ABSTRACT
Data visualizations can empower an audience to make informed decisions. At the same time, deceptive representations of data can lead to inaccurate interpretations while still providing an illusion of data-driven insights. Existing research on misleading visualizations primarily focuses on examples of charts and techniques previously reported to be deceptive. These approaches do not necessarily describe how charts mislead the general population in practice. We instead present an analysis of data visualizations found in a real-world discourse of a significant global event - Twitter posts with visualizations related to the COVID-19 pandemic. Our work shows that, contrary to conventional wisdom, violations of visualization design guidelines are not the dominant way people mislead with charts. Specifically, they do not disproportionately lead to reasoning errors in posters' arguments. Through a series of examples, we present common reasoning errors and discuss how even faithfully plotted data visualizations can be used to support misinformation. © 2023 Owner/Author.
ABSTRACT
This paper presents the second part of the research conducted at Riga Technical University aimed to explore the impact of the COVID-19 pandemic on Generation X (Gen X) and Generation Y (Gen Y) consumer behavior and purchasing priorities. While the changes in consumer behavior have already been analyzed and published earlier [1], the changes in purchasing priorities which might have caused changes in consumer behavior, are going to be studied in this work. The choice of these two generations is not made randomly;on the contrary, it was an intentional selection among other consumers, as they make a very active and prominent part of buyers all over the world. The research methods used are comparative descriptive analysis, Chi-square test and qualitative content analysis of data collected in an electronic survey of respondents from Asia, Europe, and America. The findings show that statistically significant differences between the changes in purchasing priorities of both generations are found for: meat and dairy products, fruit & vegetables, non-alcoholic and alcoholic drinks, clothes & shoes, body care & cosmetics, entertainment (pay TV services, computer games, etc.) and transport. Altogether, purchasing priorities of Gen X consumers were impacted by the COVID-19 pandemic less than Gen Y consumers. Copyright 2022. © by the International Institute of Informatics and Systemics. All rights reserved.
ABSTRACT
SARS-CoV-2 has bought many challenges to the world, socially, economically, and healthy habits. Even to those that have not experienced the sickness itself, and even though it has changed the lifestyle of the people across the world nation wise the effects of COVID-19 need to be analyzed and understood, analyzing a large amount of data is a process by itself, in this document details the analysis of the data collected from México by the Secretary of Health, the data was analyzed by implementing statistics, and classification methods known as K-Means, C&R Tree and TwoStep Cluster, using processed and unprocessed data. With the main emphasis on K-means. The study has the purpose of detecting what makes the highest impact on a person, to get sick, and succumb to the effects of the disease. In the study, it was found that in México the age of risk is at its highest at the age of 57, and the ones at the highest risk of mortality are those with hypertension and obesity, with those that present both at the age of 57 having a 19.37% of death. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The Covid-19 pandemic has changed the way customers shop. Previous research reports stated that during the Covid-19 pandemic there was an increase in transactions that occurred in e-Commerce and even a fairly large increase in income for the industry. On the other hand, it was found that there were often mistakes in shopping made by customers, resulting in product returns and so on. The implementation of data quality and data privacy is believed to have been carried out by e-Commerce. This study will explore how Data Quality and Data Privacy factors affect customer buying interest. This quantitative research uses the SmartPLS application to process the data and uses the SEM-PLS technique. This study used 477 e-commerce customers as respondents during the Covid-19 pandemic and from this research, interesting results were obtained for the development of e-commerce. The Complete Information factor was found to have a negative and significant influence on intention to buy, and other factors were also found that describe the behavior of customers who shop for e-commerce during the Covid-19 pandemic. © 2022 IEEE.
ABSTRACT
Exposure to technology impacts children’s perception and conceptualisation of the way devices they regularly use work. This prompts us to study if almost two years of online teaching, enabled by a broad range of technologies, have influenced the way children imagine a search companion would look and behave when helping them perform school-related search tasks. We conducted a 2-stage study during which children ages 9 to 11 drew and described their imaginary search companion;they also chose a few desirable and non-necessary traits. By following the protocol of a study conducted pre-pandemic, we contextualise salient altered expectations that we attribute to exposure to technology prompted by the COVID-19 pandemic. We highlight and discuss emerging trends observed from the analysis of data gathered before and after the extensive online experience and how these will guide the design of functionality of a search companion for the classroom. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The inclusion of information and communication technologies in education has become a priority for all universities. To meet this need, there are several research works that have dealt with the subject for several decades. However, for its inclusion, the analysis of each institution is necessary since the needs of the university population and the resources for its application change according to each situation. This work seeks to create a method that allows establishing the needs and doubts of students about the use of educational technologies in the classroom without affecting their performance. For this, a process has been designed that identifies learning needs, through the validation of data obtained from surveys and the monitoring of the academic efficiency and learning of a cohort of students. The follow-up includes a period of four years from 2019 to 2022. This follow-up allowed establishing three different realities, in 2019 the academic data was analyzed in a face-to-face education model, from 2020 to 2021 the follow-up was included in a remote model with the use of technologies as a communication channel and in 2022 these were included as a learning component, which marked an in-depth analysis of student performance and how technology affected their learning.
