Public institutions rely on external data sources and analysis to guide policymaking and intervention. Through our AI for Good initiative, we support organizations that provide such inputs with our technical expertise. We were recently approached by IIASA to create a dashboard to visualize COVID-19 data. This builds on our previous collaboration, which had us deliver a decision-making tool for natural disaster risk planning in Madagascar. In this article, we provide an example of how to help policymakers navigate the ocean of available data with dashboards that turn these data into actionable information.
Data is useful information when it creates value…or saves lives
The current pandemic emergency has put an unprecedented strain on both public health services and policymaking bodies around the world. Government action has been constrained in many cases by limited access to equipment and personnel. Adequate policymaking can help to coordinate the emergency relief effort effectively, make better use of scarce resources, and prevent such shortages in the future. This, however, requires access to secure, timely, and accurate information.
Governments commission various public bodies and research institutes to provide such data both for planning and coordinating the response. For instance, in the UK, the government commissioned the National Health Service (NHS) to build a data platform to consolidate a number of data providers into one single source. However, for the data to be useful it must be presented in a way that is consistent with the demands of an emergency situation. Therefore, the NHS partnered with a number of tech companies to visualize the data in dashboards and to provide deeper insights. Raw data, regardless of its quality, is not useful information until it is understood in a way that creates value – or in this case informs action that could save lives.
IIASA approached us to support them in making their COVID-19 data and indicators more useful to policymakers. The institute’s research is used by policymakers around the world to make critical decisions. We appreciated the opportunity to use our skills to support their efforts by creating an interactive data visualization tool.
IIASA COVID-19 report and mapbook
Research indicates that while all segments of the population are vulnerable to the virus, not all countries are equally vulnerable at the same time. Therefore, there is a need for accurate socioeconomic and demographic data to inform the allocation of scarce resources between countries and even within countries.
Current COVID-19 trends – information about the number of cases and effectiveness of policy response measures
Demographic indicators – age, population density, migration
Economic indicators – GDP, income, share of workers who work from home
Health-related indicators – information about healthcare system capacity
Tourism – number of visitors, including foreign
The indicators and data were chosen for their value in assisting epidemiological analysis and balanced policy formulation. Policymakers often face the challenge of prioritizing pandemic mitigation efforts over long-term impacts like unemployment, production losses, and supply-chain disruptions. IIASA’s series of maps and graphs facilitates understanding of these impacts while maintaining the focus on containing the spread of the virus.
Our collaboration – a dashboard for policymakers
Having taken the first step to disseminate the data as information in the form of a mapbook, Asjad Naqvi decided to make these data even more accessible by turning the maps into an interactive and visually appealing tool.
IIASA has previously approached Appsilon Data Science with a data visualization project, which had us improve the features and design of Visualize, a decision support tool for policymakers in natural disaster risk management. Building on this experience, we set out to assist Naqvi with creating a dashboard to deliver the data to end-users even faster.
The application allows for browsing through a list of 32 indicators and visualizing them on an interactive map. The list is not final with indicators being regularly reviewed, added, and retired on a weekly basis.
White circles indicate the number of cases per 1 million citizens.
The application will continue to provide the latest and most relevant information to track regional performance in Europe also in the post-pandemic phase:
The pandemic has a disproportionate impact on women’s employment and revealed some of the systemic inequalities.
Social distancing measures, for instance, have a large impact on sectors with high female employment rates. The closure of schools and daycare facilities particularly affects working mothers. Indicators such as female unemployment rate can inform appropriate remedial action in the post-COVID world and highlight regions of special concern like Castilla-La-Mancha in Spain.
Given the urgency of the pandemic emergency, we managed to develop and deploy this application within five days. We believe such partnerships between data science consultancies and research institutes can transform the way policymakers utilize data. We are looking forward to future collaborations with IIASA and other partners to help transform data into accessible and useful information.
A question from a Time magazine article has a clear underlying message: “Why is COVID-19 striking men harder than women?” By now, everyone has learned that men are more vulnerable to COVID-19 and, if infected, they tend to die much more often than women.
Are men however also more likely to get infected? On the face of it, the number of infections by gender suggests an almost perfect gender equality. Women represent on average 47% of all infections in 70 countries reporting the number of cases by sex, as listed in the online data tracker by Global Health 5050.
Case settled? Not quite yet. The aggregated total number might be deceiving. To understand an underlying story, one has to dig into the age and sex components of total infections. The overall balance of COVID cases by gender is an outcome of age- and sex-specific patterns of infection rates and the actual age- and sex composition of the population. This in turn, is often gender-unequal, especially at older ages, due to excess mortality among men and higher longevity of women.
