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The article focuses on the need for standardized data collection, interpretation, and reporting in the management of Long COVID. Topics include the lack of a consistent definition for Long COVID, the benefits of adopting a data-driven framework for Long COVID management, and the challenges and strategies of data sharing in Long COVID research.
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According to the job's website Recruiter, clinical data management vacancies have increased by almost 94% since 2019.' Artificial intelligence (AI), including machine learning (ML) and natural language processing (NLP), is allowing for the more intelligent analysis of trial data. New approaches, new roles Centralized statistical monitoring, which streamlines drug development by allowing for the near real-time analysis of data as it accumulates, is the perfect example of how the changing research paradigm is creating new roles.
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Percentage of body fat, age, weight, height, and 14 circumference measurements (e.g., waist) are given for 184 women aged 18–25. Body fat, one measure of health, was accurately determined by an underwater weighing technique which requires special equipment and training of the individuals conducting the process. Modeling body fat percentage using multiple regression provides a convenient method of estimating body fat percentage using measures collected using only a measuring tape and a scale. This dataset can be used to show students the utility of multiple regression and to provide practice in model building.
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Although statistical literacy has become a key competence in today's data-driven society, it is usually not a part of statistics education. To address this issue, we propose an innovative concept for a conference-like seminar on the topic of statistical literacy. This seminar draws attention to the relevance and importance of statistical literacy, and moreover, students are made aware of the process of science communication and are introduced to the peer review process for the assessment of scientific papers. In the summer term 2020, the seminar was conducted as a joint project by the University of Hamburg, the University of Muenster, and the Joachim Herz Foundation. In this article, we present the concept of the seminar and our experience with this concept in the summer term 2020.
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Text matching tools employed to detect plagiarism are widely used in universities, but their availability may have pushed students to find ways to evade detection. One such method is the use of automatic paraphrasing software, where assignments can be rewritten with little effort required by students. This paper uses the search engine analytics methodology with data from SEMrush and Google Trends to estimate the level of interest in online automatic paraphrasing tools, focusing on the period 2016 to 2020 and the four countries: the USA, UK, Canada and Australia. The results show a concerning trend, with the number of searches for such tools growing during the period, especially during COVID-19, and notable increases observed during the months where assessment periods take place in universities. The method employed in this study opens up a new avenue of analysis to enrich and supplement the existing knowledge in the field of academic integrity research. The data obtained demonstrates that faculty should be alert for student use of automatic paraphrasing tools and that academic integrity interventions need to be in place across the sector to address this problem. © 2023 Tabriz University of Medical Sciences. All rights reserved.
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An increased focus on patient engagement and the 21st Century Cures Act, regulatory authorities, i.e., the FDA, are embracing the need for more patient-centric drug development and wider access to assure accurate data collection as trials become more decentralized. The focus on improved visibility and oversight of data collection, faster trial implementation, sharing of real-time data and patient comfort and collaboration has led to a variety of eClinical applications. ePRO and other eCOA approaches can transform trials to make them more pragmatic, patient-centric and efficient by maximizing the potential to quickly access data through electronic health records, and especially to assist trial managers to make reliable data-driven decisions, and to mitigate risks. In the area of event prediction, a trial manager can look into a company's historical clinical trial data and provide data guidance when for example, writing new protocols, i.e., for dosages that may need to be increased/ reduced for trials in different geographic areas or age groups.
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COVID-19 creates unique challenges for medical monitors The clinical research community is experiencing unprecedented challenges when it comes to treating COVID-19 and its associated symptoms, particularly tracking the impact of offlabel use of existing medicines, repurposed drugs, new standard of care protocols, and new therapies under investigation. To protect patients' safety throughout the course of a study in this environment, the role of medical monitoring should be redefined from an analog process perspective that heavily relies on reviewing spreadsheets, to utilizing modern review methodologies that focus on data science. To adapt to this need, medical monitoring needs to move from data handling in silos using old-school methods to new methods that enable the synchronous interaction of data monitors with stakeholders who handle data.