ABSTRACT
Analysis of data sets that may be changing often or in real-time, consists of at least three important synchronized components: i) figuring out what to infer (objectives), ii) analysis or computation of those objectives, and iii) understanding of the results which may require drill-down and/or visualization. There is considerable research on the first two of the above components whereas understanding actionable inferences through visualization has not been addressed properly. Visualization is an important step towards both understanding (especially by non-experts) and inferring the actions that need to be taken. As an example, for Covid-19, knowing regions (say, at the county or state level) that have seen a spike or are prone to a spike in the near future may warrant additional actions with respect to gatherings, business opening hours, etc. This paper focuses on a modular and extensible architecture for visualization of base as well as analyzed data. This paper proposes a modular architecture of a dashboard for user interaction, visualization management, and support for complex analysis of base data. The contributions of this paper are: i) extensibility of the architecture providing flexibility to add additional analysis, visualizations, and user interactions without changing the workflow, ii) decoupling of the functional modules to ease and speed up development by different groups, and iii) supporting concurrent users and addressing efficiency issues for display response time. This paper uses Multilayer Networks (or MLNs) for analysis. To showcase the above, we present the architecture of a visualization dashboard, termed CoWiz++ (for Covid Wizard), and elaborate on how web-based user interaction and display components are interfaced seamlessly with the back-end modules. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This paper describes the methodology and outcomes of a series of educational events conducted in 2021 which leveraged robot swarms to educate high-school and university students about epidemiological models and how they can inform societal and governmental policies. With a specific focus on the COVID-19 pandemic, the events consisted of 4 online and 3 in-person workshops where students had the chance to interact with a swarm of 20 custom-built brushbots - small-scale vibration-driven robots optimized for portability and robustness. Through the analysis of data collected during a post-event survey, this paper shows how the events positively impacted the students' views on the scientific method to guide real-world decision making, as well as their interest in robotics. © 2022 IEEE.
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Credit Rating Agencies (CRA’s) assess the potential payment of an issuer on a financial obligation, and credit rating (CR), as a symbolic indicator of CRA’s opinion, represents the creditworthiness of countries and financial organizations that recently has been affected by unexpected global COVID-19 health crises. To explore the ratings’ vulnerability to downgrades over the crisis, Fitch Rating System has been studied by logical analysis of data (LAD). The high percentage of the matched cases in training and test sets, shed light on the robust results of the explored patterns in the form of the decision trees. Despite these uncertainties, our best estimate clarifies that the coronavirus crisis had no adverse impact on “investment grades”;(AAA-BBB) till mid-year 2020 significantly, whereas “high yields”;(BBBM-BM) is threatened to be downgraded further as a Fitch comeback to the pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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BACKGROUND: After conducting necessary condition analysis (NCA), researchers have concluded that a certain, not too low, level of well-being is necessary but not sufficient for a high level of resilience. However, as acknowledged by the developers of the test, NCA only evaluates if the association between two variables is characterized by some unspecified type of non-randomness and not conditions of necessity. METHOD: Earlier reported data on the association between well-being and resilience among Filipino adults (N = 533) in COVID-19 quarantine were re-analyzed with an extended version of NCA. RESULTS: Analyses indicated a significant necessity effect of resilience on overall well-being, which is not logically compatible with well-being being necessary but not sufficient for resilience. Analyses with an extended version of NCA suggested that the association between overall well-being and resilience was characterized by equal degrees of necessity and sufficiency. CONCLUSIONS: The original version of NCA is only capable of evaluating if the association between two variables is characterized by some unspecified type of non-randomness. The extended version of NCA allows researchers to draw more specific conclusions.