In fact, in ten European countries I examined with colleagues from the Wittgenstein Centre for Demography and Global Human Capital, including Raya Muttarak from the IIASA World Population Program, it turns out that infection rates are highly gendered, especially when looking at the age pattern of coronavirus infection. From the teenage years up until their late 50s, women are more likely than men to be infected with COVID-19. Women in their 20s display the biggest gender gap in infections: on average only 64 men were infected per 100 infected women aged 20-29. After age 60, the pattern reverses, as infection rates among women drop at age 60-69 and the male infection rates go up or stay stable. This crossover is also clearly visible in the charts for Belgium, Czechia, Germany, and Italy. Between ages 60 and 79, men are more likely than women to be infected. The imbalance is sharpest among people in their 70s, with an average of 136 infected males per 100 infected women. This puts older males at a double disadvantage: they are more likely to be infected and, once infected, they are much more likely to die (with both higher age and being a male identified as important risk factors).
Is our evidence credible? Clearly, many infections are undetected and our data are affected by different testing availability and testing priorities across countries. It is possible that women of working age get more frequently tested than men as women tend to be more concerned about their health. This would bias the estimated share of infected women upwards. However, the remarkable regularity in the age- and gender-pattern of infections in the analyzed countries suggests that the observed gender disparities are real. The same gender disparity by age is observed in Czechia, Denmark, Germany, and Norway with relatively few infections, as well as in Belgium, England, Italy, and Spain with high numbers of reported infections. Of course, countries differ in their gender imbalance, especially at younger ages: the gender gap is, well, gaping, in Belgium, which reports only 34 infected men per 100 infected women at age 20-29. It is much smaller in Czechia, Germany, and Norway, but the female dominance at young ages and the male dominance at older ages, with a crossover around age 60, is consistently found in each society we studied.
What’s the likely explanation? At younger ages, the smoking gun points at women’s employment and occupations. Most women of working age in Europe are employed. This may also partly explain why European countries actually register a higher number of infections among women than most other countries, with an average share of 55%. More importantly, women are often working in professions that are most exposed to the infection. Think of nurses, medical doctors, other healthcare professionals, but also all the care workers in retirement homes, which turned out in some countries to be the focal points of infection. The switch in gender balance occurs right around the retirement age. The higher likelihood of infection among older men is probably linked with their poorer health and lower immunity.
If employment is potentially risky for women, staying at home with children—itself a product of ingrained gender inequalities in work and care—may lead to fewer infections. In countries where women’s employment dips after age 30 due to their extensive parental leaves, infection rates often show a distinct dip after that age as well, going up again in their 40s: Czechia, Germany, and partly Norway and Switzerland show such an M-shaped pattern of infection rates among women.
Even though the fatality rates of women below age 60 are low, engagement in care-work poses a higher risk to healthcare workers and care-home staff. This factor should be included in the ongoing discussions on the impact of COVID-19 on women’s health and wellbeing.
COVID-19 infection rates by age and sex per 1,000 population (solid line for females, dashed line for males, left-hand axis) and the relative M/F ratio in infection rates by age in four European countries
Late last year, my IIASA colleague Raya Muttarak, Roman Hoffmann from the Vienna Institute of Demography/Potsdam Institute for Climate Impact Research, and I were informed by the City of Vienna that our proposal to study “Climate, Health and Population” (CHAP) in the metropolitan area of Vienna had been granted funding for the 2020 period. Originally, we wanted to study what climate change and demographic change in the rapidly growing Austrian capital implies with regard to future vulnerability to extreme weather events. As the city is booming with economic activity and experiencing more tropical summer heat every year, the extent of the urban heat island increases as well, thus posing a steadily increasing risk to the city’s growing population, especially the elderly.
One conventional way of thinking about a population’s risk in the context of climate change is to decompose the risk and focus on its individual components. According to the famous “risk triangle” after Crichton, risk equals hazard times exposure times vulnerability. If any of the three can be taken out of the equation, the risk is reduced to zero … much like in the absence of sun, even the palest person can safely go outside without sunscreen! If, however, the hazard is there, people would be well advised to either not expose themselves to the sun or to reduce their vulnerability to skin damage and cancer by wearing sunscreen.
Now what does that have to do with our current predicament of a vast fraction of the world’s population being quarantined due to the outbreak of COVID-19? Well, as we and our CHAP colleagues were waiting for the meteorological data necessary for answering CHAP’s main research questions, we thought that we could focus on this much more imminent threat instead. In some way, the risk posed by COVID-19 can be viewed under the same lens as the above risk equation:
In terms of hazard, COVID-19 represents an unprecedented shock to social and economic systems and thus has a lot in common with climate-induced natural disasters. As humans are the carriers of the disease, the number of infected people in a local area can be considered as the hazard estimate. Meanwhile, by employing physical distancing (while remaining socially very active and helping, in particular, those around us that are in a more dire situation), we can lower exposure to that hazard a great deal and the risk can be reduced decisively. While under a business-as-usual scenario, our health system would soon find itself overwhelmed by an unbearable demand for health care, eventually having to give up lots of patients. The quarantine measures imposed in many countries serve to lower exposure and subsequently “flatten the curve”. So in order to reduce your own risk exposure and avoid increasing the risk for others, everyone who can afford to, please stay at home!
