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PurposeA proliferation of articles surrounding the COVID-19 pandemic is calling for new insights through review. This paper aims to bibliometrically analyze the current progress of research around hospitality and tourism to define the research directions on herd immunity and the prevention of disease under the "new normal.”Design/methodology/approachThis paper analyzed 326 articles regarding COVID-19 published in SSCI hospitality, leisure and tourism journals in 2020 and 2021 by combining manual analysis and bibliometrics to reveal research topics and to gain insight into research structures.FindingsThe results of this paper summarized topics related to stakeholders' mentality and behavior, responses of travel suppliers to the COVID-19 pandemic, economic impact and demand forecasting, social issues of human rights and racism and reflection on tourism and transformation of the industry. More research is called for in the future to focus on a better response to the crisis, including crisis management education and training and the improving the resilience of small- and medium-sized enterprises.Research limitations/implicationsA three-dimensional consideration was proposed to promote the sustainable development of hospitality and tourism.Originality/valueIn the "new normal” phase of herd immunity and disease prevention, to the best of the authors' knowledge, this is the first paper that provides an up-to-date systematic overview of the evolution of COVID-19 research in tourism and hospitality and encourages more conceptual, practical and futuristic studies.
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Traditional villages are a valuable cultural asset that occupy an important position in Chinese traditional culture. This study focuses on 206 traditional villages in Hebei Province and aims to explore their spatial distribution characteristics and influencing factors using ArcGIS spatial analysis. The analysis shows that traditional villages in Hebei Province were distributed in clusters during different historical periods, and eventually formed three core clusters in Shijiazhuang, Zhangjiakou and Xingtai-Handan after different historical periods. Moreover, the overall distribution of traditional villages in Hebei Province is very uneven, with clear regional differences, and most of them are concentrated in the eastern foothills of the Taihang Mountains. To identify the factors influencing traditional villages, natural environmental factors, socio-economic factors, and historical and cultural factors are considered. The study finds that socio-economic and natural environmental factors alternate in the spatial distribution of traditional villages in Hebei Province. The influence of the interaction of these factors increases significantly, and socio-economic factors have a stronger influence on the spatial distribution. Specifically, the spatial distribution of traditional villages in Hebei Province is influenced by natural environmental factors, while socio-economic factors act as drivers of spatial distribution. Historical and cultural factors act as catalysts of spatial distribution, and policy directions are external forces of spatial distribution. Overall, this study provides valuable insights into the spatial distribution characteristics and influencing factors of traditional villages in Hebei Province, which can be used to develop effective strategies for rural revitalisation in China.
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BACKGROUND: New York State (NYS) is the 27th largest state and the 4th most populous state in the U.S., with close to 20 million people in 62 counties. Territories with diverse populations present the best opportunity to study health outcomes and associated covariates, and how these differ across different populations and groups. The County Health Ranking and Roadmaps (CHR&R) ranks counties by linking the population's characteristics and health outcomes and contextual factors in a synchronic approach. METHODS: The goal of this study is to analyze the longitudinal trends in NYS counties of age-adjusted premature mortality rate and years of potential life loss rate (YPLL) from 2011-2020 using (CHR&R) data to identify similarities and trends among the counties of the state. This study used a weighted mixed regression model to analyze the longitudinal trend in health outcomes as a function of the time-varying covariates and clustered the 62 counties according to the trend over time in the covariates. RESULTS: Four clusters of counties were identified. Cluster 1, which represents 33 of the 62 counties in NYS, contains the most rural counties and the least racially and ethnically diverse counties. Clusters 2 and 3 mirror each other in most covariates and Cluster 4 is comprised of 3 counties (Bronx, Kings/Brooklyn, Queens) representing the most urban and racial and ethnic diverse counties in the state. CONCLUSION: The analysis clustered counties according to the longitudinal trends of the covariates, and by doing so identified clusters of counties that shared similar trends among the covariates, to later examine trends in the health outcomes through a regression model. The strength of this approach lies in the predictive feature of what is to come for the counties by understanding the covariates and setting prevention goals.
