healthcare workers) from Pitie-Salpetriere and Bichat Claude-Bernard University hospitals (Paris, FRANCE) were diagnosed with SARS-CoV-2 infection by RT-qPCR. Whole genome sequencing was performed according to ARTIC protocol on Oxford Nanopore platform. All 712 full viral genomes were used to determine lineages and mapped to Wuhan-Hu-1 reference to produce a maximum likelihood phylogenetic tree (IQTree, 1000 bootstraps). Differences in temporal distributions of infections and VL were assessed using nonparametric statistical tests. Result(s): According to phylogenetic analysis, genomes from SARS-CoV- 2 infecting immunocompromised patients and those infecting healthy individuals are distributed in a similar way. No significant genetic differences can be observed between viral genomes from patients and controls within the different lineages. Temporal distribution of COVID-19 infections were also similar between immunocompromised patients and controls, with the exception of BA.2 variant for which controls were infected earlier (p< 0.001). VL were significantly lower in immunocompromised patients infected with Omicron variants (p=0.04). No differences in VL were observed for Alpha and Delta variants. Conclusion(s): At diagnosis, no intrinsic genetic divergence was observed in virus infecting immunocompromised patients compared to those circulating in the general population. Similarities in temporal distribution of infections between controls and patients suggest that these different groups become infected concomitantly. VL appeared to be lower for Omicron variants in immunocompromised patients. An earlier VL peak of Omicron and a testing of immunocompromised patients hospitalized once severe symptoms have appeared could indicate a delayed testing in these patients, once the replicative phase over. (Figure Presented).

Evolution of Clusters Transmission Patterns in France during the Last Decade

Background: The recent transmission clusters (RTCs) identified through phylogenetic approaches allow to describe the main transmission networks. This render possible to describe potential shifts among HIV transmission routes and populations and, in some cases, to specifically target prevention measures. Here we describe the evolution of RTCs over the last decade in a specialized laboratory serving centers from the entire French territory. Method(s): We extracted all the HIV reverse transcriptase sequences available between 01/01/2013 and 31/08/2022. The sequences dataset was studied overall and divided into three equal time periods: 2013-15, 2016-18, 2019-2021. The first sequences available for each patient were aligned and the trees were reconstructed by maximum likelihood using IQtree software. Clusters, defined by a maximum genetic distance < 4.5% and a branch support >90%, were extracted using ClusterPicker. Result(s): Overall, 8591 sequences were included. Among them, 950 RTCs were identified including 2492 sequences (29%) and 68 large RTCs ( >4 sequences) with 475 (5.6%) sequences. The mean duration of large RTCs (from the first to the last sequences) was 5.1 years [IQR: 4.1-7.1] and 34 were still active (including at least one sequence during the last year of the study period). 3640, 2897 and 2157 sequences were included for the 2013-15, 2016-18 and 2019-2021 periods, respectively. We identified 298 RTCs (19.5% of sequences), 249 (20.4%) and 226 (27.5%) among those periods, respectively. While the number of sequence pairs decreased from 2013-15 to 2019-21, the number of large RTCs increased steadily (see Table 1). During the period 2019-21, including the largest clusters, patients belonging to a RTC were more often male (68 vs 58%, p< 0.001) and younger (average age: 39 vs 44 years, p< 0.001) than non-RTC patients. This observation was even more marked for very large RTCs (see Table 2). It should be noted that the largest cluster (14 patients) was mainly composed of women and located in French overseas territories. Conclusion(s): This study shows an evolution of the structure of HIV sequence clusters over time with a decreasing number of small RTCs but an increasing number of large RTCs. These trends can be explained by a better global control of transmission, due in part to TasP, but not preventing some super-transmitters networks, despite PrEP use and not only including MSM is some settings. The COVID period does not seem to have strongly prevented such large transmission networks.