Likewise, we can to a certain extent work on lowering our vulnerability, both at the individual and at the societal level. Not everyone is equally vulnerable to the disease. As in the case of facing the challenges of climate change, populations faced with this pandemic are characterized by demographic differential vulnerability, expressed by the fact that the virus is more (but certainly not exclusively) lethal for older people, as well as those with preexisting health conditions and weakened immune systems. To reduce our individual vulnerability (in case we are exposed to the hazard), we can work on strengthening our immune systems.
At the societal level, we can reduce risks by identifying those places where the disease outbreak might have the strongest impact. For this we need suitable indicators available with sufficient spatial granularity. The initial, pre-lockdown infection hotspots, were often places that are well connected, such as travel hubs and touristic areas. In some cases, though, these hotspots were created simply as the consequence of bad luck, in other words, because there was a local “super-spreader” or a social event that brought together a large number of people. Such situations can hardly be anticipated. What we might be able to anticipate, though, are those vulnerable geographical hotspots where, given the pre-existing burden of disease, as well as the demographic and socioeconomic characteristics of the people that live there, the pandemic might cause the most havoc.
In line with the work by our IIASA colleague, Asjad Naqvi, we set out to map various indicators at the Austrian and Slovak municipal level (Slovak data courtesy of Michaela Potancokova from the IIASA World Population Program). Our indicators include things like the proportion of elderly population (>70+) or population density, but also the proportion of people with low socioeconomic status or a region’s connectedness in terms of the proportion of population commuting for work. These indicators can have varying importance in the short, medium, and long term — while mobility is no longer a big issue now that the population is in lockdown, socioeconomic characteristics, for example, may play a bigger role the longer the crisis lasts. While at the initial stages, Austrians with higher socioeconomic status were more likely to get infected due their mobility and larger social networks, the socioeconomic gradient might turn around eventually and those with lower social status might carry the brunt of the pandemic, as they are more likely to become unemployed and stay there for a longer period of time.
Our work to create a meaningful risk index from such vulnerability indicators is still in progress, but we aspire to pinpoint which areas are most likely going to need additional interventions, such as more testing or increased hospital capacities. This exercise will not only be useful at later stages of the pandemic, that is, when we slowly start moving back from the current quarantine situation (“The Hammer”) to gradual normalization (“The Dance”), but also when faced by other types of risks, such as from climatic hazards or economic shocks.
Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis
By Sonja Spitzer,research assistant in the IIASA World Population Program
Sonja Spitzer discusses how survey data often fails to capture all socioeconomic groups and explains how to ensure health information used by policymakers is based on accurate statistics.
Life expectancy continues to increase in Europe. We live longer, but do we live healthier? One way of tackling this question is by analysing health expectancy: a widely used indicator that counts the number of years an average person can expect to live in good health. To create this indicator researchers usually combine information about mortality with health data from surveys – and this is where many problems begin.
Survey participation is shaped by socioeconomic differences
Surveys do not always correctly represent the countries they seek to describe. A common deviation is that highly educated individuals are more likely to participate in surveys than less-educated individuals This is problematic for health research in particular, because highly educated people tend to be healthier than those who are less educated. Overrepresenting healthy and better educated individuals in surveys makes countries appear to have healthier populations than is the actual case. A recent study I conducted, that focused on European countries, showed that health expectancy measures are frequently upward biased, because less-educated people are underrepresented in the underlying data. The results of this study reflect the outcomes of other research; for example, estimates of rates of diabetes and asthma in Belgium are too low because individuals with a high level of education are overrepresented in the core data. In the Netherlands, the underrepresentation of those with lower levels of education has led to underestimating smoking prevalence, alcohol intake, and low levels of physical activity.
Make everyone count with statistical weights
Are you now wondering if you can ever trust health measures again? Do not despair! Surveys can still be a very useful source for answering health-related questions if the appropriate statistical tools are used. It is possible to account for the misrepresentation of participants with lower levels of education in surveys. The only thing needed is accurate information about the education structure of the population, that is: How many highly educated versus less-educated individuals live in a given country? In Europe, this information is readily available via censuses. Using information from censuses makes it possible to calculate statistical weights for surveys. If the less educated are underrepresented in surveys, each observation of a less educated individual is weighted relatively more than those with a higher level of education to account for the misrepresentation. This weighting enables surveys to resemble the population in the real world and the health measures that are based on them to no longer be biased by educational differences in survey participation.