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Mortality, Premature , Rural Population , Humans , United States , New York/epidemiologyABSTRACT
BACKGROUND: Formation of GP clusters began in Scotland in April 2016 as part of a new Scottish GP contract. They aim to improve the care quality for local populations (intrinsic role) and the integration of health and social care (extrinsic role). AIM: To compare predicted challenges of cluster implementation in 2016 with reported challenges in 2021. DESIGN & SETTING: Qualitative study of senior national stakeholders in primary care in Scotland. METHOD: Qualitative analysis of semi-structured interviews with 12 senior primary care national stakeholders in 2016 (n = 6) and 2021 (n = 6). RESULTS: Predicted challenges in 2016 included balancing intrinsic and extrinsic roles, providing sufficient support, maintaining motivation and direction, and avoiding variation between clusters. Progress of clusters in 2021 was perceived as suboptimal and was reported to vary significantly across the country, reflecting differences in local infrastructure. Practical facilitation (data, administrative support, training, project improvement support, and funded time) and strategic guidance from the Scottish Government was felt to be lacking. GP engagement with clusters was felt to be hindered by the significant time and workforce pressures facing primary care. These barriers were considered as collectively contributing to cluster lead 'burnout' and loss of momentum, exacerbated by inadequate opportunities for shared learning between clusters across Scotland. Such barriers preceded, but were perpetuated by, the impact of the COVID-19 pandemic. CONCLUSION: Apart from the COVID-19 pandemic, many of the challenges reported by stakeholders in 2021 were predicted in 2016. Accelerating progress in cluster working will require renewed investment and support applied consistently across the country.
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Objective To identify the pathogen and track the genetic source of a cluster of cases with fever in a kindergarten in Fengtai district during the normalization of COVID-19 prevention and control in Beijing.Methods A descriptive analysis method was used to investigate this cluster of cases with fever in April 2021.Pharyngeal swabs were collected and viral nucleic acid was extracted, real-time PCR was performed to identify SARS-CoV-2 and other common respiratory virus. G gene of human metapneumovirus（hMPV） was amplified by RT-PCR and was then sequenced. BioEdit was used for G gene sequence analysis and the Neighbor-Joining model in MEGA 5. 0 software was used to construct the phylogenic tree of G gene. Results A total of 16 cases were reported in one class with the incidence of 53. 3%（16/30） during 8 days of a cluster outbreak. All pharyngeal swabs collected from 12 cases were tested SARS-CoV-2 negative, six were found to be hMPV positive by multiplex-PCR, and one was positive for both human adenovirus and hMPV. Full-length sequences of G genes were obtained from 2 strains of hMPV. Sequence analysis showed that both strains were hMPV B2 and the nucleic acid homology of G gene was 96. 73%-98. 01% with strains from Japan（LC337940, LC337935, LC1922349） in 2016 and over 98. 40%with strains from Shandong（OL625642, OL625644） in 2019, Henan MN944096 in 2019.Compared with the amino acid sequence of hMPV-B2 reference strain（AY297748）, six amino acid insertions containing EKEKEK were identified between 161-166 amino acid location and N-glycosylation of G protein analysis showed that the two strains had four N-glycosylation sites. Conclusions The leading pathogen for this cluster outbreak is found to be hMPV-B2, which are highly homologous with strains from Japan, Shandong and Henan. Therefore, a non-stop surveillance of hMPV is necessary during the normalization control and prevention period for COVID-19.
