ABSTRACT
Today we find a large audience for Classical Studies online: from podcasts and Twitter feeds to informal reading groups and virtual performance of plays, there is an appetite for information on the ancient world that is often filled by professionals and semi-professionals who move between spheres of conventional training and the enthusiasts' realm (e.g., Reddit). There are still important steps to be made in the curation of texts, the collection of images and archaeological artifacts, the construction of classroom space, the exploration of performance, and the creation of virtual environments. A good example of this is the Ancient Lives project, which followed earlier institutional initiatives like the Advanced Papyrological Information System (APIS) to transcribe and edit papyri.9 It creates a massive and searchable database on the foundation of distributed authorship and distributed institutional cost for storage and bandwidth. Mapping and the use of Geographic Information Systems (GIS) data have been at the forefront of providing new frameworks
ABSTRACT
BACKGROUND: In response to the COVID-19 pandemic, general practice in Australia underwent a rapid transition, including the roll-out of population-wide telehealth, with uncertain impacts on GP use and costs. AIM: To describe how use and costs of GP services changed in 2020 - following the COVID-19 pandemic and introduction of telehealth - compared with 2019, and how this varied across population subgroups. DESIGN AND SETTING: Linked-data analysis of whole-population data for Australia. METHOD: Multi-Agency Data Integration Project data for â¼19 million individuals from the 2016 census were linked to Medicare data for 2019-2020. Regression models were used to compare age- and sex-adjusted GP use and out-of-pocket costs over time, overall, and by sociodemographic characteristics. RESULTS: Of the population, 85.5% visited a GP in Q2-Q4 2020, compared with 89.5% in the same period of 2019. The mean number of face-to-face GP services per quarter declined, while telehealth services increased; overall use of GP services in Q4 2020 was similar to, or higher than, that of Q4 2019 for most groups. The proportion of total GP services by telehealth stabilised at 23.5% in Q4 2020. However, individuals aged 3-14 years, ≥70 years, and those with limited English proficiency used fewer GP services in 2020 compared with 2019, with a lower proportion by telehealth, compared with the rest of the population. Mean out-of-pocket costs per service were lower across all subgroups in 2020 compared with 2019. CONCLUSION: The introduction of widespread telehealth maintained the use of GP services during the COVID-19 pandemic and minimised out-of-pocket costs, but not for all population subgroups.
Subject(s)
COVID-19 , General Practice , Telemedicine , Humans , Australia/epidemiology , COVID-19/epidemiology , National Health Programs , PandemicsABSTRACT
Introduction: One of the ways of coping with COVID-19 concerns aspects related to the production and dissemination of reliable, clear and quickly understood information. There are many communicational and informational actions and initiatives in favor of dissemination and of the means that guarantee the acceptability, adherence and compliance with the prevention and control measures of COVID-19. This research aims to develop a digital environment, understood here as a panel with topics related to COVID-19, based on SPARQL Protocol and RDF Query Language (SPARQL) queries and on the Wikidata dataset. Method: To do so, a theoretical and applied methodology is used, based on the Systematic Literature Review to support the construction of the conceptual corpus underlying the computational technologies from the Semantic Web and Linked Data and its application in the structuring and modeling of the environment, for making scientific data available and sharing. Results: The data collected in the Systematic Literature Review reveal little scientific production available at the international level, however, interesting initiatives are already concerned with the openness and availability of scientific data on the Web. In addition, the information panel on COVID-19 developed is categorized into six main axes, such as Map COVID-19, Symptoms of COVID-19, Possible treatments, Taxonomy, Related works and Related images. Conclusion: Thus, the information panel about COVID-19 presents itself as a digital environment that enhances the visualization, access and sharing of data and information for heterogeneous users, contributing to the transfer of consistent, structured and reliable information, as well as the promotion of public guidelines for controlling the spread of the disease. © 2020 Arakaki, Castro & Arakaki.
