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1.
Am J Public Health ; 112(6): 893-895, 2022 06.
Article in English | MEDLINE | ID: covidwho-1875237
2.
Clin Infect Dis ; 74(Suppl_3): e14-e22, 2022 05 15.
Article in English | MEDLINE | ID: covidwho-1864958

ABSTRACT

Presenting information in a visual format helps viewers digest complex concepts in an efficient, effective manner. Recently, infographics have been used on social media and other digital platforms to educate health professionals, trainees, and patients about medical and public health topics. In addition, visual abstracts, visual representations of a research article's written abstract, have been increasingly used to disseminate new research findings to other health professionals. In this review article, we will define infographics and visual abstracts, describe why they are useful, outline how to create them, and explain how researchers, educators, and clinicians can use them effectively. We share resources and a stepwise approach that allows readers to create their own infographics and visual abstracts for research dissemination, medical education, and patient communication.


Subject(s)
Education, Medical , Social Media , Communication , Data Visualization , Health Personnel , Humans
4.
Essays Biochem ; 66(1): 65-73, 2022 04 29.
Article in English | MEDLINE | ID: covidwho-1784057

ABSTRACT

The present paper addresses a case study on the implementation of an online learning exercise utilising infographics in undergraduate Biochemistry and General Chemistry courses at the University of Roehampton (UoR) and Hostos Community College (HCC) of the City University of New York (CUNY). Students at UoR were asked to create infographics on topics related to the four major classes of biomolecules: carbohydrates, lipids, proteins and nucleic acids, and these infographics were shared with HCC students in an active learning exercise which incorporated peer evaluation and feedback. We highlight the various teaching and learning strategies, as well as the challenges related to the implementation of digital tools, in the educational process during the COVID-19 pandemic to maintain student engagement and active learning. Student feedback revealed positive learning gains on biochemistry concepts related to the four biomolecules. The exercise was viewed favourably by students, with learners indicating the acquisition of digital skills to effectively represent and visualise their understanding of biochemical concepts and explain these processes to peers.


Subject(s)
COVID-19 , Pandemics , Biochemistry/education , Data Visualization , Humans , Peer Group
5.
J Vis Commun Med ; 45(2): 39-47, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1764444

ABSTRACT

Navigating for accurate information, especially health- and science-related content, on social media has been challenging during the COVID-19 pandemic. Although infographics are a popular medium for simplifying text-based information into visual components, their usefulness during a global health crisis has not been explored. The study aims to explore the perceptions of infographics in conveying scientific information related to COVID-19 on social media. Following a social media campaign that published COVID-19 related infographics from May to August 2020, a cross-sectional survey was administered to social media users, primarily students from Western University. Several questions asked respondents to make comparisons with written articles when reporting their perceptions of infographics. Seventy-three percent of students from 361 responses belonged to health-related academic backgrounds. Seventy-two percent felt more likely to share infographics than written articles on social media due to the visual appeal. Nearly 90% felt it was easier to navigate through complicated science and that more scientists should use infographics on social media. Educational background did not influence the perceived usefulness of infographics in understanding scientific information. Infographics are perceived favourably in conveying scientific information about COVID-19 on social media. Findings from this study can inform communication strategies during a pandemic and, more broadly, global crises.


Subject(s)
COVID-19 , Social Media , COVID-19/epidemiology , Communication , Cross-Sectional Studies , Data Visualization , Humans , Pandemics , Surveys and Questionnaires
7.
Public Underst Sci ; 31(6): 751-765, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1736232

ABSTRACT

Guided by feelings-as-information theory, this experiment (N = 643), based in the United States, tested whether the use of jargon and infographics within messages designed to explain the COVID-19 mRNA vaccines affected behavioral intentions to vaccinate. The results revealed that the presence of jargon was associated with a difficult processing experience, message resistance, decreased perceptions of message credibility, and reduced intentions to get the COVID-19 vaccine. That said, when an infographic was integrated into the jargon message, these negative relationships went away and the presence of jargon no longer indirectly impacted intention to vaccinate. This experiment demonstrates that in contexts where jargon use exists, the use of an infographic can counteract some of the negative effects of a difficult processing experience.