Viral Suppression Trajectories Destabilized after Covid-19 among Us People with Hiv

Background: Disruptions in clinical services during the COVID-19 pandemic could compromise past progress towards meeting U.S. Ending the HIV Epidemic (EHE) goals. We examined changes in the proportion with virologic suppression (VS) before and since the onset of COVID-19 in a multi-site U.S. cohort of people with HIV (PWH) using an interrupted time series design. Method(s): We assessed VS (< 200 copies/mL) trajectories 1/1/2018-1/1/2022, comparing trends before and after March 21, 2020 at 8 HIV clinics within the U.S. Center for AIDS Research Network of Integrated Clinical Systems (CNICS'). Hierarchical mixed-effects logistic regression and interrupted time series analyses examined changes in the trend (i.e., slope) of VS over time, and maximum likelihood estimation was used to account for missing VS data among those lost to follow-up (LTFU) post-COVID-19. Analyses were adjusted for demographics, site, CDC transmission group, CD4 nadir, VS, time on ART. Result(s): Data from 17,999 participants were included, providing a total of 120,918 VS assessments. Median age was 53 (interquartile range 42-61);19% were female sex at birth;the mean time on ART was 9.5 years;18% were unsuppressed at any point;17.7% were LTFU. Among the overall population, prior gains in VS slowed during COVID-19 (adjusted odds ratio [AOR] 0.93 per quarter-year;95% CI: 0.88-0.98;p=0.004;Figure). Greater impacts occurred among women (AOR 0.90;95% CI 0.81-0.99;p=0.05), persons with a history of injection drug use (PWID) (AOR 0.77 95% CI: 0.66-0.90;p=0.001), and Black PWH (AOR 0.90;95% CI: 0.84-0.96;p=0.001) in whom prior positive VS trends plateaued or began to reverse (Figure). VS remained lower among those with unstable housing (AOR 0.44;95% CI: 0.40-0.50;p< 0.001) but stayed unchanged from the pre-pandemic period. Conclusion(s): Previous gains in VS slowed during the COVID-19 pandemic among PWH in a multi-site network of U.S. HIV clinics. Known disparities in VS according to housing status remain unchanged, but VS disparities worsened for PWH who were women, PWID, or Black. Changes in VS trends could be related to socioeconomic impacts of the pandemic, insurance lapses, reduction of in-person clinic services, fear of coming to clinics, or other factors. Renewed investment in HIV public health and clinical services will be vital to achieve the U.S. EHE goals following COVID-19, with additional targeted interventions to support key populations with persistent or worsening disparities needed.

VARIABLE REDUCTIONS IN SARS-CoV-2 VARIANT IMPORTATIONS FOLLOWING TRAVEL RESTRICTIONS

Background: Retrospectively quantifying effectiveness of interventions such as travel restrictions to counter viral introduction and transmission is critical to inform public health policy. Phylogenetic analyses of SARS-CoV-2 variants were undertaken to quantify the effects Canadian COVID-19 travel restrictions had on variant importation and transmission dynamics. Method(s): Global and Canadian GISAID sequences available up to March 2022 were subsampled proportionally to variant-specific case counts and ten phylogenies were inferred for each variant. Trees, dates, and geographies were inferred using maximum likelihood. Result(s): In response to Alpha, Canada implemented a UK flight ban from December 20, 2020-January 6, 2021, resulting in a 1.5-fold reduction in UK sublineage importation rate, with subsequent rebound (Fig. 1). Enhanced screening measures were implemented on December 24, 2020 for South African arrivals to counter Beta. Although there was a 6.3-fold reduction of Beta sublineages per week from South Africa following enhanced screening, there is low confidence in rare events. For Gamma, enhanced screening for arrivals from Brazil was implemented March 31-April 13, 2021. Proportion of Gamma sublineages from Brazil was reduced 1.6-fold within 2 weeks of the intervention, but the weekly importation rate was not significantly changed from start to end of intervention. In response to Delta, Canada issued a suspension of flights from India from April 22-September 23, 202, coinciding with a 2.4-fold reduction in sublineage importation and 3.8-fold reduction in proportion of sublineages from India. Increased importations from the USA and Europe progressively negated the ban's effectiveness. Against Omicron, Canada banned entry of all foreign nationals who had travelled through southern Africa and implemented enhanced screening for Canadians from November 26- December 18, 2021. Subsequently, the BA.1 sublineage importation rate from South Africa was maintained at a low level amid rising cases, while importations from other sources increased, reducing the proportion of sublineages from South Africa and diluting the measure's effectiveness. Conclusion(s): Flight bans and enhanced screening against SARS-CoV-2 variants were most effective when implemented rapidly and for lengthier time;however, effectiveness declined as variants became globally widespread. Ongoing genomic surveillance programs incorporating phylodynamic analyses can inform travel restriction and non-pharmaceutical intervention policy. (Figure Presented).