ABSTRACT
Purpose: In this article, the authors analyse the impact of the 2020 lockdown and the subsequent measures to contain the spread of COVID-19 in Italy in the hospitality industry by looking at the social demands brought forward by the restaurant sector. Design/methodology/approach: To analyse social demands, the authors choose Twitter as an observation point using two hashtags as keywords to scratch the data: #iononriapro and #ioapro, which correspond to two different instances conveyed by the same subject: the restaurant sector. The instances linked to the hashtags produced different levels of engagement and penetration within the social structure and digital platform. To analyse the first block of data linked to the first hashtag-flag #iononriapro, the authors used content analysis. To analyse the second and third block of data linked to the hashtag-flag #ioapro, the authors used an automatic procedure, emotional text mining. Findings: The analysis procedures allow us to reconstruct the positioning of the topics of closures and reopenings due to lockdown in this sector and to identify two explanatory dimensions: structural and affective, which explain the tension that has emerged between the State and the restaurant sector around COVID-related closures. Originality/value: The study's findings not only contribute to the current understandings of the birth, transformation and penetration of social issues by the restaurant sector over the specific period linked to the COVID-19 pandemic and the measures imposed for its containment but are also valuable to analyse the dynamics through which Twitter hashtags and the social issues they represent find strength or lose interest in the public. © 2022, Emerald Publishing Limited.
ABSTRACT
BackgroundMonitoring differences in COVID-19 vaccination uptake in different groups is crucial to help inform the policy response to the pandemic. A key data gap is the absence of data on uptake by occupation. This study investigates differences in vaccination rates by occupation in England, using nationwide population-level data.MethodsWe calculated the proportion of people who had received three COVID-19 vaccinations (assessed on 28 February 2022) by detailed occupational categories in adults aged 18–64 and estimated adjusted ORs to examine whether these differences were driven by occupation or other factors, such as education. We also examined whether vaccination rates differed by ability to work from home.ResultsOur study population included 15 456 651 adults aged 18–64 years. Vaccination rates differed markedly by occupation, being higher in health professionals (84.7%) and teaching and other educational professionals (83.6%) and lowest in people working in elementary trades and related occupations (57.6%). We found substantial differences in vaccination rates looking at finer occupational groups. Adjusting for other factors likely to be linked to occupation and vaccination, such as education, did not substantially alter the results. Vaccination rates were associated with ability to work from home, the rate being higher in occupations which can be done from home. Many occupations with low vaccination rates also involved contact with the public or with vulnerable peopleConclusionsIncreasing vaccination coverage in occupations with low vaccination rates is crucial to help protecting the public and control infection. Efforts should be made to increase vaccination rates in occupations that cannot be done from home and involve contact with the public.
ABSTRACT
Régime d’exception, banalisation et repli identitaire. : Sophie Breteshé et Sylvain Le Berre Technological Strikebreaking : A Case Study of Québec’s Anti-Scab Legislation : Andrea Talarico The pandemic is continuing, as are its consequences. Perhaps, but there is also a bigger picture — a picture that draws together and consolidates the potential of post-industrial technology, the priorities and aspirations of generation Xers and Ys and, of course, the modern imperative of work-life balance. [...]in this new reality, what happens to collective bargaining and the existing capital/labour power asymmetry?
ABSTRACT
BACKGROUND: With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data. OBJECTIVE: To improve the utilization value of the information and effectively aid researchers to combat COVID-19, we have constructed and successively released a unified linked data set named OpenKG-COVID19, which is one of the largest existing KGs related to COVID-19. OpenKG-COVID19 includes 10 interlinked COVID-19 subgraphs covering the topics of encyclopedia, concept, medical, research, event, health, epidemiology, goods, prevention, and character. METHODS: In this paper, we introduce the key techniques exploited in building COVID-19 KGs in a top-down manner. First, the schema of the modeling process for each KG in OpenKG-COVID19 is described. Second, we propose different methods for extracting knowledge from open government sites, professional texts, public domain-specific sources, and public encyclopedia sites. The curated 10 COVID-19 KGs are further linked together at both the schema and data levels. In addition, we present the naming convention for OpenKG-COVID19. RESULTS: OpenKG-COVID19 has more than 2572 concepts, 329,600 entities, 513 properties, and 2,687,329 facts, and the data set will be updated continuously. Each COVID-19 KG was evaluated, and the average precision was found to be above 93%. We have developed search and browse interfaces and a SPARQL endpoint to improve user access. Possible intelligent applications based on OpenKG-COVID19 for further development are also described. CONCLUSIONS: A KG is useful for intelligent question-answering, semantic searches, recommendation systems, visualization analysis, and decision-making support. Research related to COVID-19, biomedicine, and many other communities can benefit from OpenKG-COVID19. Furthermore, the 10 KGs will be continuously updated to ensure that the public will have access to sufficient and up-to-date knowledge.