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As COVID-19 has been prevalent around the world in recent years, many studies about monitoring and predicting the spread of disease have been conducted in various fields including geography. However, little research has been devoted to infectious disease prediction modeling that adopts constantly changing travel behavior patterns during epidemics. This is due to the limited methodologies to investigate spatio-temporal change in travel behaviors at large-scale and the difficulty in interpreting massive and diverse travel patterns. This study suggests an effective disease surveillance method based on cluster analysis to identify change in travel behaviors during the pandemic by implementing space-time cluster analysis. The results show that K-means++ well represent dynamic changes in travel behaviors at daily scale, whereas retrospective space-time scan statistics have the advantage of detecting travel behavior changes in each period at large spatial scale. Those results could inform decision makers to establish guidelines on travel behavior to curb individual contacts under potential future pandemic. © 2023 Korean Society of Surveying. All rights reserved.
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Through fiscal policy, the government can influence businesses and individuals in order to regulate their behaviour. The research used panel data from all 27 EU countries covering the period 2008–2020 to investigate the impact of direct taxation on economic growth at the level of two main clusters of countries concerning fiscal efficiency. Therefore, the analysis employed cluster methods to classify the main EU countries in both groups of countries with a high level of fiscal efficiency and those with a rather limited level of fiscal efficiency. The study employs fixed effect models and dynamic GMM methods to investigate the effect of direct taxation components (personal and corporate income taxes) on economic growth. The analysis also considers the informal economy's role in relation to the official economy. The empirical results revealed that corporate income taxes significantly negatively impact economic growth for both clusters of high- and limited fiscal efficiency countries. Additionally, personal income tax was associated with lower economic growth for countries in the limited fiscal efficiency group. Thus, from the perspective of policymakers, lowering direct taxation can increase disposable income, stimulate consumption and economic growth, encourage investment leading to job creation, increase competitiveness, and reduce tax evasion and avoidance, thereby leading to a more efficient tax system.
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BACKGROUND: The first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors. METHODS: COVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian. RESULTS: The risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR = 4.16; 95% Credible Interval: 4.05-4.27), being on oxygen (aOR = 1.49; 95% Credible Interval: 1.46-1.51) and on invasive mechanical ventilation (aOR = 3.74; 95% Credible Interval: 3.61-3.87). Being admitted in a public hospital (aOR = 3.16; 95% Credible Interval: 3.10-3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts. CONCLUSION: The results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts.
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COVID-19 , Humans , Bayes Theorem , Hospitalization , Hospitals , SARS-CoV-2 , South Africa/epidemiologyABSTRACT
A novel coronavirus infection was described in 2019 in Wuhan, China. From the first months of the spread of the infection around the world, evidence began to appear that patients after recovery had various symptoms. Duration, intensity, and variability of symptoms vary among patients and are often not associated with the severity of the most acute illness. Recently the concept of post-COVID syndrome (postCOVID or long-COVID in the English-language literature) has acquired increasingly clear diagnostic criteria. Persistent symptoms and / or the appearance of delayed complications after 4 weeks or more from the onset of symptoms of an acute illness are commonly called post-COVID syndrome. The wide range of symptoms that can occur in patients with post-COVID syndrome is now a major health concern worldwide. A proper clinical evaluation will help determine the etiology and build a treatment plan. Longer studies aimed at identifying the effects of COVID-19, possible risk factors for their development, a detailed study of the pathogenetic mechanisms of SARS-CoV-2, and the development of treatment and rehabilitation methods to improve the mental and physical health of surviving patients are relevant elements of study for the foreseeable future. T-lymphocytes are a poorly studied population of T lymphocytes. These cells are more often localized in the mucous membranes of the body which have the properties of innate and acquired immunity. The main biological functions are cytolysis, immunoregulation which indicates an important immunocompetent role of this type of cell population in severe infectious diseases. This article provides information on the fraction of T-lymphocytes during the formation of adaptive immunity in patients with post-COVID syndrome.