Potenciální produkt a mezera výstupu v období ekonomických krizí

The article investigates potential output and output gap modelling and estimation in the Czech Republic in the period 1996-2021, including the global recession from 2008 and the recent crisis caused by government measures against the COVID-19 pandemic. The unobserved components (UC) methodology is applied, coefficients are estimated by the maximum likelihood method, unobserved variables are estimated using the Kalman filter. The standard UC model is modified in an original way to nonlinearly describe the hysteresis effect by allowing the output gap to have an asymmetrical influence on potential output. The econometric model verification proved significance of the hysteresis effect and showed a substantial inertia of negative consequences of both crises. Predictions of an impact of the War in Ukraine on the gap were also calculated and the uncertainty associated with these predictions was quantified.

Epidemiology and Molecular Characterizations of Coronavirus from Companion Animals Living in Chengdu, Southwest China

The recent COVID-19 pandemic has once again caught the attention of people on the probable zoonotic transmission from animals to humans, but the role of companion animals in the coronavirus (CoV) epidemiology still remains unknown. The present study was aimed to investigate epidemiology and molecular characterizations of CoVs from companion animals in Chengdu city, Southwest China. 523 clinical samples from 393 animals were collected from one veterinary hospital between 2020 and 2021, and the presence of CoVs was detected by end-point PCR using pan-CoV assay targeting the RdRp gene. Partial and complete S genes were sequenced for further genotyping and genetic diversity analysis. A total of 162 (31.0%, 162/523) samples and 146 (37.2%, 146/393) animals were tested positive for CoVs. The positive rate in rectal swabs was higher than that in eye/nose/mouth swabs and ascitic fluid but was not statistically different between clinically healthy and diseased ones. Genotyping identified twenty-two feline enteric coronavirus (FCoV) I, four canine enteric coronavirus (CECoV) I, fourteen CECoV IIa, and one CECoV IIb, respectively. Eight complete S genes, including one canine respiratory coronavirus (CRCoV) strain, were successfully obtained. FCoV strains (F21071412 and F21061627) were more closely related to CECoV strains than CRCoV, and C21041821-2 showed potential recombination event. In addition, furin cleavage site between S1 and S2 was identified in two strains. The study supplemented epidemiological information and natural gene pool of CoVs from companion animals. Further understanding of other functional units of CoVs is needed, so as to contribute to the prevention and control of emerging infectious diseases.

Consumers' Behavior in the Field of Organic Agriculture and Food Products during the COVID-19 Pandemic in the Czech Republic: Focus on a Comparison of Hyper-, Super- and Farmers' Markets and Direct Purchases from Producers

The aim of this paper is to assess Czech food consumers' behavior when buying organic products during the COVID-19 pandemic, with an emphasis on the place of purchase of organic agriculture and food products—especially those purchases with the shortest logistics value chain, i.e., purchase at farmers' markets, or directly from the producer—and a comparison with the current most common places of purchase of organic products in the Czech Republic, supermarkets and hypermarkets. Categorical data analysis methods were used to create a profile of the consumer according to the most frequent purchase locations. To create mathematical–statistical models and interpretations, the methods of logistic regression, correspondence analysis and contingency table analysis were chosen. According to the results of the survey, respondents under 25 years of age are the least likely to make purchases at farmers' markets or directly from the producer. Consumers aged 26–35 and with a university degree are the most likely to buy organic agriculture and food products at this location, followed closely by older respondents in the categories 36–45 and 46+ and with a secondary education. It is important for manufacturers to have an overview of where, in what quantities, and for what reasons consumers buy their products, especially for reasons of production optimization and planning, ecological concerns, rural development, and the impact on local areas and the value chain.