ABSTRACT
Semantic Web technology is not new as most of us contemplate;it has evolved over the years. Linked data web terminology is the name set recently to the Semantic Web. Semantic Web is a continuation of Web 2.0, and it is to replace existing technologies. It is built on natural language processing and provides solutions to most of the prevailing issues. Web 3.0 is the version of the Semantic Web that caters to the information needs of half of the population on earth. This paper links two important current concerns, the security of information and enforced online education due to COVID-19 with Semantic Web. The steganography requirement for the Semantic Web is discussed elaborately, even though encryption is applied which is inadequate in providing protection. Web 2.0 issues concerning online education and Semantic Web solutions have been discussed. An extensive literature survey has been conducted related to the architecture of Web 3.0, detailed history of online education, and security architecture. Finally, Semantic Web is here to stay and data hiding along with encryption makes it robust. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
Open government data (OGD) provide an opportunity for developing various services by disclosing information monopolized by the government to the public so that the private sector can use it. The private sector is utilizing this to improve the work efficiency and productivity by collecting, analyzing, and reprocessing OGD for various work steps of a BIM-based design project. However, most studies on OGD focus on the functionality and usability of data portals and the factors for evaluating the data itself such as openness, accountability, and transparency. This study aims to provide an evaluation framework for OGD for the AEC industry to assess the data utilization environment in order to improve the productivity of BIM-based projects. Several OGD principles found within related literature are discussed, and from them we extract evaluation framework levels. Then, we validate the proposed framework by applying it to a case of developing a BIM-based design support system using OGD datasets. This research concludes by suggesting that to effectively utilize OGD in the construction industry, the private sector should simply view data after collecting them, create an institutional environment for creating new values by reprocessing data, and build an active data utilization roadmap based on this environment.
ABSTRACT
Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug targets and indications. To effectively utilize information in published literature, we integrate knowledge graphs and published literature using named entity recognition and normalization before applying a machine learning model that utilizes the combination of graph and literature. We then use supervised machine learning to show the effects of combining features from biomedical knowledge and published literature on the prediction of drug targets and drug indications. We demonstrate that our approach using datasets for drug-target interactions and drug indications is scalable to large graphs and can be used to improve the ranking of targets and indications by exploiting features from either structure or unstructured information alone.
ABSTRACT
The Web of Data, the Internet of Things, and Industry 4.0 are converging, and society is challenged to ensure that appropriate regulatory responses can uphold the rule of law fairly and effectively in this emerging context. The challenge extends beyond merely submitting digital processes to the law. We contend that the 20th century notion of ‘legal order’ alone will not be suitable to produce the social order that the law should bring. The article explores the concepts of rule of law and of legal governance in digital and blockchain environments. We position legal governance from an empirical perspective, i.e., as an explanatory and validation concept to support the implementation of the rule of law in the new digital environments. As a novel contribution, this article (i) progresses some of the work done on the metarule of law and complements the SMART middle-out approach with an inside-out approach to digital regulatory systems and legal compliance models;(ii) sets the state-of-the-art and identifies the way to explain and validate legal information flows and hybrid agents’ behaviour;(iii) describes a phenomenological and historical approach to legal and political forms;and (iv) shows the utility of separating enabling and driving regulatory systems.