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Researchers from the Global North and South have collaborated for decades to conduct cutting-edge interdisciplinary research. The tools they used to manage their research projects remained virtually unchanged until the COVID-19 pandemic outbreak early in 2020. Since then, a lot has changed, including the nature and dynamics of research collaboration. How are researchers and project managers adapting to this change? Survey data, semi-structured interviews, and personal accounts from 102 respondents from 33 countries are used in this empirical study to investigate the impact of the pandemic on research collaboration projects. An independent non-parametric t-test revealed a significant difference in the research collaboration infrastructure and home environment between Global North and global South collaborators. The findings have practical implications for people involved in collaborative research projects, funding agencies, project management professionals, and universities interested in pursuing or sustaining North-South research collaboration during the pandemic.
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Gold (Au) nanoclusters (NCs) are novel materials with low cytotoxicity and high chemical stability. These properties are in high demand during the bioimaging. Moreover, the optical properties of gold clusters allow to use them as colorimetric and luminescent bionanosensors. Pterins are low molecular weight organic compounds, which are used in medicine as biomarkers of phenylketonuria, vitiligo, inflammation and immune system activation, cancer, COVID-19, etc. We have investigated the possibility of gold nanosensors usage to detect pterin (Ptr). Ptr-Aunq structures (n = 1–6;q = 0–2) Gibbs energy of complexation (Eb) have been obtained using density functional theory. The highest Eb was determined for the complexes of Au62+ and Au32+ in acidic and alkaline aqueous solution, respectively. The detection of pterin with gold clusters seems to be prospective using both colorimetric and fluorescent detection because of the intense S0→S1 transition in the absorption spectrum of the Au5+ complex. Raman detection of pterin should be performed at alkaline pH because of the dramatic changes in the spectrum of Ptr−1 upon the addition of Au clusters. We believe that these tunable changes of the pterin spectra due to Au clusters and nanoparticles attachment could be exploited in further studies on nanosensor design. © 2023
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The COVID-19 pandemic has caused the society lockdowns and a large number of deaths in many countries. Potential transmission cluster discovery is to find all suspected users with infections, which is greatly needed to fast discover virus transmission chains so as to prevent an outbreak of COVID-19 as early as possible. In this article, we study the problem of potential transmission cluster discovery based on the spatio-temporal logs. Given a query of patient user q and a timestamp of confirmed infection tq, the problem is to find all potential infected users who have close social contacts to user q before time tq. We motivate and formulate the potential transmission cluster model, equipped with a detailed analysis of transmission cluster property and particular model usability. To identify potential clusters, one straightforward method is to compute all close contacts on-the-fly, which is simple but inefficient caused by scanning spatio-temporal logs many times. To accelerate the efficiency, we propose two indexing algorithms by constructing a multigraph index and an advanced BCG-index. Leveraging two well-designed techniques of spatio-temporal compression and graph partition on bipartite contact graphs, our BCG-index approach achieves a good balance of index construction and online query processing to fast discover potential transmission cluster. We theoretically analyze and compare the algorithm complexity of three proposed approaches. Extensive experiments on real-world check-in datasets and COVID-19 confirmed cases in the United States validate the effectiveness and efficiency of our potential transmission cluster model and algorithms. © 2023 Association for Computing Machinery.
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The pandemic spread of African swine fever (ASF) has caused serious effects on the global pig industry. Virus genome sequencing and genomic epidemiology analysis play an important role in tracking the outbreaks of the disease and tracing the transmission of the virus. Here we obtained the full-length genome sequence of African swine fever virus (ASFV) in the first outbreak of ASF in China on August 3rd, 2018 and compared it with other published genotype II ASFV genomes including 9 genomes collected in China from September 2018 to October 2020. Phylogenetic analysis on genomic sequences revealed that genotype II ASFV has evolved into different genetic clusters with temporal and spatial correlation since being introduced into Europe and then Asia. There was a strong support for the monophyletic grouping of all the ASFV genome sequences from China and other Asian countries, which shared a common ancestor with those from the Central or Eastern Europe. An evolutionary rate of 1.312 × 10−5 nucleotide substitutions per site per year was estimated for genotype II ASFV genomes. Eight single nucleotide variations which located in MGF110-1L, MGF110-7L, MGF360-10L, MGF505-5R, MGF505-9R, K145R, NP419L, and I267L were identified as anchor mutations that defined genetic clusters of genotype II ASFV in Europe and Asia. This study expanded our knowledge of the molecular epidemiology of ASFV and provided valuable information for effective control of the disease.
