A multifaceted graphical display, including treatment ranking, was developed to aid interpretation of network meta-analysis.

OBJECTIVES: Network meta-analysis (NMA) is becoming a popular statistical tool for analyzing a network of evidence comparing more than two interventions. A particular advantage of NMA over pairwise meta-analysis is its ability to simultaneously compare multiple interventions including comparisons not previously trialed together, permitting intervention hierarchies to be created. Our aim was to develop a novel graphical display to aid interpretation of NMA to clinicians and decision-makers that incorporates ranking of interventions. STUDY DESIGN AND SETTING: Current literature was searched, scrutinized, and provided direction for developing the novel graphical display. Ranking results were often found to be misinterpreted when presented alone and, to aid interpretation and effective communication to inform optimal decision-making, need to be displayed alongside other important aspects of the analysis including the evidence networks and relative intervention effect estimates. RESULTS: Two new ranking visualizations were developed-the 'Litmus Rank-O-Gram' and the 'Radial SUCRA' plot-and embedded within a novel multipanel graphical display programmed within the MetaInsight application, with user feedback gained. CONCLUSION: This display was designed to improve the reporting, and facilitate a holistic understanding, of NMA results. We believe uptake of the display would lead to better understanding of complex results and improve future decision-making.

A Literature Review of the Effects of Air Pollution on COVID-19 Health Outcomes Worldwide: Statistical Challenges and Data Visualization.

Several peer-reviewed papers and reviews have examined the relationship between exposure to air pollution and COVID-19 spread and severity. However, many of the existing reviews on this topic do not extensively present the statistical challenges associated with this field, do not provide comprehensive guidelines for future researchers, and review only the results of a relatively small number of papers. We reviewed 139 papers, 127 of which reported a statistically significant positive association between air pollution and adverse COVID-19 health outcomes. Here, we summarize the evidence, describe the statistical challenges, and make recommendations for future research. To summarize the 139 papers with data from geographical locations around the world, we also present anopen-source data visualization tool that summarizes these studies and allows the research community to contribute evidence as new research papers are published.

Evaluation of an Immersive COVID-19 Data Visualization.

COVID-19 restrictions have detrimental effects on the population, both socially and economically. However, these restrictions are necessary as they help reduce the spread of the virus. For the public to comply, easily comprehensible communication between decision makers and the public is thus crucial. To address this, we propose a novel 3-D visualization of COVID-19 data, which could increase the awareness of COVID-19 trends in the general population. We conducted a user study and compared a conventional 2-D visualization with the proposed method in an immersive environment. Results showed that the our 3-D visualization approach facilitated understanding of the complexity of COVID-19. A majority of participants preferred to see the COVID-19 data with the 3-D method. Moreover, individual results revealed that our method increases the engagement of users with the data. We hope that our method will help governments to improve their communication with the public in the future.

Using Text Mining and Data Visualization Approaches for Investigating Mental Illness from the Perspective of Traditional Chinese Medicine.

Background and Objectives. Anxiety and depressive disorders are the most prevalent mental disorders, and due to the COVID-19 pandemic, more people are suffering from anxiety and depressive disorders, and a considerable fraction of COVID-19 survivors have a variety of persistent neuropsychiatric problems after the initial infection. Traditional Chinese Medicine (TCM) offers a different perspective on mental disorders from Western biomedicine. Effective management of mental disorders has become an increasing concern in recent decades due to the high social and economic costs involved. This study attempts to express and ontologize the relationships between different mental disorders and physical organs from the perspective of TCM, so as to bridge the gap between the unique terminology used in TCM and a medical professional. Materials and Methods. Natural language processing (NLP) is introduced to quantify the importance of different mental disorder descriptions relative to the five depots and two palaces, stomach and gallbladder, through the classical medical text Huangdi Neijing and construct a mental disorder ontology based on the TCM classic text. Results. The results demonstrate that our proposed framework integrates NLP and data visualization, enabling clinicians to gain insights into mental health, in addition to biomedicine. According to the results of the relationship analysis of mental disorders, depots, palaces, and symptoms, the organ/depot most related to mental disorders is the heart, and the two most important emotion factors associated with mental disorders are anger and worry & think. The mental disorders described in TCM are related to more than one organ (depot/palace). Conclusion. This study complements recent research delving into co-relations or interactions between mental status and other organs and systems.

A Novel Metaphor Graph Drawing Method for Multidimensional Data Visualisation and Its Case Study on COVID-19 Vaccination Analysis.