ABSTRACT
COVID-19 is one of the dangerous viruses that appears in 2020. The virus has gained popularity with its massive spread across the countries. The number of casualties has increased dramatically, which led many countries to declare a state of emergency as a result of the outbreak of this epidemic and their inability to control it. Several studies and researches have emerged to shed light on the mechanism of the virus and ways to prevent it, making it easier to control in the future. The World Health Organization (WHO) has begun to publish detailed numbers of injuries, deaths, and recovery cases and has given many advices, including the imposition of a total and partial curfew in many areas in addition to emphasizing the principle of social divergence in order to prevent the rapid spread of the virus among groups of society. The main goal of this paper is to design a system that used genetic algorithms (GAs) and the principles of linked open data (LOD) for improving the immunity system by enhancing social divergence. The system starts using GA for the purpose of finding the characteristics that must be present in a person who is dangerous to society in order to get away from him as much as possible. After taking these features, the system will take the values of these features and add it to the features for all persons in order to check it in the future and give alarm to all their friends or people around them. The RDF (Resource Description Framework) is a standard model for data interchange on the Web. The main idea for using RDF in this paper is finding a proper representation for user personal file and give the flexibility to connect many personal files in order to find a deep information and can reach an unknown person from known person using the FOAF (Friend Of A Friend) and vCard (virtual card) as a standard for vocabularies. The system takes the Statistics from the WHO which show the total infected cases in all countries arranged in decreasing order. The system gives a good result for analyzing the COVID-19 virus information and detecting the infected (possible infected) person and send warning to all nearest people and his friend and family, because sometimes the person has no coronavirus symptoms but he is infected so we need a technique for detecting that virus and take a proper action as soon as possible. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
BACKGROUND: defining features of the COVID-19 pandemic in many countries were the tragic extent to which care home residents were affected and the difficulty in preventing the introduction and subsequent spread of infection. Management of risk in care homes requires good evidence on the most important transmission pathways. One hypothesised route at the start of the pandemic, prior to widespread testing, was the transfer of patients from hospitals that were experiencing high levels of nosocomial events. METHODS: we tested the hypothesis that hospital discharge events increased the intensity of care home cases using a national individually linked health record cohort in Wales, UK. We monitored 186,772 hospital discharge events over the period from March to July 2020, tracking individuals to 923 care homes and recording the daily case rate in the homes populated by 15,772 residents. We estimated the risk of an increase in case rates following exposure to a hospital discharge using multi-level hierarchical logistic regression and a novel stochastic Hawkes process outbreak model. FINDINGS: in regression analysis, after adjusting for care home size, we found no significant association between hospital discharge and subsequent increases in care home case numbers (odds ratio: 0.99, 95% CI: 0.82, 1.90). Risk factors for increased cases included care home size, care home resident density and provision of nursing care. Using our outbreak model, we found a significant effect of hospital discharge on the subsequent intensity of cases. However, the effect was small and considerably less than the effect of care home size, suggesting the highest risk of introduction came from interaction with the community. We estimated that approximately 1.8% of hospital discharged patients may have been infected. INTERPRETATION: there is growing evidence in the UK that the risk of transfer of COVID-19 from the high-risk hospital setting to the high-risk care home setting during the early stages of the pandemic was relatively small. Although access to testing was limited to initial symptomatic cases in each care home at this time, our results suggest that reduced numbers of discharges, selection of patients and action taken within care homes following transfer all may have contributed to the mitigation. The precise key transmission routes from the community remain to be quantified.
Subject(s)
COVID-19 , COVID-19/epidemiology , Hospitals , Humans , Nursing Homes , Pandemics/prevention & control , Patient Discharge , United Kingdom/epidemiologyABSTRACT
The unprecedented mobilization of scientists caused by the COVID-19 pandemic has generated an enormous number of scholarly articles that are impossible for a human being to keep track of and explore without appropriate tool support. In this context, we created the Covid-on-the-Web project, which aims to assist the accessing, querying, and sense-making of COVID-19-related literature by combining efforts from the semantic web, natural language processing, and visualization fields. In particular, in this paper we present an RDF data set (a linked version of the "COVID-19 Open Research Dataset" (CORD-19), enriched via entity linking and argument mining) and the "Linked Data Visualizer" (LDViz), which assists the querying and visual exploration of the referred data set. The LDViz tool assists in the exploration of different views of the data by combining a querying management interface, which enables the definition of meaningful subsets of data through SPARQL queries, and a visualization interface based on a set of six visualization techniques integrated in a chained visualization concept, which also supports the tracking of provenance information. We demonstrate the potential of our approach to assist biomedical researchers in solving domain-related tasks, as well as to perform exploratory analyses through use case scenarios.
ABSTRACT
Facing many challenges of division in all aspects (social distancing, political and social divisions, remote work environments), University of South Florida Libraries took the lead in exploring how to overcome these various separations by providing access to its high-quality information sources to its local community and beyond. USFs purchases were informed by work at other institutions, such as the University of Minnesotas antiracism reading lists, which has in turn grown into a rich resource that includes other valuable resources like the Mapping Prejudice Project and a link to the Umbra Search.2 The Triad Black Lives Matter Protest Collection at the University of North Carolina Greensboro is another example of a cultural institution reacting swiftly to document, preserve, and educate.3 These new pages and lists being generated by libraries and cultural institutions seem to be curated by hand using tools that require human intervention to make them and keep them up to date. Umbra Search is a tool that aggregates content from more than 1,000 libraries, archives, and museums.4 It is also supported by high-profile grants from the Institute of Museum and Library Services, the Doris Duke Charitable Foundation, and the Council on Library and Information Resources. Despite enthusiasm from libraries and other cultural institutions, new purchases and curated content are not going to reach the world as fully as hoped. [...]libraries adopt open data formats in favor of locking away content in closed records like MARC, library and digital content will remain siloed from the internet.
