Addressing vaccine hesitancy: experimental evidence from nine high-income countries during the COVID-19 pandemic.

We study the impact of public health messages on intentions to vaccinate and vaccination uptakes, especially among hesitant groups. We performed an experiment comparing the effects of egoistic and altruistic messages on COVID-19 vaccine intentions and behaviour. We administered different messages at random in a survey of 6379 adults in December 2020, following up with participants in the nationally representative survey Citizens' Attitudes Under COVID-19 Project covering nine high-income countries (Australia, Austria, France, Germany, Italy, New Zealand, Sweden, the UK and the USA). Four alternative interventions were tested, based on narratives of (1) self-protection, (2) protecting others, (3) reducing health risks and (4) economic protection. We measure vaccination intentions in the December 2020 survey and elicit actual vaccination behaviour by respondents in the June/July 2021 survey. Messages conveying self-protection had no effect on vaccine intentions but altruistic messages, emphasising protecting other individuals (0.022, 95% CI -0.004 to 0.048), population health (0.030, 95% CI 0.003 to 0.056) and the economy (0.038, 95% CI 0.013 to 0.064) had substantially stronger effects. These effects were stronger in countries experiencing high COVID-19 mortality (Austria, France, Italy, Sweden, the UK and the USA), where health risks may have been more salient, but weaker and, in several cases, not significant where mortality was low (Australia, Germany and New Zealand). On follow-up at 6 months, these brief communication interventions corresponded to substantially higher vaccination uptake. Our experiments found that commonly employed narratives around self-protection had no effect. However, altruistic messages about protecting individuals, population health and the economy had substantially positive and enduring effects on increasing vaccination intentions. Our results can help structure communication campaigns during pandemics and are likely to generalise to other vaccine-preventable epidemics.

Situated Visual Analysis and Live Monitoring for Manufacturing.

Modern machines continuously log status reports over long periods of time, which are valuable data to optimize working routines. Data visualization is a commonly used tool to gain insights into these data, mostly in retrospective (e.g., to determine causal dependencies between the faults of different machines). We present an approach to bring such visual analyses to the shop floor to support reacting to faults in real time. This approach combines spatio-temporal analyses of time series using a handheld touch device with augmented reality for live monitoring. Important information augments machines directly in their real-world context, and detailed logs of current and historical events are displayed on the handheld device. In collaboration with an industry partner, we designed and tested our approach on a live production line to obtain feedback from operators. We compare our approach for monitoring and analysis with existing solutions that are currently deployed.

'Citizens' Attitudes Under Covid19', a cross-country panel survey of public opinion in 11 advanced democracies.

This article introduces data collected in the Citizens' Attitudes Under Covid-19 Project (CAUCP), which surveyed public opinion throughout the Covid-19 pandemic in 11 democracies between March and December 2020. In this paper, we present a unique cross-country panel survey of citizens' attitudes and behaviors during a worldwide unprecedented health, governance, and economic crisis. This dataset investigates the behavioral and attitudinal consequences of multifaceted Covid19 crisis across time and contexts. In this paper, we describe the design of the CAUCP and the descriptive features of the dataset; we also present promising research prospects.

Visualization for Architecture, Engineering, and Construction: Shaping the Future of Our Built World.

Our built world is one of the most important factors for a livable future, accounting for massive impact on resource and energy use, as well as climate change, but also the social and economic aspects that come with population growth. The architecture, engineering, and construction industry is facing the challenge that it needs to substantially increase its productivity, let alone the quality of buildings of the future. In this article, we discuss these challenges in more detail, focusing on how digitization can facilitate this transformation of the industry, and link them to opportunities for visualization and augmented reality research. We illustrate solution strategies for advanced building systems based on wood and fiber.

Ideology and compliance with health guidelines during the COVID-19 pandemic: A comparative perspective.