Subject(s)
COVID-19 , Intention , COVID-19/prevention & control , COVID-19 Vaccines , Data Visualization , Humans , United States , Vaccination
10.
Sci Rep ; 12(1): 2014, 2022 02 07.
Article in English | MEDLINE | ID: covidwho-1671620

ABSTRACT

People worldwide use SARS-CoV-2 (COVID-19) visualizations to make life and death decisions about pandemic risks. Understanding how these visualizations influence risk perceptions to improve pandemic communication is crucial. To examine how COVID-19 visualizations influence risk perception, we conducted two experiments online in October and December of 2020 (N = 2549) where we presented participants with 34 visualization techniques (available at the time of publication on the CDC's website) of the same COVID-19 mortality data. We found that visualizing data using a cumulative scale consistently led to participants believing that they and others were at more risk than before viewing the visualizations. In contrast, visualizing the same data with a weekly incident scale led to variable changes in risk perceptions. Further, uncertainty forecast visualizations also affected risk perceptions, with visualizations showing six or more models increasing risk estimates more than the others tested. Differences between COVID-19 visualizations of the same data produce different risk perceptions, fundamentally changing viewers' interpretation of information.


Subject(s)
COVID-19/epidemiology , COVID-19/psychology , Data Visualization , Pandemics , Perception/physiology , SARS-CoV-2 , Adult , COVID-19/mortality , COVID-19/virology , California/epidemiology , Communication , Female , Forecasting , Humans , Male , New York/epidemiology , Risk Factors , Uncertainty , Young Adult
11.
Nat Biotechnol ; 40(1): 30-41, 2022 01.
Article in English | MEDLINE | ID: covidwho-1585828

ABSTRACT

Gaining a better understanding of the immune cell subsets and molecular factors associated with protective or pathological immunity against severe acute respiratory syndrome coronavirus (SARS-CoV)-2 could aid the development of vaccines and therapeutics for coronavirus disease 2019 (COVID-19). Single-cell technologies, such as flow cytometry, mass cytometry, single-cell transcriptomics and single-cell multi-omic profiling, offer considerable promise in dissecting the heterogeneity of immune responses among individual cells and uncovering the molecular mechanisms of COVID-19 pathogenesis. Single-cell immune-profiling studies reported to date have identified innate and adaptive immune cell subsets that correlate with COVID-19 disease severity, as well as immunological factors and pathways of potential relevance to the development of vaccines and treatments for COVID-19. For facilitation of integrative studies and meta-analyses into the immunology of SARS-CoV-2 infection, we provide standardized, download-ready versions of 21 published single-cell sequencing datasets (over 3.2 million cells in total) as well as an interactive visualization portal for data exploration.


Subject(s)
COVID-19/immunology , COVID-19/pathology , Data Visualization , Datasets as Topic , Immunity, Innate , SARS-CoV-2/immunology , Single-Cell Analysis , Animals , COVID-19/genetics , Data Analysis , Humans , Transcriptome
12.
JMIR Public Health Surveill ; 7(12): e32814, 2021 12 03.
Article in English | MEDLINE | ID: covidwho-1556320

ABSTRACT

BACKGROUND: COVID-19 vaccination is considered a critical prevention measure to help end the pandemic. Social media platforms such as Twitter have played an important role in the public discussion about COVID-19 vaccines. OBJECTIVE: The aim of this study was to investigate message-level drivers of the popularity and virality of tweets about COVID-19 vaccines using machine-based text-mining techniques. We further aimed to examine the topic communities of the most liked and most retweeted tweets using network analysis and visualization. METHODS: We collected US-based English-language public tweets about COVID-19 vaccines from January 1, 2020, to April 30, 2021 (N=501,531). Topic modeling and sentiment analysis were used to identify latent topics and valence, which together with autoextracted information about media presence, linguistic features, and account verification were used in regression models to predict likes and retweets. Among the 2500 most liked tweets and 2500 most retweeted tweets, network analysis and visualization were used to detect topic communities and present the relationship between the topics and the tweets. RESULTS: Topic modeling yielded 12 topics. The regression analyses showed that 8 topics positively predicted likes and 7 topics positively predicted retweets, among which the topic of vaccine development and people's views and that of vaccine efficacy and rollout had relatively larger effects. Network analysis and visualization revealed that the 2500 most liked and most retweeted retweets clustered around the topics of vaccine access, vaccine efficacy and rollout, vaccine development and people's views, and vaccination status. The overall valence of the tweets was positive. Positive valence increased likes, but valence did not affect retweets. Media (photo, video, gif) presence and account verification increased likes and retweets. Linguistic features had mixed effects on likes and retweets. CONCLUSIONS: This study suggests the public interest in and demand for information about vaccine development and people's views, and about vaccine efficacy and rollout. These topics, along with the use of media and verified accounts, have enhanced the popularity and virality of tweets. These topics could be addressed in vaccine campaigns to help the diffusion of content on Twitter.