ABSTRACT
BACKGROUND: Care home residents have complex healthcare needs but may have faced barriers to accessing hospital treatment during the first wave of the COVID-19 pandemic. OBJECTIVES: To examine trends in the number of hospital admissions for care home residents during the first months of the COVID-19 outbreak. METHODS: Retrospective analysis of a national linked dataset on hospital admissions for residential and nursing home residents in England (257,843 residents, 45% in nursing homes) between 20 January 2020 and 28 June 2020, compared to admissions during the corresponding period in 2019 (252,432 residents, 45% in nursing homes). Elective and emergency admission rates, normalised to the time spent in care homes across all residents, were derived across the first three months of the pandemic between 1 March and 31 May 2020 and primary admission reasons for this period were compared across years. RESULTS: Hospital admission rates rapidly declined during early March 2020 and remained substantially lower than in 2019 until the end of June. Between March and May, 2,960 admissions from residential homes (16.2%) and 3,295 admissions from nursing homes (23.7%) were for suspected or confirmed COVID-19. Rates of other emergency admissions decreased by 36% for residential and by 38% for nursing home residents (13,191 fewer admissions in total). Emergency admissions for acute coronary syndromes fell by 43% and 29% (105 fewer admission) and emergency admissions for stroke fell by 17% and 25% (128 fewer admissions) for residential and nursing home residents, respectively. Elective admission rates declined by 64% for residential and by 61% for nursing home residents (3,762 fewer admissions). CONCLUSIONS: This is the first study showing that care home residents' hospital use declined during the first wave of COVID-19, potentially resulting in substantial unmet health need that will need to be addressed alongside ongoing pressures from COVID-19.
ABSTRACT
BACKGROUND: Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a "commons." Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases. However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modeled with entity schemas represented by Shape Expressions. RESULTS: As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable is demonstrated by integrating data from NCBI (National Center for Biotechnology Information) Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates. CONCLUSIONS: Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, human coronavirus NL63, human coronavirus 229E, human coronavirus HKU1, human coronavirus OC4).
Subject(s)
COVID-19/pathology , Genomics/methods , Knowledge Bases , Proteomics/methods , SARS-CoV-2/physiology , COVID-19/metabolism , COVID-19/virology , Coronavirus/genetics , Coronavirus/physiology , Coronavirus Infections/metabolism , Coronavirus Infections/pathology , Coronavirus Infections/virology , Genome, Viral , Humans , Internet , Pandemics , SARS-CoV-2/genetics , Viral Proteins/genetics , Viral Proteins/metabolism , WorkflowABSTRACT
BACKGROUND: Few studies of the healthy immigrant effect (HIE) have examined the mental health outcomes of Canadian-born individuals on a national scale compared with immigrants by admission category. This study fills this gap by examining the self-reported mental health (SRMH) of immigrants by admission category and other immigration dimensions (e.g., source world region and duration since landing) and making comparisons with Canadian-born respondents to a population-based survey. DATA AND METHODS: Based on four cycles (2011 to 2014) of the Canadian Community Health Survey (CCHS) linked to the Longitudinal Immigration Database (IMDB), odds ratios of high (i.e., excellent or very good) SRMH among Canadian-born respondents and IMDB-linked immigrants are compared using logistic regression. Among the IMDB immigrant population, high SRMH was also examined according to the above-mentioned immigration dimensions. Adjusted results were hierarchically controlled for age, sex, social and economic factors, and sense of belonging. RESULTS: Age-sex adjusted results show that immigrants, especially refugees, are less likely than the Canadian-born population to report high mental health levels, but these differences disappeared after full adjustment. The odds of immigrants having high SRMH differed more by source world region and duration since landing. For example, fully adjusted results show support for the HIE, with recent immigrants (interviewed within 10 years of landing) more likely to report high SRMH than either the Canadian-born population or established immigrants. Greater odds of high SRMH among recent immigrants also holds across admission classes and for selected world regions. DISCUSSION: This study provides new evidence on differences in mental health between Canadian-born individuals and immigrants by various characteristics. Results support a deterioration of the HIE in SRMH and identify factors significantly associated with SRMH. This study can also serve as a baseline for further studies on the impact of COVID-19 on immigrants' mental health by immigrant category.