A comparison between some methods of analysis count data by using R-packages

The Poisson regression model is a simple count data model that combines regression models in which the response variable is in the form of counts rather than fractional numbers in generalized linear models (GLMs). Three models (Poisson regression, quasi-Poisson regression, and negative binomial regression) were compared in r packages and applied to a sample of COVID-19 data in this study. The Poisson regression model was shown to be the best and most efficient of the other models. © 2023 Author(s).

The Influence of Social Media and Sustainability Advocacy on the Purchase Intention of Filipino Consumers in Fast Fashion

The large consumption of fast fashion brings many negative environmental impacts. Filipino consumers love and buy fast fashion because it is relatively cheap but trendy, and there are lots of fashionable designs to choose from. Despite the shortage in water supply and disposal issues of fast fashion, people still continue to purchase. The lack of awareness of consumers on sustainable fashion consumption led the researchers to conduct a study that aims to identify factors affecting Filipino consumers' buying decisions on fast fashion using the combined theory of planned behavior, elaboration likelihood model, and hedonic motivation. A total of 407 participants were gathered through a convenience sampling approach, and the data collected were analyzed using structural equation modeling (SEM). The result shows that attitude towards fast fashion is the highest contributing factor to purchase intention. While social media positively affects purchase intention, sustainability advocacy negatively impacts the consumers' intention to buy fast fashion. The awareness of sustainability leads to consumption reduction of fast fashion garments. Surprisingly, perceived product price and quality do not show a significant influence on purchase intention. Incorporating sustainability advocacy on social media may be a great strategy to encourage the sustainable consumption of fashion garments. The findings of this study could be a great tool to influence fashion companies and government institutions to promote sustainability awareness and transition marketing strategies to the sustainable consumption of fashion.

Online Phylogenetics with matOptimize Produces Equivalent Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Implementations.

Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 datasets do not fit this mold. There are currently over 14 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) and pseudo-ML methods may be more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger datasets. Here, we evaluate the performance of de novo and online phylogenetic approaches, as well as ML, pseudo-ML, and MP frameworks for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimization with UShER and matOptimize produces equivalent SARS-CoV-2 phylogenies to some of the most popular ML and pseudo-ML inference tools. MP optimization with UShER and matOptimize is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo inference. Our results therefore suggest that parsimony-based methods like UShER and matOptimize represent an accurate and more practical alternative to established maximum likelihood implementations for large SARS-CoV-2 phylogenies and could be successfully applied to other similar datasets with particularly dense sampling and short branch lengths.

A sampling scheme for estimating the prevalence of a pandemic

The spread of COVID-19 makes it essential to investigate its prevalence. In such investigation research, as far as we know, the widely-used sampling methods didn't use the information sufficiently about the numbers of the previously diagnosed cases, which provides a priori information about the true numbers of infections. This motivates us to develop a new, two-stage sampling method in this paper, which utilizes the information about the distributions of both population and diagnosed cases, to investigate the prevalence more efficiently. The global likelihood sampling, a robust and efficient sampler to draw samples from any probability density function, is used in our sampling strategy, and thus, our new method can automatically adapt to the complicated distributions of population and diagnosed cases. Moreover, the corresponding estimating method is simple, which facilitates the practical implementation. Some recommendations for practical implementation are given. Finally, several simulations and a practical example verify its efficiency. [ FROM AUTHOR] Copyright of Communications in Statistics: Simulation & Computation is the property of Taylor & Francis Ltd 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.)

How to reduce vaccination hesitancy? The relevance of evidence and its communicator.