Characterization and Evaluation of the Pathogenicity of a Natural Gene-Deleted Transmissible Gastroenteritis Virus in China

Porcine transmissible gastroenteritis virus is the major pathogen that causes fatal diarrhea in newborn piglets. In this study, a TGEV strain was isolated from the small intestine of diarrhea piglets in Sichuan Province, China, and designated SC2021. The complete genomic sequence of TGEV SC2021 was 28561 bp, revealing a new natural deletion TGEV strain. Based on phylogenetic analyses, TGEV SC2021 belonged to the Miller cluster and was closely related to CN strains. The newborn piglets orally challenged with TGEV SC2021 showed typical watery diarrhea. In addition, macro and micropathological changes in the lungs and intestines were observed. In conclusion, we isolated a new natural deletion virus strain and confirmed that the virus strain has high pathogenicity in newborn piglets. Moreover, macroscopic and microscopic lesions were observed in the lungs and intestines of all TGEV SC2021-infected piglets. In summary, we isolated a new natural deletion TGEV strain and demonstrated that the natural deletion strain showed high pathogenicity in newborn piglets. These data enrich the diversity of TGEV strains and help us to understand the genetic evolution and molecular pathogenesis of TGEV.

When and How to Use Covariate Adjustment to Improve Precision in Randomized Trials

Background In May 2021, the US Food and Drug Administration (FDA) released a revised draft guidance for industry on ''Adjustment for Covariates in Randomized Clinical Trials for Drugs and Biological Products.'' This guidance discusses adjustment for covariates in the statistical analysis of randomized clinical trials in drug development programs. It specifically focuses on the use of prognostic baseline factors to improve precision for estimating treatment effects. The impact depends on the specifics of the trial, but typical sample size reductions range from 5-25% (at no cost). Despite regulators such as the FDA and the European Medicines Agency recommending covariate adjustment, it remains highly underutilized leading to inefficient trials in many disease areas. This is especially true for binary, ordinal, and time-to-event outcomes, which are quite common in COVID-19 trials and are, moreover, prevalent as primary outcomes in many disease areas (e.g. Alzheimer's disease and stroke). Research and guidance on this topic could therefore not be more timely. In response to the FDA draft guidance on covariate adjustment, this session invites experts who represent a variety of viewpoints, coming from academia and Pharmaceutical industry. The aim of this session is to provide insight into the state-of-the-art methods at a high level and from a practical perspective. We moreover want to discuss the main obstacles that lead to the underutilization of covariate adjustment, all of which we aim to surmount in this session. Finally, we want to discuss the connections of the different talks to the FDA draft guidance and provide options for better practice. Talk by Min Zhang ''Covariate adjustment for randomized clinical trials when covariates are subject to missingness.'' One practical issue that may have limited the use of covariate adjustment is that covariates are often subject to missingness. Existing statistical methodologies often ignore this issue and assume covariates are completely observed. We discuss conditions under which robust covariate adjustment can be achieved when the missingness of covariates is present. We study various methods for handling missing data and compare their performances in terms of robustness and efficiency through comprehensive simulation studies. Recommendations on strategies for handling missing covariates to achieve robust covariate adjustment are provided. Talk by Mark van der Laan on ''Targeted Learning of causal effects in randomized Trials with continuous time-to-event outcomes.'' Targeted maximum likelihood estimation (TMLE) provides a general methodology for estimation of causal parameters in the presence of high-dimensional nuisance parameters. Generally, TMLE consists of a twostep procedure that combines data-adaptive nuisance parameter estimation with semi-parametric efficiency and rigorous statistical inference obtained via a targeted update step. In this talk, we demonstrate the practical applicability of TMLE for standard survival and competing risks settings where event times are not confined to take place on a discrete and finite grid. We demonstrate TMLE updates that simultaneously target point-treatment-specific survival curves and treatmentcause- specific subdistributions in the competing risk setting, across treatment and time points. We consider the case that we only observe baseline covariates as well as the case that we also track time-dependent covariates that potentially inform censoring/drop-out. This results in estimates that are not only fully efficient, but also respect the natural monotonicity of survival functions and cause-specific subdistributions. It moreover makes sure that the sum of subdistributions and survival equals 1. We propose a super-learner for the causespecific conditional hazards that incorporate many possible Cox models as well as a variety of highly adaptive Lasso estimators. Asymptotic theoretical guarantees are given and finite-sample robust performance is demonstrated with simulations. We illustrate the usage of the considered methods for a ovo Nordisk Leader study as well as for publicly available data from a trial on adjuvant chemotherapy for colon cancer. Talk by Kelly Van Lancker on ''Combining Covariate Adjustment with Information Monitoring and Group Sequential Designs to Improve Randomized Trial Efficiency'' In this talk, we focus on the knowledge gap in statistical methodology that leads to the underutilization of covariate adjustment. A first obstacle is the uncertainty of its efficiency gain and corresponding sample size reduction at the design stage;an incorrect projection of a covariate's prognostic value risks an over- or underpowered future trial. A second open problem is the incompatibility of many covariate-adjusted estimators with the commonly used group sequential, information-based designs (GSDs). To overcome these challenges, we suggest combining covariate adjustment with information monitoring and continuing the trial until the required information level is surpassed. Since adjusted estimators typically have smaller variance than standard estimators, the information accrues faster leading to faster trials. Building on this, we propose a new statistical method that orthogonalizes estimators in order to (1) have the independent increments property needed to apply GSDs and (2) simultaneously improve (or leave unchanged) the variance at each analysis. Such a method is needed in order to fully leverage prognostic baseline variables to speed up clinical trials without sacrificing validity or power. We prove that this method has properties such as the independent increments, consistency, asymptotic normality, and correct type I error and power, and evaluate its performance in simulations and data analyses. Discussion by Frank Bretz This talk will discuss connections between the three previous presentations in the session and recommendations in the May 2021 FDA revised draft guidance for industry document on ''Adjustment for Covariates in Randomized Clinical Trials for Drugs and Biological Products.'' It will moreover touch on the broad impact of covariate adjustment for the pharmaceutical industry and provide advice on better practice.

