Interacting with Computers

Why Pandemics and Climate Change Are Hard to Understand and Make Decision-Making Difficult

Publication typeJournal Article
Publication date2023-08-28
scimago Q2
SJR0.400
CiteScore2.7
Impact factor1
ISSN09535438, 18737951
Library and Information Sciences
Software
Human-Computer Interaction
Abstract

This paper draws on diverse psychological, behavioural and numerical literature to understand some of the challenges we all face in making sense of large-scale phenomena and use this to create a road map for HCI responses. This body of knowledge offers tools and principles that can help HCI researchers deliver value now, but also highlights challenges for future HCI research. The paper is framed by looking at patterns and information that highlight some of the common misunderstandings that arise—not just for politicians and the general public but also for many in the academic community. This paper does not have all the answers to this, but we hope it provides some and, perhaps more importantly, raises questions that we need to address as scientific and technical communities.

Dykes J., Abdul-Rahman A., Archambault D., Bach B., Borgo R., Chen M., Enright J., Fang H., Firat E.E., Freeman E., Gönen T., Harris C., Jianu R., John N.W., Khan S., et. al.
We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs—a series of ideas, approaches and methods taken from existing visualization research and practice—deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/ . This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.
Chen M., Abdul-Rahman A., Archambault D., Dykes J., Ritsos P.D., Slingsby A., Torsney-Weir T., Turkay C., Bach B., Borgo R., Brett A., Fang H., Jianu R., Khan S., Laramee R.S., et. al.
Epidemics scimago Q1 wos Q2 Open Access
2022-06-01 citations by CoLab: 13 Abstract  
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.
Mello V.M., Eller C.M., Salvio A.L., Nascimento F.F., Figueiredo C.M., Silva E.S., Sousa P.S., Costa P.F., Paiva A.A., Mares-Guias M.A., Lemos E.R., Horta M.A.
PLoS ONE scimago Q1 wos Q1 Open Access
2022-02-23 citations by CoLab: 23 PDF Abstract  
In 2019, a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is transmitted via the airborne route, caused a new pandemic namely, “coronavirus disease 2019” (COVID-19). Although the effectiveness of face masks to prevent the transmission of SARS-CoV-2 is debated, no study has evaluated the virus-blocking efficacy of masks used by patients. We aimed to evaluate this efficacy of masks used by SARS-CoV-2-infected individuals. Data, masks used, and nasopharyngeal swab samples were obtained from these patients. Forty-five paired samples of nasopharyngeal swabs and masks were obtained and processed; the majority of masks were woven. Viral RNAs were amplified using quantitative reverse‐transcription polymerase chain reaction and detected only on the inner parts of masks. Median viral load (VL) values of swabs and masks were 1.954x106 and 2,51x103, respectively. Statistically, there was a difference of approximately 1000 RNA copies/mL between swabs and masks and no significant difference in VL values among different types of masks. There were statistically significant differences in VL values between men and women and between symptomatic and asymptomatic patients. Our findings suggest the blocking of virus transmission by different types of masks and reinforce the use of masks by both infected and non-infected individuals.
Weiskopf D.
2022-02-17 citations by CoLab: 22 PDF Abstract  
This paper provides an overview of uncertainty visualization in general, along with specific examples of applications in bioinformatics. Starting from a processing and interaction pipeline of visualization, components are discussed that are relevant for handling and visualizing uncertainty introduced with the original data and at later stages in the pipeline, which shows the importance of making the stages of the pipeline aware of uncertainty and allowing them to propagate uncertainty. We detail concepts and methods for visual mappings of uncertainty, distinguishing between explicit and implict representations of distributions, different ways to show summary statistics, and combined or hybrid visualizations. The basic concepts are illustrated for several examples of graph visualization under uncertainty. Finally, this review paper discusses implications for the visualization of biological data and future research directions.
Barnard R.C., Davies N.G., Pearson C.A., Jit M., Edmunds W.J.
2021-12-16 citations by CoLab: 36 Abstract  
AbstractThe Omicron B.1.1.529 SARS-CoV-2 variant was first detected in late November 2021 and has since spread to multiple countries worldwide. We model the potential consequences of the Omicron variant on SARS-CoV-2 transmission and health outcomes in England between December 2021 and April 2022, using a deterministic compartmental model fitted to epidemiological data from March 2020 onwards. Because of uncertainty around the characteristics of Omicron, we explore scenarios varying the extent of Omicron’s immune escape and the effectiveness of COVID-19 booster vaccinations against Omicron, assuming the level of Omicron’s transmissibility relative to Delta to match the growth in observed S gene target failure data in England. We consider strategies for the re-introduction of control measures in response to projected surges in transmission, as well as scenarios varying the uptake and speed of COVID-19 booster vaccinations and the rate of Omicron’s introduction into the population. These results suggest that Omicron has the potential to cause substantial surges in cases, hospital admissions and deaths in populations with high levels of immunity, including England. The reintroduction of additional non-pharmaceutical interventions may be required to prevent hospital admissions exceeding the levels seen in England during the previous peak in winter 2020–2021.