Even though the immediate urgency of the COVID-19 pandemic seems to have passed, many countries did not reach the vaccination rates they initially aimed for. The stagnation in vaccine uptake during the height of the pandemic presented policy makers with a challenge that remains unresolved and is paramount for future pandemics and other crises: How to convince the (often not insubstantial) unvaccinated proportion of the population of the benefits of a vaccination? Designing more successful communication strategies, both in retrospect and looking ahead, requires a differentiated understanding of the concerns of those that remain unvaccinated. Guided by the elaboration likelihood model, this paper has two objectives: First, it explores by means of a latent class analysis how unvaccinated individuals might be characterized by their attitudes towards COVID-19 vaccination. Second, we investigate to what extent (i) varying types of evidence (none/anecdotal/statistical) can be employed by (ii) different types of communicators (scientists/politicians) to improve vaccination intentions across these subgroups. To address these questions, we conducted an original online survey experiment among 2145 unvaccinated respondents from Germany where a substantial population share remains unvaccinated. The results suggest three different subgroups, which differ regarding their openness towards a COVID-19 vaccination: Vaccination opponents (N = 1184), sceptics (N = 572) and those in principle receptive (N = 389) to be vaccinated. On average, neither the provision of statistical nor anecdotal evidence increased the persuasiveness of information regarding the efficacy of a COVID-19 vaccine. However, scientists were, on average, more persuasive than politicians (relatively increase vaccination intentions by 0.184 standard deviations). With respect to heterogeneous treatment effects among the three subgroups, vaccination opponents seem largely unreachable, while sceptics value information by scientists, particularly if supported by anecdotal evidence (relatively increases intentions by 0.45 standard deviations). Receptives seem much more responsive to statistical evidence from politicians (relatively increases intentions by 0.38 standard deviations).

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.

Phylodynamics of SARS-CoV-2 in France, Europe, and the world in 2020.

Although France was one of the most affected European countries by the COVID-19 pandemic in 2020, the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) movement within France, but also involving France in Europe and in the world, remain only partially characterized in this timeframe. Here, we analyzed GISAID deposited sequences from January 1 to December 31, 2020 (n = 638,706 sequences at the time of writing). To tackle the challenging number of sequences without the bias of analyzing a single subsample of sequences, we produced 100 subsamples of sequences and related phylogenetic trees from the whole dataset for different geographic scales (worldwide, European countries, and French administrative regions) and time periods (from January 1 to July 25, 2020, and from July 26 to December 31, 2020). We applied a maximum likelihood discrete trait phylogeographic method to date exchange events (i.e., a transition from one location to another one), to estimate the geographic spread of SARS-CoV-2 transmissions and lineages into, from and within France, Europe, and the world. The results unraveled two different patterns of exchange events between the first and second half of 2020. Throughout the year, Europe was systematically associated with most of the intercontinental exchanges. SARS-CoV-2 was mainly introduced into France from North America and Europe (mostly by Italy, Spain, the United Kingdom, Belgium, and Germany) during the first European epidemic wave. During the second wave, exchange events were limited to neighboring countries without strong intercontinental movement, but Russia widely exported the virus into Europe during the summer of 2020. France mostly exported B.1 and B.1.160 lineages, respectively, during the first and second European epidemic waves. At the level of French administrative regions, the Paris area was the main exporter during the first wave. But, for the second epidemic wave, it equally contributed to virus spread with Lyon area, the second most populated urban area after Paris in France. The main circulating lineages were similarly distributed among the French regions. To conclude, by enabling the inclusion of tens of thousands of viral sequences, this original phylodynamic method enabled us to robustly describe SARS-CoV-2 geographic spread through France, Europe, and worldwide in 2020.

The Topp-Leone Extended Exponential Distribution: Estimation Methods and Applications

Al-Shomrani et al. (2016) introduced a new family of distributions (TL-G) based on the Topp-Leone distribution (TL) by replacing the variable x by any cumulative distribution function G(t). With only one extra parameter which controls the skewness, this family is a good competitor to several generalized distributions used in statistical analysis. In this work, we consider the extended exponential as the baseline distribution G to obtain a new model called the Topp-Leone extended exponential distribution TL-EE. After studying mathematical and statistical properties of this model, we propose different estimation methods such as maximum likelihood estimation, method of ordinary and weighted least squares, method of percentile, method of maximum product of spacing, method of Cramer Von-Mises, modified least squares estimators and chi-square minimum method for estimating the unknown parameters. In addition to the classical criteria for model selection, we develop for this distribution a goodness-of-fit statistic test based on a modification of Pearson statistic. The performances of the methods used are demonstrated by an extensive simulation study. With applications to covid-19 data and waiting times for bank service, a comparison evaluation shows that the proposed model describes data better than several competing distributions.

