The development and validation of a dashboard prototype for real-time suicide mortality data.

Introduction/Aim: Data visualisation is key to informing data-driven decision-making, yet this is an underexplored area of suicide surveillance. By way of enhancing a real-time suicide surveillance system model, an interactive dashboard prototype has been developed to facilitate emerging cluster detection, risk profiling and trend observation, as well as to establish a formal data sharing connection with key stakeholders via an intuitive interface. Materials and Methods: Individual-level demographic and circumstantial data on cases of confirmed suicide and open verdicts meeting the criteria for suicide in County Cork 2008-2017 were analysed to validate the model. The retrospective and prospective space-time scan statistics based on a discrete Poisson model were employed via the R software environment using the "rsatscan" and "shiny" packages to conduct the space-time cluster analysis and deliver the mapping and graphic components encompassing the dashboard interface. Results: Using the best-fit parameters, the retrospective scan statistic returned several emerging non-significant clusters detected during the 10-year period, while the prospective approach demonstrated the predictive ability of the model. The outputs of the investigations are visually displayed using a geographical map of the identified clusters and a timeline of cluster occurrence. Discussion: The challenges of designing and implementing visualizations for suspected suicide data are presented through a discussion of the development of the dashboard prototype and the potential it holds for supporting real-time decision-making. Conclusions: The results demonstrate that integration of a cluster detection approach involving geo-visualisation techniques, space-time scan statistics and predictive modelling would facilitate prospective early detection of emerging clusters, at-risk populations, and locations of concern. The prototype demonstrates real-world applicability as a proactive monitoring tool for timely action in suicide prevention by facilitating informed planning and preparedness to respond to emerging suicide clusters and other concerning trends.

Unlocking the Potential of Genomic Data to Inform Typhoid Fever Control Policy: Supportive Resources for Genomic Data Generation, Analysis, and Visualization.

The global response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic demonstrated the value of timely and open sharing of genomic data with standardized metadata to facilitate monitoring of the emergence and spread of new variants. Here, we make the case for the value of Salmonella Typhi (S. Typhi) genomic data and demonstrate the utility of freely available platforms and services that support the generation, analysis, and visualization of S. Typhi genomic data on the African continent and more broadly by introducing the Africa Centres for Disease Control and Prevention's Pathogen Genomics Initiative, SEQAFRICA, Typhi Pathogenwatch, TyphiNET, and the Global Typhoid Genomics Consortium.

Small is beautiful and important: economies and firms trading in digital services

Although many firms operate on global digital platforms, small countries and firms also play an essential role at the national level, especially during crises and the slowdown in globalization. This research investigates trade patterns in digital services at the country and firm level and identifies challenges in this area in providing new information and tools to startup mentors and policymakers, who need more evidence for national authorities to develop mentorship and digital programmes. The study also contributes to transaction cost theory, explaining how it is possible to reduce transaction costs. The methodology involves using quantitative and experimental methods, logistic regression for firms and correspondence analysis for countries. The WTO dataset was used to visualise trade in services data and to interpret clusters of digitalised countries. Interestingly, Estonia stands apart from other post-socialist countries in terms of digital services exports, being among smaller countries and hosting the highest concentration of startups per capita. The firmlevel analysis revealed that firms trading in digital services differ from others – being smaller, more focused on exports and more often controlled by non-residents. The study encourages investments in small countries and small firms that trade successfully in digital services.

Visualising "the vulnerable":Understanding vulnerability to COVID-19 in relation to the South African COVID-19 Vulnerability Index (SA CVI)

This study investigates the definition of "vulnerability" in the visualisation and underpinning index of Stats SA's South African COVID-19 Vulnerability Index (SA CVI)'s data visualisation dashboard. The paper establishes definitions of vulnerability in relation to literature before COVID-19, research in the time of the pandemic, and in relation to data visualisation. The discussion finds that while the pandemic is widely perceived as a "health crisis," South African vulnerability to this pandemic is mostly constituted by factors that fall outside of normative "health" concerns - beyond "straightforward" medical, biological and epidemiological factors. Instead, South African vulnerability to COVID-19, and the "health" of its citizens in this context, are largely to be understood as systemic, socio-economic, and necropolitical conditions. It is found that these conditions have not been generated by the pandemic but have rather been exposed by it.