OBJECTIVE: We measure the prevalence of noncompliance with public health guidelines in the COVID-19 pandemic and examine how it is shaped by political ideology across countries. METHODS: A list experiment of noncompliance and a multi-item scale of health-related behaviors were embedded in a comparative survey of 11,000 respondents in nine OCED countries. We conduct a statistical analysis of the list experiment capturing degrees of noncompliance with social distancing rules and estimate ideological effect heterogeneity. A semiparametric analysis examines the functional form of the relationship between ideology and the propensity to violate public health guidelines. RESULTS: Our analyses reveal substantial heterogeneity between countries. Ideology plays an outsized role in the United States. No association of comparable magnitude is found in the majority of the other countries in our study. In many settings, the impact of ideology on health-related behaviors is nonlinear. CONCLUSION: Our results highlight the importance of taking a comparative perspective. Extrapolating the role of ideology from the United States to other advanced industrialized societies might paint an erroneous picture of the scope of possible nonpharmaceutical interventions. Heterogeneity limits the extent to which policymakers can learn from experiences across borders.

A guilt-free strategy increases self-reported non-compliance with COVID-19 preventive measures: Experimental evidence from 12 countries.

Studies of citizens' compliance with COVID-19 preventive measures routinely rely on survey data. While such data are essential, public health restrictions provide clear signals of what is socially desirable in this context, creating a potential source of response bias in self-reported measures of compliance. In this research, we examine whether the results of a guilt-free strategy recently proposed to lessen this constraint are generalizable across twelve countries, and whether the treatment effect varies across subgroups. Our findings show that the guilt-free strategy is a useful tool in every country included, increasing respondents' proclivity to report non-compliance by 9 to 16 percentage points. This effect holds for different subgroups based on gender, age and education. We conclude that the inclusion of this strategy should be the new standard for survey research that aims to provide crucial data on the current pandemic.

2019 IEEE Scientific Visualization Contest Winner: Visual Analysis of Structure Formation in Cosmic Evolution.

Simulations of cosmic evolution are a means to explain the formation of the universe as we see it today. The resulting data of such simulations comprise numerous physical quantities, which turns their analysis into a complex task. Here, we analyze such high-dimensional and time-varying particle data using various visualization techniques from the fields of particle visualization, flow visualization, volume visualization, and information visualization. Our approach employs specialized filters to extract and highlight the development of so-called active galactic nuclei and filament structures formed by the particles. Additionally, we calculate X-ray emission of the evolving structures in a preprocessing step to complement visual analysis. Our approach is integrated into a single visual analytics framework to allow for analysis of star formation at interactive frame rates. Finally, we lay out the methodological aspects of our work that led to success at the 2019 IEEE SciVis Contest.

Gender differences in COVID-19 attitudes and behavior: Panel evidence from eight countries.

The initial public health response to the breakout of COVID-19 required fundamental changes in individual behavior, such as isolation at home or wearing masks. The effectiveness of these policies hinges on generalized public obedience. Yet, people's level of compliance may depend on their beliefs regarding the pandemic. We use original data from two waves of a survey conducted in March and April 2020 in eight Organisation for Economic Co-operation and Development countries (n = 21,649) to study gender differences in COVID-19-related beliefs and behaviors. We show that women are more likely to perceive COVID-19 as a very serious health problem, to agree with restraining public policy measures, and to comply with them. Gender differences in attitudes and behavior are sizable in all countries. They are accounted for neither by sociodemographic and employment characteristics nor by psychological and behavioral factors. They are only partially mitigated for individuals who cohabit or have direct exposure to the virus. We show that our results are not due to differential social desirability bias. This evidence has important implications for public health policies and communication on COVID-19, which may need to be gender based, and it unveils a domain of gender differences: behavioral changes in response to a new risk.

Feature-Based Volumetric Terrain Generation and Decoration.

Two-dimensional height fields are the most common data structure used for storing and rendering of terrain in offline rendering and especially real-time computer graphics. By its very nature, a height field cannot store terrain structures with multiple vertical layers such as overhanging cliffs, caves, or arches. This restriction does not apply to volumetric data structures. However, the workflow of manual modelling and editing of volumetric terrain usually is tedious and very time-consuming. Therefore, we propose to use three-dimensional curve-based primitives to efficiently model prominent, large-scale terrain features. We present a technique for volumetric generation of a complete terrain surface from the sparse input data by means of diffusion-based algorithms. By combining an efficient, feature-based toolset with a volumetric terrain representation, the modelling workflow is accelerated and simplified while retaining the full artistic freedom of volumetric terrains. Feature Curves also contain material information that can be complemented with local details by using per-face texture mapping. All stages of our method are GPU-accelerated using compute shaders to ensure interactive editing of terrain. Please note that this paper is an extended version of our previously published work [1] .