Visualisation techniques have been one of the best data processing and analysis methods in recent decades, and they have assisted in data understanding efforts in various fields. Visualisation techniques for low-dimensional data are well developed and applied in multiple sectors; however, multidimensional data visualisation techniques still present some limitations, such as inaccurate data comparison and perception, exaggerated visual differences, label occlusion, and overlap. This study addresses the pros and cons and proposes a novel graphical drawing method, the multidimensional rose chart. It adopts the design idea of the Nightingale rose chart, but overcomes relevant limitations. The main challenges of this area include the incomplete presentation of multidimensional data, the neglect of the linkage of multiple attributes, the inefficient use of space, and the lack of simplicity of the interface. Contributions include enriching the representations of multidimensional data through the use of colour shades, area, and height sizes to represent values; straightforward data attribute comparisons via graph nesting; and detailed attributes showing the use of specific value labels. To verify the preliminary validity of this method, we imported COVID-19 data into experiments and further compared the final layouts with traditional methods, such as the line chart, bar chart, tree, parallel coordinate chart, and Nightingale rose chart, as well as their structures, functionalities, clear advantages, and disadvantages. The experimental results show that multidimensional rose diagrams perform effectively in presenting multidimensional data when comparing other graph drawing methods in our case, and the outcomes match existing works' conclusions in related COVID-19 research sectors. This work has the potential to provide a suitable supplemental approach to the multidimensional data analysis.

Informing the Design of Data Visualization Tools to Monitor the COVID-19 Pandemic in Portugal: A Web-Delphi Participatory Approach.

Due to the large amount of data generated by new technologies and information systems in the health arena, health dashboards have become increasingly popular as data visualization tools which stimulate visual perception capabilities. Although the importance of involving users is recognized in dashboard design, a limited number of studies have combined participatory methods with visualization options. This study proposes a novel approach to inform the design of data visualization tools in the COVID-19 context. With the objective of understanding which visualization formats should be incorporated within dashboards for the COVID-19 pandemic, a specifically designed Web-Delphi process was developed to understand the preferences and views of the public in general regarding distinct data visualization formats. The design of the Delphi process aimed at considering not only the theory-based evidence regarding input data and visualization formats but also the perception of final users. The developed approach was implemented to select appropriate data visualization formats to present information commonly used in public web-based COVID-19 dashboards. Forty-seven individuals completed a two-round Web-Delphi process that was launched through a snowball approach. Most respondents were young and highly educated and expressed to prefer distinct visualisation formats for different types of indicators. The preferred visualization formats from the participants were used to build a redesigned version of the official DGS COVID-19 dashboard used in Portugal. This study provides insights into data visualization selection literature, as well as shows how a Delphi process can be implemented to assist the design of public health dashboards.

Prosocial nudges and visual indicators increase social distancing, but authoritative nudges do not.

Social distancing reduces the transmission of COVID-19 and other airborne diseases. To test different ways to increase social distancing, we conducted a field experiment at a major US airport using a system that presented color-coded visual indicators on crowdedness. We complemented those visual indicators with nudges commonly used to increase COVID-19-preventive behaviors. Analyzing data from 57,146 travelers, we find that visual indicators and nudges significantly affected social distancing. Introducing visual indicators increased the share of travelers practicing social distancing, and this positive effect was enhanced by introducing nudges focused on personal benefits ("protect yourself") and public benefits ("protect others"). Conversely, an authoritative nudge referencing the Centers for Disease Control and Prevention ("don't break CDC COVID-19 guidelines") did not change social distancing behavior. Our results demonstrate that visual indicators and informed nudges can boost social distancing and potentially curb the spread of contagious diseases.

Creating Effective Infographics and Visual Abstracts to Disseminate Research and Facilitate Medical Education on Social Media.

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.

Case study: Biochemistry without borders: a case study utilising infographics.

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.

Perceptions of using infographics for scientific communication on social media for COVID-19 topics: a survey study.

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.

Using infographics to reduce the negative effects of jargon on intentions to vaccinate against COVID-19.

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.

Impact of COVID-19 forecast visualizations on pandemic risk perceptions.

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.

Single-cell immunology of SARS-CoV-2 infection.

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.

Factors Driving the Popularity and Virality of COVID-19 Vaccine Discourse on Twitter: Text Mining and Data Visualization Study.

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.

MicrobeTrace: Retooling molecular epidemiology for rapid public health response.

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.