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PurposeUsing science mapping analysis approach and co-word analysis, the present study explores and visualizes research fields and thematic evolution of the coronavirus. Based on this method, one can get a picture of the real content of the themes in the mentioned thematic area and identify the main minor and emerging themes.Design/methodology/approachThis study was conducted based on co-word science mapping analysis under a longitudinal study (from 1988 to 2020). The collection of documents in this study was further divided into three subperiods: 1988–1998, 1999–2009 and 2010–2020. In order to perform science mapping analysis based on co-word bibliographic networks, SciMAT was utilized as a bibliometric tool. Moreover, WoS, PubMed and Scopus bibliographic databases were used to download all records.FindingsIn this study, strategic diagrams were demonstrated for the coronavirus research for a chronological period to assess the most relevant themes. Each diagram depended on the sum of documents linked to each research topic. In the first period (1988–1998), the most centralizations were on virology and evaluation of coronavirus structure and its structural and nonstructural proteins. In the second period (1999–2009), with due attention to high population density in eastern Asia and the increasing number of people affected with the new generation of coronavirus (named severe acute respiratory syndrome virus or SARS virus), publications have been concentrated on "antiviral activity.” In the third period (2010–2020), there was a tendency to investigate clinical syndromes, and most of the publications and citations were about hot topics like "severe acute respiratory syndrome,” "coronavirus” and "respiratory tract disease.” Scientometric analysis of the field of coronavirus can be regarded as a roadmap for future research and policymaking in this important area.Originality/valueThe originality of this research can be considered in two ways. First, the strategic diagrams of coronavirus are drawn in four thematic areas including motor cluster, basic and transversal cluster, highly developed cluster and emerging and declining cluster. Second, COVID-19 is mentioned as a hot topic of research.
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PurposeThis paper discloses the risk management response strategies and the perceived effectiveness of the strategies employed by companies operating within manufacturing clusters in Puerto Rico from 2016 until 2020, the second year of the coronavirus disease 2019 (COVID-19) pandemic.Design/methodology/approachThe research design consists of questionnaire-based survey responses from companies belonging to manufacturing clusters, followed by semi-structured interviews and secondary sources of information.FindingsThe results reveal the risk responses used to manage specific risk types. Albeit respondents' dependency on an assortment of company-centric and cluster-bound risk response strategies, the perception is that the former is more effective when adequate local sources are available and the latter when the cluster has strong interconnectedness among the cluster's members.Research limitations/implicationsFurthermore, there is a generalized belief that long-term cluster-bound strategies are required to complement individual companies' overall risk management strategies.Originality/valueThis paper demonstrated that due to the volatility, uncertainty, complexity and ambiguity (VUCA) nature of the Caribbean region, mixed risk management might result in better and more favorable long-term performance.
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The United Nations 2030 Agenda recognized the importance of focusing on cities to achieve sustainable development goals. The COVID-19 pandemic reaffirmed the need to consider spatial variables when analyzing the impact of a risk or epidemic. Many studies have assessed the impact of this pandemic on countries and its connection with numerous population-related factors, such as vulnerability and resilience. However, there have been less spatial analyses at an urban and neighborhood scale, also considering time as a variable. In spite that, some researchers have recently shown how the patterns of the pandemic evolution is changing in time. We performed a case study in Malaga (Spain) using a tempo-spatial analysis with the purpose of going as deep as possible into the micro-scale of the pandemic impacts, without leaving anyone behind. The micro-level research using composite indexes and cluster analysis clarify the living conditions of people. The results show some patterns of the spatial segregation in the neighborhoods that could better oriented integrated policies and good governance in the recovery process. [ FROM AUTHOR] Copyright of Journal of Urban Affairs 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.)