Jeon Y., Kim B., Xiong A., LEE D., Han K.
2021-10-13 citations by CoLab: 17 Abstract  
Because of the increasingly negative impacts of the echo chamber effect, such as the dissemination of fake news and political polarization occurring in social networking services (SNSs), considerable efforts are being made to mitigate this effect. Prior HCI studies have presented the development of user interfaces to display information that reflects various standpoints, with the aim of nudging people to consume information in a more objective fashion. However, these efforts still lack the ability to highlight the characteristics, generation processes, and negative effects of echo chambers, so they may not be effective in helping people become sufficiently aware of the echo chamber effect and those who are already in an echo chamber. In this paper, we present ChamberBreaker (CB), which has been designed to help increase a player's awareness of and preemptively respond to an echo chamber effect based on psychological concepts: inoculation, heuristics for judging, and gamification. Through a user study with 882 participants (control group: 446, experimental group: 436), we demonstrated the feasibility of our game-based methodology to support the awareness of the echo chamber effect and the importance of maintaining diverse perspectives when consuming information. Our findings highlight the externalization of psychological standpoints in mitigating an echo chamber effect and suggest design implications for system development---the consideration of demographics, playing time, and the connection to fake news recognition---for digital literacy education. You can play CB at http://tiny.cc/chamberbreaker (The game only works with Chrome.)
Martin S., Longo F., Lomas J., Claxton K.
BMJ Open scimago Q1 wos Q1 Open Access
2021-09-01 citations by CoLab: 21 Abstract  
ObjectivesThe first objective is to estimate the joint impact of social care, public health and healthcare expenditure on mortality in England. The second objective is to use these results to estimate the impact of spending constraints in 2010/2011–2014/2015 on total mortality.MethodsThe impact of social care, healthcare and public health expenditure on mortality is analysed by applying the two-stage least squares method to local authority data for 2013/2014. Next, we compare the growth in healthcare and social care expenditure pre-2010 and post-2010. We use the difference between these growth rates and the responsiveness of mortality to changes in expenditure taken from the 2013/2014 cross-sectional analysis to estimate the additional mortality generated by post-2010 spending constraints.ResultsOur most conservative results suggest that (1) a 1% increase in healthcare expenditure reduces mortality by 0.532%; (2) a 1% increase in social care expenditure reduces mortality by 0.336%; and (3) a 1% increase in local public health spending reduces mortality by 0.019%. Using the first two of these elasticities and data on the change in spending growth between 2001/2002–2009/2010 and 2010/2011–2014/2015, we find that there were 57 550 (CI 3075 to 111 955) more deaths in the latter period than would have been observed had spending growth during this period matched that in 2001/2002–2009/2010.ConclusionsAll three forms of public healthcare-related expenditure save lives and there is evidence that additional social care expenditure is more than twice as productive as additional healthcare expenditure. Our results are consistent with the hypothesis that the slowdown in the rate of improvement in life expectancy in England and Wales since 2010 is attributable to spending constraints in the healthcare and social care sectors.
Sanders J.G., Tosi A., Obradovic S., Miligi I., Delaney L.
Frontiers in Psychology scimago Q2 wos Q2 Open Access
2021-06-17 citations by CoLab: 9 PDF Abstract  
In recent years behavioural science has quickly become embedded in national level governance. As the contributions of behavioural science to the UK's COVID-19 response policies in early 2020 became apparent, a debate emerged in the British media about its involvement. This served as a unique opportunity to capture public discourse and representation of behavioural science in a fast-track, high-stake context. We aimed at identifying elements which foster and detract from trust and credibility in emergent scientific contributions to policy making. With this in mind, in Study 1 we use corpus linguistics and network analysis to map the narrative around the key behavioural science actors and concepts which were discussed in the 647 news articles extracted from the 15 most read British newspapers over the 12-week period surrounding the first hard UK lockdown of 2020. We report and discuss (1) the salience of key concepts and actors as the debate unfolded, (2) quantified changes in the polarity of the sentiment expressed toward them and their policy application contexts, and (3) patterns of co-occurrence via network analyses. To establish public discourse surrounding identified themes, in Study 2 we investigate how salience and sentiment of key themes and relations to policy were discussed in original Twitter chatter (N = 2,187). In Study 3, we complement these findings with a qualitative analysis of the subset of news articles which contained the most extreme sentiments (N = 111), providing an in-depth perspective of sentiments and discourse developed around keywords, as either promoting or undermining their credibility in, and trust toward behaviourally informed policy. We discuss our findings in light of the integration of behavioural science in national policy making under emergency constraints.
Hutzler F., Richlan F., Leitner M.C., Schuster S., Braun M., Hawelka S.
Royal Society Open Science scimago Q1 wos Q1 Open Access
2021-04-28 citations by CoLab: 13 Abstract  