SECTORAL CONNECTEDNESS AND RISK SPILLOVERS IN THAILAND'S STOCK MARKET

Investigating the mechanisms of risk transmission within economic sectors is vital for comprehending the interconnectedness among industries. This study aims to examine the channels of risk propagation by analyzing volatility spillovers within eleven sectors of Thailand's stock market from January 2012 to December 2021. The sectoral volatility is estimated using the ARMA-GARCH technique. The paper utilizes the connectedness measures developed by Diebold and Yilmaz (2009, 2012, 2014) to examine changes in sectoral connectedness and identify significant trends in specific sectors before and during the COVID-19 pandemic. The result is that total volatility connectedness has increased significantly during the COVID-19 pandemic, indicating a significant rise in systematic risk. The Petrochemical and Chemical sector became the largest transmitter during the COVID-19 pandemic. These two findings are consistent with several studies on sectoral connectedness during the COVID-19 situation. In addition, some certain sectors shifted their role from a net transmitter to a net receiver and vice versa. Investors should be aware of the impact of an increase in systematic risk and the switching roles of net transmitters and net receivers when selecting hedging strategies. The Banking sector and the Finance and Security sector did not transmit much volatility to the market. They were net receivers for both the pre-COVID and the COVID periods. The Finance and Security sector was the largest receiver of volatility shocks during the pandemic. This raised concerns about the future stability of Thailand's financial sector. Overall, the results of this study contribute to an understanding of the changes in sectoral connectedness and risk spillovers in Thailand's stock exchange as a result of the COVID-19 situation.