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We described the frequency of residential case clusters and the efficiency of compulsory testing in identifying cases using buildings targeted in compulsory testing and locally infected COVID-19 cases matched by residence in Hong Kong. Most of the buildings (4246/7688, 55.2%) with COVID-19 cases identified had only one reported case and 13% of the daily reported cases were detected through compulsory testing. Compulsory testing notices could be essential in attempting to eliminate infections ('zero covid') and impactful early in an epidemic but appears to be relatively inefficient in response to sustained community transmission.
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Inferring the heterogeneous connection pattern of a networked system of multivariate time series observations is a key issue. In finance, the topological structure of financial connectedness in a network of assets can be a central tool for risk measurement. Against this, we propose a topological framework for variance decomposition analysis of multivariate time series in time and frequency domains. We build on the network representation of time–frequency generalized forecast error variance decomposition (GFEVD), and design a method to partition its maximal spanning tree into two components: (a) superhighways, i.e. the infinite incipient percolation cluster, for which nodes with high centrality dominate;(b) roads, for which low centrality nodes dominate. We apply our method to study the topology of shock transmission networks across cryptocurrency, carbon emission and energy prices. Results show that the topologies of short and long run shock transmission networks are starkly different, and that superhighways and roads considerably vary over time. We further document increased spillovers across the markets in the aftermath of the COVID-19 outbreak, as well as the absence of strong direct linkages between cryptocurrency and carbon markets. © 2023 Elsevier B.V.
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We propose a novel approach for modeling capture-recapture (CR) data on open populations that exhibit temporary emigration, while also accounting for individual heterogeneity to allow for differences in visit patterns and capture probabilities between individuals. Our modeling approach combines changepoint processes—fitted using an adaptive approach—for inferring individual visits, with Bayesian mixture modeling—fitted using a nonparametric approach—for identifying clusters of individuals with similar visit patterns or capture probabilities. The proposed method is extremely flexible as it can be applied to any CR dataset and is not reliant upon specialized sampling schemes, such as Pollock's robust design. We fit the new model to motivating data on salmon anglers collected annually at the Gaula river in Norway. Our results when analyzing data from the 2017, 2018, and 2019 seasons reveal two clusters of anglers—consistent across years—with substantially different visit patterns. Most anglers are allocated to the "occasional visitors” cluster, making infrequent and shorter visits with mean total length of stay at the river of around seven days, whereas there also exists a small cluster of "super visitors,” with regular and longer visits, with mean total length of stay of around 30 days in a season. Our estimate of the probability of catching salmon whilst at the river is more than three times higher than that obtained when using a model that does not account for temporary emigration, giving us a better understanding of the impact of fishing at the river. Finally, we discuss the effect of the COVID-19 pandemic on the angling population by modeling data from the 2020 season. Supplementary materials for this article are available online.
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PurposeThis study aims to examine the commonalities and contrasts in the tourism objectives and strategies of the Gulf Cooperation Council (GCC) countries, thereby revealing the foundations and pillars underlying the strategic initiatives implemented.Design/methodology/approachThis exploratory study applied the resource-based view (RBV) lens to the country level. A comparative analysis of the tourism strategy in each GCC country was undertaken to determine commonalities and contrasts between the different strategies and to classify them into clusters. In the next stage, in-depth interviews were carried out to validate the findings.FindingsFour distinct clusters were identified in this paper: lifestyle glamor, emerging giant, sports-induced country branding and eco-friendly regional-based strategies.Practical implicationsThe findings and cluster classification are pertinent to policymakers and industry marketers in crafting tourism development plans.Originality/valueThis study analyzed the economic diversification strategies adopted by GCC countries as new entrants in the tourism sector and classified them into four clusters.