Humans grossly underestimate exponential growth, but are at the same time overconfident in their (poor) judgement. The so-called ‘exponential growth bias' is of new relevance in the context of COVID-19, because it explains why humans have fundamental difficulties to grasp the magnitude of a spreading epidemic. Here, we addressed the question, whether logarithmic scaling and contextual framing of epidemiological data affect the anticipation of exponential growth. Our findings show that underestimations were most pronounced when growth curves were linearly scaledandframed in the context of a more advanced epidemic progression. For logarithmic scaling, estimates were much more accurate, on target for growth rates around 31%, and not affected by contextual framing. We conclude that the logarithmic depiction is conducive for detecting exponential growth during an early phase as well as resurgences of exponential growth.
Rambo A.P., Gonçalves L.F., Gonzáles A.I., Rech C.R., Paiva K.M., Haas P.
Sao Paulo Medical Journal scimago Q3 wos Q2 Open Access
2021-04-01 citations by CoLab: 14
Karlinsky A., Kobak D.
2021-01-29 citations by CoLab: 69 Abstract  
AbstractComparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the expected mortality, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no global, frequently-updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 94 countries and territories, openly available as the regularly-updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in several worst-affected countries (Peru, Ecuador, Bolivia, Mexico) the excess mortality was above 50% of the expected annual mortality. At the same time, in several other countries (Australia, New Zealand) mortality during the pandemic was below the usual level, presumably due to social distancing measures decreasing the non-COVID infectious mortality. Furthermore, we found that while many countries have been reporting the COVID-19 deaths very accurately, some countries have been substantially underreporting their COVID-19 deaths (e.g. Nicaragua, Russia, Uzbekistan), sometimes by two orders of magnitude (Tajikistan). Our results highlight the importance of open and rapid all-cause mortality reporting for pandemic monitoring.
Maxmen A.
Nature scimago Q1 wos Q1
2021-01-23 citations by CoLab: 28 Abstract  
Nearly one year ago, the World Health Organization sounded the alarm about the coronavirus, but was ignored. Nearly one year ago, the World Health Organization sounded the alarm about the coronavirus, but was ignored.
Howard J., Huang A., Li Z., Tufekci Z., Zdimal V., van der Westhuizen H., von Delft A., Price A., Fridman L., Tang L., Tang V., Watson G.L., Bax C.E., Shaikh R., Questier F., et. al.
2021-01-11 citations by CoLab: 858 Abstract  
The science around the use of masks by the public to impede COVID-19 transmission is advancing rapidly. In this narrative review, we develop an analytical framework to examine mask usage, synthesizing the relevant literature to inform multiple areas: population impact, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations. A primary route of transmission of COVID-19 is via respiratory particles, and it is known to be transmissible from presymptomatic, paucisymptomatic, and asymptomatic individuals. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. Given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory particles become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask wearing by infectious people (“source control”) with benefits at the population level, rather than only mask wearing by susceptible people, such as health care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.
Cooper I., Mondal A., Antonopoulos C.G.
Chaos, Solitons and Fractals scimago Q1 wos Q1
2020-10-01 citations by CoLab: 531 Abstract  
In this paper, we study the effectiveness of the modelling approach on the pandemic due to the spreading of the novel COVID-19 disease and develop a susceptible-infected-removed (SIR) model that provides a theoretical framework to investigate its spread within a community. Here, the model is based upon the well-known susceptible-infected-removed (SIR) model with the difference that a total population is not defined or kept constant per se and the number of susceptible individuals does not decline monotonically. To the contrary, as we show herein, it can be increased in surge periods! In particular, we investigate the time evolution of different populations and monitor diverse significant parameters for the spread of the disease in various communities, represented by countries and the state of Texas in the USA. The SIR model can provide us with insights and predictions of the spread of the virus in communities that the recorded data alone cannot. Our work shows the importance of modelling the spread of COVID-19 by the SIR model that we propose here, as it can help to assess the impact of the disease by offering valuable predictions. Our analysis takes into account data from January to June, 2020, the period that contains the data before and during the implementation of strict and control measures. We propose predictions on various parameters related to the spread of COVID-19 and on the number of susceptible, infected and removed populations until September 2020. By comparing the recorded data with the data from our modelling approaches, we deduce that the spread of COVID-19 can be under control in all communities considered, if proper restrictions and strong policies are implemented to control the infection rates early from the spread of the disease.

Top-30

Journals

1
1

Publishers

1
2
1
2
  • We do not take into account publications without a DOI.
  • Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
  • Statistics recalculated weekly.

Are you a researcher?

Create a profile to get free access to personal recommendations for colleagues and new articles.
Share
Cite this
GOST | RIS | BibTex
Found error?