Potential Output during Great Recession and Covid-19 Crisis

Potential output is an important economic concept representing the level of economic activity sustainable in the long run. Potential output is traditionally considered to be driven by supply-side factors such as labor supply, capital investments and new technologies. An associated concept of the output gap is usually seen as short-run deviations of actual output from its potential due to demand-side factors. The great recession of 2008 slightly altered this traditional view and suggested that a demand-side factors might also play an important role on potential output. This paper proposes a modelling framework in which short-run demand-driven fluctuations of the output gap might have an important effect on the potential output and its long-run growth. Unobserved components methodology is applied and original nonlinear conditionally Gaussian state space model is formulated and econometrically estimated for the Czech Republic by maximum likelihood methodology for the time period 1996Q1-2021Q4 including great recession of 2008 and recent covid-19 crisis.

A New Normal Regression with Medical Applications

We define the generalized odd log-logistic normal regression with a dispersion systematic component. We obtain a linear representation, some of its properties, and maximum likelihood estimates. Furthermore, we carry out several simulations for different schemes to evaluate the accuracy of the estimators. The robustness of the new regression model is proved by modeling COVID-19 data. The proposed model explains COVID-19 ICU survival times of the patients in a Brazilian hospital. © 2023.

Ola Distribution: A New One Parameter Model with Applications to Engineering and Covid-19 Data

We employed the notion of mixture distributions to suggest a new one parameter continuous distribution for modeling real lifetime data called Ola Distribution. Its properties are explored including moments and related measures, moment generating function, reliability analysis functions, order statistics, Bonferroni and Lorenz curves, stochastic ordering, Rényi entropy and mean deviations. The maximum likelihood method is adapted to estimate the parameter of the distribution. Applications to engineering and COVID-19 data sets are presented to illustrate the usefulness of the suggested distribution. The applications showed that Ola distribution outperforms some competitive distributions and can be considered as a useful tool for modeling such real data. © 2023.

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.

Technological heterogeneity and time-varying efficiency of sharing accommodation: Evidence from the Canary Islands

The production of services in the accommodation sharing industry is heterogeneous in the sense that listings with different strategic management could adopt distinct technologies. This paper analyses the time-varying efficiency of the peer-to-peer accommodation sector using the input distance stochastic frontier model with random coefficients to accommodate both multi-input and multi-output technology and the technological heterogeneity among listings. An empirical analysis is conducted based on data from Airbnb and HomeAway listings in the Canary Islands (Spain), before, during and after the COVID-19 lockdown (source: AirDNA), in the period January 2019-September 2020 (monthly data). The results show technological heterogeneity between listings and time-varying inefficiency which negatively depends on productivity. Moreover, multi-unit hosts are clearly more efficient than single-unit hosts. A mean efficiency of around 78% during the study period was estimated. © 2023 The Authors

The Maxwell-Burr X Distribution: Its Properties and Applications to the COVID-19 Mortality Rate in Thailand

A novel distribution, the Maxwell-Burr X (M-BX) distribution, was proposed. This distribution was an extension of the Burr X distribution by applying the Maxwell generalized family of distributions. The cumulative distribution function, probability density function, survival function, hazard function and quantile function of the M-BX distribution were defined. Some important properties and the parameters of its estimates were discussed. A simulation study was conducted from the basis of quantile function to ascertain the performance of maximum likelihood estimators. The M-BX distribution were also applied to model two lifetime data sets relating to the COVID-19 mortality rate in Thailand during different periods to express the flexibility of the distribution against other competing distributions. According to information criteria, AIC, CAIC, BIC, and HQIC, the M-BX distribution gave the best fit among all chosen distributions. © 2023, Thai Statistical Association. All rights reserved.