Modeling time to event for epicenters of the pandemic COVID-19

The epidemic of COVID-19 has been the most mathematically informative pandemic. The unprecedented information gives rise to some unprecedented models, problems, and discussions. One of these new matters is modeling the epicenters of a pandemic. The present paper is the first attempt to model the waiting time to introduce a new epicenter during a pandemic. This modeling is conducted in terms of time-to-event, the number of epicenters, and the normalized time. We model the waiting time data by an exponential distribution, therefore, the number of epicenters can be represented through a Poisson process. Then, the parameters are estimated by the method of moments and maximum likelihood method. All the simulations are the result of 10,000 runs conducted on MATLAB R2015b. It is expected to encounter 12 and 14 (with probability 95%, 3-24 and 7-23) epicenters from 15th May to 13th June and from June 14 to July 12, 2020, respectively. We forecast that the cumulative number of confirmed cases for coming epicenters is over 10,000 when they join the existing epicenters. The paper suggests that the time to epicenter is a suitable criterion to compare the spreading speed of an epidemic in different periods or even different epidemics. Highlights: The study aims to model the time to the next epicenters during the pandemic COVID-19. The study introduces the time to epicenter as a criterion to study of spreading speed of an epidemic in different periods or compare different epidemics. The study deals with the number of cumulative confirmed cases at the time that a region become epicenter. The study proposes the Poisson process as the model to describe the number of epicenters. The study suggests that exponential distribution can model the time to event for the epicenters of COVID-19.Copyright © 2023 International Biometric Society-Chinese Region.

Relationship Between Phylogenetic of Apium and Foeniculum Plants from Central Java, Indonesia, and Their Secondary Metabolites Potency against COVID-19 Protease

BACKGROUND: The emergence of COVID-19 in the late of 2019 resulted in the massive screening of drugs, including natural products, to support the current vaccines. Apium and Foeniculum vegetables are members of the Apiaceae family that potentially used to be natural immunosuppressant. AIM: The purpose of this research is to analyze the phylogenetic position between these two plants as well as find out their secondary metabolites potency against COVID-19 main protease (Mpro) and the papain-like protease (PLpro). METHODS: The phylogenetic analysis of Apium and Foeniculum from Indonesia was carried out based on internal transcribed spacer (ITS) region and the bioactive virtual screening assay was completed through AutoDock Vina software. CONCLUSION: Overall, Apium and Foeniculum have close relationships among the members of Apiaceae after maximum likelihood analysis. Furthermore, it also has 70 similar bioactive compounds that some of these potentially inhibit both of COVID-19 proteases.

Household genomic epidemiology of SARS-CoV-2 in rural coastal Kenya

Introduction/ Background: Household environments are characterized by frequent person-to-person contacts and potential transmission of respiratory infections. We used whole genome sequencing to describe the molecular epidemiology of SARS-CoV-2 in households in rural coastal Kenya. Methods: We collected 1,802 nasopharyngeal/ oropharyngeal swabs from 137 households (502 participants) in Kilifi County between 10th December 2020 and 14th September 2021. These households were selected because a member had been confirmed to have SAR-CoV-2 infection by routine health service testing, or because a member was a close contact of a confirmed case. RT- PCR positive samples with a cycle threshold of < 30.0 were targeted for genome sequencing. Phylogenetic relationships were inferred using maximum likelihood methods and the number of independent introductions into the households inferred using both pairwise nucleotide differences and ancestral state reconstruction approaches. Results: A total of 332 samples from 155 participants in 71 households tested SARS-CoV-2 positive, 132 (39.7%) of which yielded genomes with >80% coverage (73 participants in 41 households). All recovered genomes were classified within lineages of known variants of concern: Alpha (n=60), Beta (n=18) and Delta (n=54). Of the 41 households with sequence data recovered, 26 (63%) had one distinct introduction, 10 (24%) had two introductions, 4 (10%) had three introductions and 1 (3%) had four introductions. Among these sequenced households 31 within household transmission events and 16 interhousehold transmission events were identified from the genomic data. Impact: Transmission of SARS-CoV-2 infection within and between households is common in rural Kenya, is often asymptomatic, and realistic measures to mitigate infection spread within households are needed to reduce the disease burden. Conclusion: We found both frequent SARS-CoV-2 transmission within households and its multiple introductions into households. Genomic data adds value in estimating household attack rates by distinguishing single from multiple introductions to households.