Data stories and dashboard development: a case study of an aviation schedule and delay causes

In this case study, five key processes in modelling a data story of aviation data patterns during COVID-19 have been executed. It started with the collection of secondary data from relevant sources. Data inspection, transformation, and preparation activities, including data cleaning, filtering, and sampling, are all included in this work. Iterative exploratory data analysis (EDA) has been conducted to determine the pattern of each independent attribute, followed by an assessment after the data story is modelled and integrated on a dashboard. The questionnaire has been distributed and the visuals were assessed by giving respondents a few tasks to interpret stories based on their comprehension. The result shows that the data stories have been interpreted in a similar narrative by all the respondents. The overall mean score is 4.71, and this significantly shows that the respondents agree and strongly agree that the visual objects help in communicating patterns and stories. The overall process gives researchers experience and guidelines for future work. Overall, the objectives of the study have been met. Nevertheless, it gives researchers a lot of experience in interpreting data, cleansing and transformation, analysis, modelling the visualisation by selecting suitable charts, and integrating the objects together into a dashboard.

TRENDS AND PATTERNS IN RESEARCH REGARDING OPEN EDUCATIONAL RESOURCES BETWEEN 2018 AND 2022: A BIBLIOMETRIC ANALYSIS

Purpose. The purpose of this paper is to conduct a research regarding trends and patterns in a global scientific landscape of open educational resources – OERs in the period from 2018 to 2022. The results of the study could be significant not only for teachers and scientists who are involved into the researches dealing with OERs but for the whole academic community as well as to policy makers of educational policies. Approach/methodology/design. In line with the predefined search strategy, the corpus of relevant bibliographic units within Web of Science and Scopus databases was selected. For the purpose of a research a set of standard bibliometric methods based on the following six indicators: publication year, country distribution, institution distribution, journals, authors and keywords was used to find answers to the research questions. The visualisation of the research results based upon selected and extracted data from Web of Science and Scopus databases was prepared in VoSViewer tool. Findings. In 2020, during the pandemic of COVID-19 the focus of academic community regarding OERs reached its peak and it caused a massive increase of global researches dealing with OERs. According to the research results the most productive author was Daniel Burgos, the most productive institution was The Open University, the most productive country was USA and the most productive journal was International Review of Research in Open and Distance Learning. Furthermore, three intensive collaboration trends in citing and co-authorships were identified among: (1) Chinese and Spanish authors, (2) Turkish and American authors, and (3) South African and American authors. Originality/value. The research results obtained from this research are valuable because they give insight into the current state of trends and research patterns regarding open educational resources – OERs in the period from 2018 to 2022. The research is original because it encompasses a period just before and during the pandemic caused by the COVID-19 virus which undoubtedly led to acceleration of the digital transformation of education at all levels and put OERs in the focus of teachers, scientists and wider public. © 2022, Hrvatsko Knjiznicarsko Drustvo. All rights reserved.

Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2.

Analysing complex datasets while maintaining the interpretability and explainability of outcomes for clinicians and patients is challenging, not only in viral infections. These datasets often include a variety of heterogeneous clinical, demographic, laboratory, and personal data, and it is not a single factor but a combination of multiple factors that contribute to patient characterisation and host response. Therefore, multivariate approaches are needed to analyse these complex patient datasets, which are impossible to analyse with univariate comparisons (e.g., one immune cell subset versus one clinical factor). Using a SARS-CoV-2 infection as an example, we employed a patient similarity network (PSN) approach to assess the relationship between host immune factors and the clinical course of infection and performed visualisation and data interpretation. A PSN analysis of ~85 immunological (cellular and humoral) and ~70 clinical factors in 250 recruited patients with coronavirus disease (COVID-19) who were sampled four to eight weeks after a PCR-confirmed SARS-CoV-2 infection identified a minimal immune signature, as well as clinical and laboratory factors strongly associated with disease severity. Our study demonstrates the benefits of implementing multivariate network approaches to identify relevant factors and visualise their relationships in a SARS-CoV-2 infection, but the model is generally applicable to any complex dataset.

Data Driven decision support during COVID.

This paper outlines the development and use of a tool suite developed by the NCI Agency to provide situational awareness and decision support during the current Covid-19. The tool suite was developed to understand how Covid-19 could impact the provision of communication and information services (CIS) to NATO, and so understand where risks to NATO operational functions might occur. The tool suite combines open source data on instances of Covid-19 globally along with internal information about the impact of Covid-19 on NCI Agency staff and the services they deliver to the NATO enterprise. It supports business impact assessments due to Covid-19; showing trends, age demographics, and providing early indications of critical services that may be affected, sites that may be affected, etc. The tool suite is an example of data science techniques supporting data driven decision making within a military organization.