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This book explores how digital technologies have proved to be a useful and necessary tool to help ensure that local and regional governments on the frontline of the emergency can continue to provide essential public services during the COVID-19 crisis. Indeed, as the demand for digital technologies grows, local and regional governments are increasingly committed to improving the lives of their citizens under the principles of privacy, freedom of expression and democracy. The Digital Revolution began between the late 1950s and 1970s and represents the evolution of technology from the mechanical and analog to the digital. The advent of digital technology has also changed how humans communicate today using computers, smartphones and the internet. Further, the digital revolution has made a tremendous wealth of information accessible to virtually everyone. In turn, the book focuses on key challenges for local and regional governments concerning digital technologies during this crisis, e.g. the balance between privacy and security, the digital divide, and accessibility. Privacy is a challenge in the mitigation of COVID-19, as governments rely on digital technologies like contact-tracking apps and big data to help trace peoples patterns and movements. While these methods are controversial and may infringe on rights to privacy, they also appear to be effective measures for rapidly controlling and limiting the spread of the virus. Next, the book discusses the 10 technology trends that can help build a resilient society, as well as their effects on how we do business, how we work, how we produce goods, how we learn, how we seek medical services and how we entertain ourselves. Lastly, the book addresses a range of diversified technologies, e.g. Online Shopping and Robot Deliveries, Digital and Contactless Payments, Remote Work, Distance Learning, Telehealth, Online Entertainment, Supply Chain 4.0, 3D Printing, Robotics and Drones, 5G, and Information and Communications Technology (ICT). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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[...]prior to the COVID-19 pandemic, which could be described as a waning period, nurses expressed a high level of concern about safe nurse staffing levels, a shortage of nurses, and the quality and safety of patient care. The movement sought a new, more balanced view of nurses' impact on patients and healthcare by stimulating the shift to a state that considers the costs and quality of care simultaneously, and, relative to the nursing workforce, appreciates the value of nurses' contributions to and impact on healthcare and society. [...]these authors call for a realignment of systems and structures within nursing education, practice, and research to build competencies and confidence for nurses to advocate not only for patients, the profession, and the healthcare sector, but most importantly to serve as agents of change for better health of our nation and planet (Oiemeni et al„ 2023). Discussion To put the work of these commissioned papers in context, we draw on the work of Kellerman and Seligman (2023). who recently offered a new typology for creative thinking: * Integration to demonstrate the similarities of different objects or entities that appear different;* Splitting, or teasing apart objects or entities that appear similar to view the differences;* Figure-ground reversal, or appreciating that elements or components of objects or entities deemed essential actually may be hidden, or in the background, rather than superficial or in the foreground;and * Distal thinking, or the imagining of objects and entities as being very different from their present state.
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The UK online puppy trade has rapidly outgrown the current legislation, aided by the anonymity provided by classified advertisement platforms. In an effort to meet increased demand, some unregulated and regulated breeders may have employed practices that negatively impact canine welfare. A paucity of up-to-date empirical data, necessary to characterise the scale and nature of this industry, makes intervention challenging. This study quantifies the online puppy trade via web-scraped online classified advertisements, providing empirical data that reveal market trends, along with spatial and temporal patterns. A total of 17,389 unique dog advertisements were collated and analysed over a 2-year period (1 June 2018 to 31 May 2020). The second year included the COVID-19 Lockdown (23 March 2020 to 31 May 2020). Statistical comparisons between dependent and independent variables were performed by linear regression. In the case of a single continuous variable, a one sample t-test was used. Of these advertisements, 57.2% were sourced from a pet-specific classified advertisement website (Pets4Homes, n = 9948), and the remaining 42.8% from two general classified advertisement websites (Gumtree, n = 7149, 41.1%; Preloved, n = 292, 1.7%, respectively). England exhibited the greatest number of advertisements (n = 10,493), followed by Wales (n = 1566), Scotland (n = 975), and Northern Ireland (NI; n = 344). Scaled for estimated human population density, Wales possessed as many advertisements per million inhabitants (489.4) as the other three combined (England = 186.4, Scotland = 177.3, and NI = 181.1). Across both years, 559 unique breeds were advertised, yet 66% of all advertisements focused on 20 breeds, and 48% advertisements focused on only 10 breeds. Regional breed popularity was suggested, with French Bulldog as the most advertised breed in England (7.3%), Scotland (6.8%), and Wales (6.8%), but Schnauzers were most popular within Northern Ireland (6.83%). Within the 559 unique breeds advertised, only 3.4% had links to conformational disorders CD); however, these breeds were among the most commonly advertised, totalling 46.9% of all ads. Across all regions, price density peaked between GBP 300 and GBP 1000, with Bulldogs presenting the greatest cost (mean = GBP 1461.38, SD = GBP 940.56), followed closely by French Bulldog (mean = GBP 1279.44, SD = GBP 664.76) and Cavapoo (mean = GBP 1064.56, SD = GBP 509.17). CD breeds were found to be GBP 208.07 more expensive, on average, than non-CD breeds. Our results represent a buoyant online market with regional and seasonal fluctuations in price, advertised breed frequency and total counts. This suggests a market influenced by consumer trends, with a particular focus on breed preference, despite links to illness/disease associated with conformation. Our findings highlight the value of utilising online classified advertisement data for long-term monitoring, in order to assist with evidence-based regulatory reform, impact measurement of targeted campaigns, and legislative enforcement.