The Influence of Taxation on Income Inequality: Analysis of the Practice in the EU Countries

The growing economic inequality around the world is recognized as a global problem of mankind. At the same time, the key tool for reducing inequality and ensuring the achievement of sustainable development goals is the taxation system given its distributive function. That is why this paper puts forward and proves a scientific hypothesis according to which direct taxation has a significant impact on economic inequality, with its scale and sphere depending on the level of economic development and the specific architecture of the tax system adopted in a particular country. The study relies on data from 28 European Union countries, including the United Kingdom, whose tax systems are not identical but harmonized in accordance with European Union directives, the same as the legislation in other economic sectors. Accordingly, it can be concluded that similar institutional characteristics are present. We have used the method of two-stage cluster analysis, which is meant for identifying the natural splitting of the mass of data into groups, then carried out regression analysis and built some models. The contribution of the study is revealing a number of important regularities that are significant for characterizing the dependence of income inequality on direct taxation as well as formulation recommendations for improving the tax policies of European Union countries, with the potential of policy implications. The results obtained can play a significant role in the development and further harmonization of tax systems and resolving the global problem of increased inequality within and between countries.

Factores que influyen en la satisfacción del alumnado universitario en la educación en línea: Un estudio con SEM (Modelo de ecuaciones estructurales)

By taking as a reference the results obtained with a questionnaire used during the class period among undergraduate students at the Quevedo State Technical University (UTEQ) of Ecuador in the period Covid-19. The instrument consists of 35 questions classified into six factors: student-content interaction, student-teacher relationship, use of SGA platform and other digital tools, evaluation of online education, technological resources and evaluation of online education. The results obtained show that the proposed theoretical model is acceptable, which was confirmed by estimating it using the maximum likelihood method, establishing that the factors that predominantly affect student satisfaction in online education are: student-content interaction, student-teacher relationship and technological resources. El mes de marzo del 2020 se convirtió en un mes que América Latina no podrá olvidar debido a la suspensión de clases presenciales que ocurrió en casi todo el mundo como consecuencia directa de la cuarentena a resguardar por el COVID 19, donde el pánico colectivo, el estrés generado por el confinamiento y el rol de las instituciones educativas frente al uso de herramientas tecnológicas para crear ambientes de aprendizaje virtual improvisados, nos lleva a replantearnos el modo y la forma en que se educa en tiempos de crisis.

Behavioural Intention To Use University Websites: A Scale Validation

In unprecedented times such as the COVID-19 pandemic, many customers are increasingly looking to online platforms and websites to make their purchasing decisions instead of in-store purchases and face-to-face contact with a business representative. Therefore, it is important that businesses, including universities invest the necessary time, effort and resources into their websites to ensure that it is up-to-date, user friendly and regularly used and visited by their customers. To understand better customers' behavioural intention to use university websites, a validated behavioural-intention-to-use scale is necessary. Following a widespread online search of the largest databases available for academic research, no proof of a validated behavioural-intention-to-use scale could be found within the South African university website context. As such, the purpose of this study was to contribute to the literature by explaining the process followed to validate a behavioural-intention-to-use scale within the South African university website context. The study applied a descriptive and single cross-sectional research design. A non-probability convenience sample of 319 Generation Y students completed a self-administered questionnaire. Descriptive statistics, correlation analysis, multicollinearity analysis, reliability and validity measures as well as confirmatory factor analysis (maximum likelihood method) were employed for data analysis. The study's results validate the proposed measurement model of behavioural intention to use university websites as an eight-factor structure that includes information quality, playfulness, system quality, ease of use, trust, attitude, satisfaction and behavioural intention. In addition, the results indicate internal-consistency and composite reliability, nomological, construct, convergent and discriminant validity as well as acceptable model fit.

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.

MODELING DEATH RATE OF THE COVID-19 PANDEMIC IN IRAQ

In the last years, when the Coronavirus appeared, all countries tended to take the necessary precautions in order to preserve the lives of the people and protect them from exposure to this virus because of its danger that leads to the death of the infected person. There are many factors that can have an impact on the mortality rate, so we presented in this paper a study on the factors affecting the death rate with the virus (COVID-19) and accordingly, the response variable took relative numbers. Therefore, a beta regression model was the most used in cases. Ratios and fractions values are between (0-1). This model is characterized by its flexibility and works to estimate the dispersion parameter, and the method of greatest possibility has been used to estimate the parameters of the model to study the mortality rate, real data for the community during the postvaccination period was relied on in Iraq, where it was studied in order to know the factors that affect this percentage.