Subject(s)
COVID-19 , Social Media , COVID-19 Vaccines , Data Mining , Data Visualization , Humans , SARS-CoV-2
13.
PLoS Comput Biol ; 17(9): e1009300, 2021 09.
Article in English | MEDLINE | ID: covidwho-1546830

ABSTRACT

Outbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions. We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. MicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully operational without an internet connection. Using publicly available data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. Users can email microbetrace@cdc.gov for support. The source code is available at https://github.com/cdcgov/microbetrace.


Subject(s)
Communicable Diseases/epidemiology , Data Visualization , Molecular Epidemiology/methods , Public Health/methods , Software , Centers for Disease Control and Prevention, U.S. , Disease Outbreaks , Humans , United States
14.
BMJ ; 375: n2978, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1546512
15.
J Healthc Eng ; 2021: 7197224, 2021.
Article in English | MEDLINE | ID: covidwho-1501832

ABSTRACT

During the COVID-19 pandemic, this study sought to determine the impact of AI-supported infographic templates (static versus animated) on academic achievement, visual thinking skills, and willingness to learn among a sample of university students. The researcher created two distinct AI-supported instructional infographic templates to determine the effect of such an independent variable on his three other primary dependent variables, namely, academic accomplishment, visual thinking skills, and willingness to learn, in order to meet his study goals. An achievement exam, a visual thinking test, and a willingness to learn scale were among the research methods used. The quasiexperimental method was used to provide the research instruments, and experimental therapy was used to a select group of students at Umm Al-Qura University. According to the findings, AI-assisted static and animated infographic templates had a favorable impact on the development of all learning aspects studied. AI-assisted animated infographics, on the other hand, had a more significant impact than AI-assisted static infographics. In light of this, the researcher concluded his research by making a number of recommendations and suggesting additional research, such as maximizing the use of AI emerging technologies and their practical application in an adaptive manner suitable for education.


Subject(s)
COVID-19 , Pandemics , Artificial Intelligence , Cognition , Data Visualization , Humans , SARS-CoV-2 , Students , Technology , Universities
16.
PLoS Comput Biol ; 17(10): e1009360, 2021 10.
Article in English | MEDLINE | ID: covidwho-1496326

ABSTRACT

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world's most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements under various non-pharmaceutical intervention strategies. The model, based on the June open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. The development and testing of this approach focuses on the Cox's Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings. Our findings suggest the encouraging self-isolation at home of mild to severe symptomatic patients, as opposed to the isolation of all positive cases in purpose-built isolation and treatment centers, does not increase the risk of secondary infection meaning the centers can be used to provide hospital support to the most intense cases of COVID-19. Secondly we find that mask wearing in all indoor communal areas can be effective at dampening viral spread, even with low mask efficacy and compliance rates. Finally, we model the effects of reopening learning centers in the settlement under various mitigation strategies. For example, a combination of mask wearing in the classroom, halving attendance regularity to enable physical distancing, and better ventilation can almost completely mitigate the increased risk of infection which keeping the learning centers open may cause. These modeling efforts are being incorporated into decision making processes to inform future planning, and further exercises should be carried out in similar geographies to help protect those most vulnerable.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Epidemics , Refugees , SARS-CoV-2 , Bangladesh/epidemiology , COVID-19/prevention & control , Comorbidity , Computational Biology , Computer Simulation , Data Visualization , Disease Progression , Humans , Masks , Physical Distancing , Refugees/statistics & numerical data , Schools , Systems Analysis
18.
Patient Educ Couns ; 105(2): 269-276, 2022 02.
Article in English | MEDLINE | ID: covidwho-1401769