The Datatext: A Multilevel-Discursive Theory for Improved Public Health Data Visualizations

In the run up to the COVID-19 lockdown in March 2020, Prime Minister Boris Johnson challenged the British public to “squash the sombrero,” and so save thousands of lives in the event of the pandemic overburdening an already stretched National Health Service. There was a jarring sense of incongruity between this tabloid metaphor, and the minimalist line-graph to which the prime minister was referring. Best practice in infographic design may be well-suited to the communication of data amongst scientists and other literate audiences. But today matters of public health are subject to debate between citizens who are actively engaged in creating and circulating knowledge amongst wider publics with variable levels of literacy. Here a different epistemic approach, and different assumptions about design, are required. When conceiving of the infographic in public health as a multilevel discourse containing visual arguments mutually re-enforced by combinations of words, numbers and images, what I call a datatext (after W.J.T. Mitchell), it may be possible to design more effective communications. In this paper I set out a theoretical approach to infographic design drawing upon image schema theory, as well as conventional best practice. I conclude with recommendations for designing effective datatexts for optimal biocommunicability. [ FROM AUTHOR] Copyright of Javnost-The Public is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

RAMPVIS: Answering the challenges of building visualisation capabilities for large-scale emergency responses.

The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses.

Towards an Understanding of Population Health Data in a Single NHS Trust during COVID-19.

The objective of this study was to determine the further care needs of people discharged from the hospital following a COVID-19 illness from April-September 2020. Methods: In partnership with an NHS trust in the UK, data analysis was undertaken by linking data from the Trust, to facilitated a triage process. The intention was to provide information in a format that enabled an examination of the population data and highlight any inequality in provision. Data were mapped onto the indices of multiple deprivation, and a range of text and graphical methods were used to represent the population data to the hospital leadership. The visual representation of the demographics and deprivation of people discharged during a critical period of the pandemic was intended to support planning for community services. The results demonstrated that just under half of those discharged were from the poorest fifth of the English population and that just under half were aged 75 or older. This reflected the disproportional effect of COVID-19 on those who were poorer, older or had pre-existing multiple morbidities. Referral to community or outpatient services was informed by the analysis, and further understanding of the diversity of the population health was established in the Trust. Conclusion: By identifying the population and mapping to the IMD, it was possible to show that over half of discharged patients were from deprived communities, and there was significant organisational learning bout using data to identify inequalities.. The challenge of planning services that target underserved communities remains an important issue following the pandemic, and lessons learnt from one health system are being shared.

Communicating uncertainty in epidemic models.

While mathematical models of disease transmission are widely used to inform public health decision-makers globally, the uncertainty inherent in results are often poorly communicated. We outline some potential sources of uncertainty in epidemic models, present traditional methods used to illustrate uncertainty and discuss alternative presentation formats used by modelling groups throughout the COVID-19 pandemic. Then, by drawing on the experience of our own recent modelling, we seek to contribute to the ongoing discussion of how to improve upon traditional methods used to visualise uncertainty by providing a suggestion of how this can be presented in a clear and simple manner.

Mapping population vulnerability and community support during COVID-19: a case study from Wales.

BACKGROUND: Disasters such as the COVID-19 pandemic pose an overwhelming demand on resources that cannot always be met by official organisations. Limited resources and human response to crises can lead members of local communities to turn to one another to fulfil immediate needs. This spontaneous citizen-led response can be crucial to a community's ability to cope in a crisis. It is thus essential to understand the scope of such initiatives so that support can be provided where it is most needed. Nevertheless, quickly developing situations and varying definitions can make the community response challenging to measure. AIM: To create an accessible interactive map of the citizen-led community response to need during the COVID-19 pandemic in Wales, UK that combines information gathered from multiple data providers to reflect different interpretations of need and support. APPROACH: We gathered data from a combination of official data providers and community-generated sources to create 14 variables representative of need and support. These variables are derived by a reproducible data pipeline that enables flexible integration of new data. The interactive tool is available online (www.covidresponsemap.wales) and can map available data at two geographic resolutions. Users choose their variables of interest, and interpretation of the map is aided by a linked bee-swarm plot. DISCUSSION: The novel approach we developed enables people at all levels of community response to explore and analyse the distribution of need and support across Wales. While there can be limitations to the accuracy of community-generated data, we demonstrate that they can be effectively used alongside traditional data sources to maximise the understanding of community action. This adds to our overall aim to measure community response and resilience, as well as to make complex population health data accessible to a range of audiences. Future developments include the integration of other factors such as well-being.