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The COVID-19 pandemic has highlighted the extreme vulnerability of our aviation system towards external disruptions. While there have been several earlier aviation-related crises, the impact of COVID-19 is unmatched in the history of modern aviation. Accordingly, a better understanding of the mechanisms and ramifications of this pandemic is instrumental for preparing towards future external disruptions. The contribution of our study is threefold. First, we dissect the disruptive impact of the pandemic on the scientific literature and extract the major trends and insights. Given the wide range of related venues and the extent of disruption, there have been many studies published in the last 2–3 years. Second, we perform a data-driven analysis of the full disruption cycle containing three episodes, starting with the epidemic shock early in the year 2020, over the pandemic stalemate, towards the endemic-induced recovery in the year 2022. Third, we summarize the major insights and derive a set of policy recommendations and future research directions which we consider essential on the way towards what we call pandemic-resilient aviation.
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Artificial intelligence (AI) applied to the genome sciences has the potential to revolutionize healthcare. Yet to fully harness the predictive power of AI, at least in the fields of oncology and infectious disease, evolutionary theory must be brought to bear. In oncology, AI is uniquely suited to analyze the complex latticework of correlations among the many genomic and environmental influences that constitute cancer risk. It also makes possible the evolutionary theory-inspired concepts of next-generation cancer treatment, such as evolutionary traps, adaptive therapy, and treatment vaccination. In infectious disease, AI promises the rapid diagnosis of a pathogen's current drug resistance profile, as well as the prediction of its potential to develop resistance. Using anticipatory diagnostics, drug regimens can be tailored to probabilistically channel pathogens toward less resistance-prone genotypes to avoid the emergence of resistance. Advanced computational methods are also used in antimicrobial drug design and to anticipate outbreaks of infectious disease and the evolution of epidemics, such as the SARS-CoV-2 pandemic. In detailing these advances, we discuss illustrative examples of the productive collaboration of data scientists, evolutionary theorists, epidemiologists, and clinicians. In addition, we briefly note the dangers of overreliance on advanced computational tools that involve "black box” algorithms and question whether they undermine the synthesis of Mendelian genetics and Darwinian theory. Yet, insofar as evolutionary theory is used for hypothesis algorithm development and AI for data creation and analysis, this problem may be avoided, and the potential of both will be realized. © Springer Nature Switzerland AG 2022.
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Gaining an understanding of speed–crash relationships is a critical issue in highway safety research. Because of the ongoing pandemic (COVID-19) there has been a reduction in traffic volume, and some early studies explain that speeding in an environment with less traffic is associated with a high number of crashes, especially fatal and serious injury crashes. This study aims to quantify the impact of operating speed on traffic crash occurrences. The study conflated several databases (speed data, roadway inventory data, and crash data) that contain data from Dallas, Texas, spanning from 2018 to 2020, to examine the speed–crash association. Using the negative binomial Lindley regression model, this study showed that the trends of crash prediction models vary over the years (2018, 2019, and 2020) by different injury severity levels (i.e., fatal crashes, fatal and incapacitating injury crashes). The 2020 models show that operating speed measures (i.e., average operating speed) have a significant impact on crash frequencies. The magnitudes of the speed measures show variations across the models at different injury severity levels.
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This paper builds on the PySD project, which seeks to bring together System Dynamics and Data Science by migrating models into a programming environment in Python. The authors develop an interactive tool, built on top of the PySD module as a step toward accessibility, which helps further the ability of (1) simulation to be intuitive for non-experts and (2) Data Science to facilitate model structure understanding. This tool is meant to serve as a conduit through which Data Science can be leveraged in Systems Dynamics modeling efforts.