ABSTRACT

OBJECTIVE: We propose that harm reduction messages advocating moderation versus abstinence from social interaction will be seen as less threatening and increase intentions to follow COVID-19 guidelines. We also examine two important moderators: the influence of risk framing and willingness to risk infection. METHOD: A 2 × 2 between-participants, randomized experiment (N = 476) varied infographics portraying low-risk behaviors, like going camping, versus high-risk behaviors, like attending a concert, followed by either moderation or abstinence guidelines. Participants in two additional control groups saw an infographic displaying either a full range of risk behaviors or behaviors that pose no risk, each followed by generic guidelines. RESULTS: Regression analyses show moderation messages are less freedom-threatening only when presenting low-risk behaviors. Persons more willing to risk infection found all messages more freedom-threatening; however, for these individuals, moderation messages increased behavioral intentions when risks were presented as high. CONCLUSION: This study suggests harm reduction may be applied effectively in a pandemic, where the behavior of risk-tolerant individuals, at a population level, could have suboptimal effects on curbing virus transmission. PRACTICE IMPLICATIONS: Health educators should communicate harm reduction with certain populations but also test to ensure messaging, including visuals communicating relative risks, are received as intended.


Subject(s)
COVID-19 , Data Visualization , Harm Reduction , Humans , Intention , SARS-CoV-2
19.
Nucleic Acids Res ; 49(W1): W36-W45, 2021 07 02.
Article in English | MEDLINE | ID: covidwho-1387964

ABSTRACT

Efficient integration and visualization of heterogeneous biomedical information in a single view is a key challenge. In this study, we present Arena3Dweb, the first, fully interactive and dependency-free, web application which allows the visualization of multilayered graphs in 3D space. With Arena3Dweb, users can integrate multiple networks in a single view along with their intra- and inter-layer connections. For clearer and more informative views, users can choose between a plethora of layout algorithms and apply them on a set of selected layers either individually or in combination. Users can align networks and highlight node topological features, whereas each layer as well as the whole scene can be translated, rotated and scaled in 3D space. User-selected edge colors can be used to highlight important paths, while node positioning, coloring and resizing can be adjusted on-the-fly. In its current version, Arena3Dweb supports weighted and unweighted undirected graphs and is written in R, Shiny and JavaScript. We demonstrate the functionality of Arena3Dweb using two different use-case scenarios; one regarding drug repurposing for SARS-CoV-2 and one related to GPCR signaling pathways implicated in melanoma. Arena3Dweb is available at http://bib.fleming.gr:3838/Arena3D or http://bib.fleming.gr/Arena3D.


Subject(s)
Algorithms , Data Visualization , Internet , Protein Interaction Maps , Software , COVID-19/drug therapy , COVID-19/metabolism , Color , Drug Repositioning , Humans , Melanoma/drug therapy , Melanoma/metabolism , Programming Languages , Receptors, Endothelin/metabolism , SARS-CoV-2/metabolism , Signal Transduction
20.
Public Underst Sci ; 30(7): 898-912, 2021 10.
Article in English | MEDLINE | ID: covidwho-1374061

ABSTRACT

Infographics of modest complexity are commonly used to convey knowledge to non-experts. However, little is known regarding how the use of infographics may convince the public and lead to massive behavioral changes in response to an acute cause. In March 2020, scientists and journalists revamped a scholarly published graph into the "flatten the curve" (FTC) mantra that defined the United States' initial response to the COVID-19 pandemic. This study examined how Americans' awareness of the flatten the curve charts relates to their perceived effectiveness of social distancing measures, perceived controllability of the pandemic, and behavioral intentions toward social distancing measures. Implications on visual communication of science are discussed.


Subject(s)
Attitude to Health , COVID-19/prevention & control , COVID-19/psychology , Communicable Disease Control/methods , Information Dissemination/methods , Pandemics/prevention & control , Physical Distancing , Data Visualization , Humans , SARS-CoV-2 , United States
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