Subject(s)
COVID-19 , Humans , SARS-CoV-2ABSTRACT
The emergence of the pandemic, Covid-19, impacted the worldwide economy. The daily scientific development helped in creating a vaccine against the virus. In order to limit the spread of the virus, government authorities are making it mandatory for vaccination certificates for people to run businesses or access public facilities like theaters, restaurants, etc. The government mandate of vaccine certificates compelled people to purchase fake certificates and fake entities to provide fake certificates. This paper provides an application-based blockchain solution for registering vaccinating authorities to vaccinate and provides relevant vaccine certificates that anyone can verify in no time. Additionally, analysis of data retrieving from the blockchain is provided because the data reading will be happening by thousands of authorities. The code for the prototype application is available on Github. © 2021 ACM.
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Corona Virus Disease 2019 (Covid-19) is a war between all humans and viruses. The outbreak of the Covid-19 epidemic has produced a large amount of data related to case information. Current related visualization studies are difficult to analyze these data, so a visualization analysis method for the Covid-19 epidemic situation in China is proposed. In this study, we present an effort to compile and analyze epidemiological outbreak information of Covid-19 based on the epidemic news and data in China after January 10, 2020. Through the analysis of data, it is concluded that the Covid-19 has the characteristics of a high infection rate and rapid transmission rate, and it also reflects the great contribution made by the Chinese government in controlling the epidemic. This study can obtain the hidden value behind the data, facilitate the understanding of the results of data analysis, and provide a reference for the government. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Outbreak of Covid-19 pandemic has proved highly disruptive to the order of world. With countries coming at standstill and economy reeling under the pressure, producing negative growth rate. Simultaneous it proved to be blessing in disguise in providing an opportunity for faster adaption of ICT especially into education and healthcare fields. Countries are coming with new acts, laws and rules to facilitate this. National Digital Health Mission, Telemedicine Practice Guidelines and CoWIN driven largest vaccination drive on the earth are some of such instances. ICT integration to healthcare can also help to control the impact and spread of pandemic. So, it the ripe time to implement ICT in healthcare to make maximum benefit out of it on one hand and to overcome scarcity of resource on other side. It can bring speed, convenience, and reliability in various aspects of healthcare ranging from prediction to treatment. In this paper authors have proposed one such comprehensive Covid management schema, for health infrastructure constrained countries like India, based on digitization of healthcare data, its intelligent analysis using Artificial Intelligence and remote patient care though telemedicine-based home treatment. Proposed schema considers historical EHR data comprising of structured health parameter data, unstructured imaging/genomic data and live semi structured critical parameter data collected through IoT devices. The proposed schema may relieve the extra burden on healthcare system by raising warning/alarm based on automated intelligent analysis of data. During this work authors have identified the critical role of Artificial Intelligence and need to tap its true potential in processing huge & inconsistent data produced by different sources. Real time analysis of data through some brute force method is not feasible, so we need intelligent solutions in detection and management of Covid. AI act as savior in this situation. ©2021 Copyright for this paper by its authors.
ABSTRACT
This study aimed to identify an appropriate simple mathematical model to fit the number of coronavirus disease 2019 (COVID-19) cases at the national level for the early portion of the pandemic, before significant public health interventions could be enacted. The total number of cases for the COVID-19 epidemic over time in 28 countries was analysed and fit to several simple rate models. The resulting model parameters were used to extrapolate projections for more recent data. While the Gompertz growth model (mean R2 = 0.998) best fit the current data, uncertainties in the eventual case limit introduced significant model errors. However, the quadratic rate model (mean R2 = 0.992) fit the current data best for 25 (89%) countries as determined by R2 values of the remaining models. Projection to the future using the simple quadratic model accurately forecast the number of future total number of cases 50% of the time up to 10 days in advance. Extrapolation to the future with the simple exponential model significantly overpredicted the total number of future cases. These results demonstrate that accurate future predictions of the case load in a given country can be made using this very simple model.
Subject(s)
COVID-19/diagnosis , Logistic Models , Models, Theoretical , COVID-19/epidemiology , Europe/epidemiology , Humans , Pandemics/prevention & controlABSTRACT
We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and to assess the potential of SNA as a tool for outbreak monitoring and control. We analysed contact tracing data of 1147 COVID-19 positive cases (mean age 34.91 years, 61.99% aged 11-40, 742 males), anonymised and made public by the Karnataka government. Software tools, Cytoscape and Gephi, were used to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links from source to target patients). Outdegree was 1-47 for 199 (17.35%) nodes, and betweenness, 0.5-87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher mean betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) 'super-spreaders' (outdegree ⩾ 5) caused 60% of the transmissions. Real-time social network visualisation can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritising these areas and individuals for rigorous containment could help minimise resource outlay and potentially achieve a significant reduction in COVID-19 transmission.