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BACKGROUND: Digital education has expanded since the COVID-19 pandemic began. A substantial amount of recent data on how students learn has become available for learning analytics (LA). LA denotes the "measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs." OBJECTIVE: This scoping review aimed to examine the use of LA in health care professions education and propose a framework for the LA life cycle. METHODS: We performed a comprehensive literature search of 10 databases: MEDLINE, Embase, Web of Science, ERIC, Cochrane Library, PsycINFO, CINAHL, ICTP, Scopus, and IEEE Explore. In total, 6 reviewers worked in pairs and performed title, abstract, and full-text screening. We resolved disagreements on study selection by consensus and discussion with other reviewers. We included papers if they met the following criteria: papers on health care professions education, papers on digital education, and papers that collected LA data from any type of digital education platform. RESULTS: We retrieved 1238 papers, of which 65 met the inclusion criteria. From those papers, we extracted some typical characteristics of the LA process and proposed a framework for the LA life cycle, including digital education content creation, data collection, data analytics, and the purposes of LA. Assignment materials were the most popular type of digital education content (47/65, 72%), whereas the most commonly collected data types were the number of connections to the learning materials (53/65, 82%). Descriptive statistics was mostly used in data analytics in 89% (58/65) of studies. Finally, among the purposes for LA, understanding learners' interactions with the digital education platform was cited most often in 86% (56/65) of papers and understanding the relationship between interactions and student performance was cited in 63% (41/65) of papers. Far less common were the purposes of optimizing learning: the provision of at-risk intervention, feedback, and adaptive learning was found in 11, 5, and 3 papers, respectively. CONCLUSIONS: We identified gaps for each of the 4 components of the LA life cycle, with the lack of an iterative approach while designing courses for health care professions being the most prevalent. We identified only 1 instance in which the authors used knowledge from a previous course to improve the next course. Only 2 studies reported that LA was used to detect at-risk students during the course's run, compared with the overwhelming majority of other studies in which data analysis was performed only after the course was completed.
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COVID-19 , Pandemics , Humans , COVID-19/prevention & control , Learning , Delivery of Health Care , Power, PsychologicalABSTRACT
This paper combines available NLP technologies for Serbian languages and traditional data science methods in order to analyze collected dataset on the news headlines related to the COVID-19 pandemics. As an addition to NLP technologies for the Serbian language, a specialized database was created in an attempt to enhance the research within the field. Within the paper, the database was exploratory analyzed, and perspectives of the work with the data were thoroughly explored. © 2023 IEEE.
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This chapter outlines the approach of Expedia Group, the world's travel platform, and the role of technology in revolutionizing travel search, discovery, and booking. It covers innovations developed by online travel agencies (OTAs) and the unique challenges and opportunities provided by the breadth and depth of the data that global OTAs leverage to power travelers' online experiences. The focus is on accommodation, the largest revenue-generating, and most complex tourism segment. The chapter explores specific use cases where data are brought together with leading and innovative machine learning methodologies to improve traveler and supplier experiences. They include recommender systems, machine learning models that help Expedia Group manage the text and image content for over a million properties and revenue management systems for accommodation providers. The chapter concludes with a comment on the Expedia Group COVID- 19 response. © Springer Nature Switzerland AG 2022.
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Transport plays a major role in spreading contagious diseases such as COVID-19 by facilitating social contacts. The standard response to fighting COVID-19 in most countries has been imposing a lockdown—including on the transport sector—to slow down the spread. Though the Government of Bangladesh also imposed a lockdown quite early, it was forced to relax the lockdown for economic reasons. This motivates this study to assess the interaction between various non-pharmaceutical intervention (NPI) policies and transport sector outcomes, such as mobility and accidents, in Bangladesh. The study explores the effect of NPIs on both intra-and inter-regional mobility. Intra-regional mobility is captured using Google mobility reports which provide information about the number of visitors at different activity locations. Inter-regional, or long-distance, mobility is captured using vehicle count information from toll booths on a major bridge. Modeling shows that, in most cases, the policy interventions had the desired impact on people's mobility patterns. Closure of education institutes, offices, public transport, and shopping malls reduced mobility at most locations. The closure of garment factories reduced mobility for work and at transit stations only. Mobility was increased at all places except at residential locations, after the wearing of masks was made mandatory. Reduced traffic because of policy interventions resulted in a lower number of accidents (crashes) and related fatalities. However, mobility-normalized crashes and fatalities increased nationally. The outcomes of the study are especially useful in understanding the differential impacts of various policy measures on transport, and thus would help future evidence-based decision-making. © National Academy of Sciences: Transportation Research Board 2021.
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By March of 2020, most cities worldwide had enacted stay-at-home public health orders to slow the spread of COVID-19. Restrictions on nonessential travel had extensive impacts across the transportation sector in the short term. This study explores the effects of COVID-19 on shared e-scooters by analyzing route trajectory data in the pre- and during-pandemic periods in Austin, TX, from a single provider. Although total shared e-scooter trips decreased during the pandemic, partially owing to vendors pulling out of the market, this study found average trip length increased, and temporal patterns of this mode did not meaningfully change. A count model of average daily trips by road segment found more trips on segments with sidewalks and bus stops during the pandemic than beforehand. More trips were observed on roads with lower vehicle miles traveled and fewer lanes, which might suggest more cautious travel behavior since there were fewer trips in residential neighborhoods. Stay-at-home orders and vendor e-scooter rebalancing operations inherently influence and can limit trip demand, but the unique trajectory data set and analysis provide cities with information on the road design preferences of vulnerable road users.