Why do the less educated not participate in surveys?
Using survey methods such as statistical weights might become even more necessary in the future – it appears that the gap in survey participation between the higher and the less-educated is increasing year upon year. Those with low levels of education are frequently more difficult to engage, for example, less educated people can have less stable life paths and thus more often change their address. They may be less likely to provide requested information in surveys because they are too sick to participate or are less aware of the details of their health and financial situation. Finally, survey participation is usually voluntary and those with lower levels of education are more likely to refuse participation. One could speculate that this refusal to participate is because we, as researchers fail to engage with, or reach out to, less-educated individuals and the “value” of participating in surveys is therefore not well-communicated. This concern seems particularly important in the age of ‘fake news’. If less-educated individuals were better represented in surveys, this would make official statistics more reliable and might also lead to a better appreciation of statistics and how they can be more profound indicators than, for example, an opinion posed by someone on TV.
 Demarest, S., Van Der Heyden, J., Charafeddine, R., Tafforeau, J., Van Oyen, H., Van Hal, G.: Socio economic differences in participation of households in a Belgian national health survey. European Journal of Public Health. 23, 981–985 (2013). DOI:10.1093/eurpub/cks158
 Korkeila, K., Suominen, S., Ahvenainen, J., Ojanlatva, A., Helenius, H.: Non-response and related factors in a nation-wide health survey. European Journal of Epidemiology 17, 991–999 (2001)
 Reinikainen, J., Tolonen, H., Borodulin, K., Härkänen, T., Jousilahti, P., Karvanen, J., Koskinen, S., Kuulasmaa, K., Männistö, S., Rissanen, H., Vartiainen, E.: Participation rates by educational levels have diverged during 25 years in Finnish health examination surveys. European Journal of Public Health. 28, 237–243 (2018). DOI:10.1093/eurpub/ckx151
 Spitzer, S., Biases in health expectancies due to educational differences in survey participation of older Europeans: It’s worth weighting for. The European Journal of Health Economics. (2020) IIASA doi:10.1007/s10198-019-01152-0. http://pure.iiasa.ac.at/id/eprint/16281/
Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
By Roman Hoffmann, Wittgenstein Centre for Demography and Global Human Capital (IIASA, VID/ÖAW, and University of Vienna), Vienna Institute of Demography, Austrian Academy of Sciences, and the Potsdam Institute for Climate Impact Research.
Taking action to combat climate change and its impacts is urgent and vital to achieve the Sustainable Development Goals. Although per capita emissions are still highest in high-income countries, several emerging low and middle-income countries have seen a rise in carbon dioxide and other greenhouse gas emissions in recent years. While much of that rise was due to increased (export-oriented) industrial activities, changing lifestyle, consumption, and mobility patterns also played a significant role. How people can be encouraged to behave in an environmentally friendly way is a fundamental question for climate change mitigation. Despite a call for a stronger emphasis on demand-side solutions in mitigation strategies, little is known about the determinants of pro-environmental behaviors of people from the developing world.
In a study with IIASA researcher Raya Muttarak, which has recently been published in Environmental Research Letters, we found that education significantly contributes to increasing pro-environmental orientations and actions among low-income households in the Philippines. As an emerging lower-middle-income country, the Philippines are faced with severe environmental issues, such as pollution, deforestation, and environmental degradation. Already in the 1990s, public policy has responded to these challenges by developing several environmentally-focused learning initiatives and by making environmental education a fundamental pillar of the national school curriculum. Today, according to the World Value Survey, more than 73% of the population can identify with a person who gives importance to looking after the environment compared to 55% and 63% in the neighboring countries Thailand and Malaysia, respectively.
Figure 1 – Conceptual framework explaining the direct and indirect channels through which education influences environmental behavior. Note: The empirical design controls for the respondents’ pre-education background and indirect channels of influence allowing us to capture the direct effects of education on pro-environmental behaviors.
Based on original cross-sectional survey data, we found education to be positively related to pro-environmental behaviors such as recycling, proper garbage disposal, and planting trees. An additional year of schooling is estimated to increase the probability of carrying out climate-friendly actions by a substantial 3.3%. Going beyond previous research, we explored some of the underlying mechanisms through which education influences environmental behavior. . While knowledge and awareness raising are both important, it is found that education influences behavior mainly by increasing awareness about the anthropogenic causes of climate change, which may consequently affect the individual perception of self-efficacy in reducing human impacts on the environment. This is in line with the environmental psychology literature, which finds that poor understanding of the connection between human actions and climate change influences the perception of one’s ability to control and take action against it. People will become active only if the perceived likelihood of achieving a desired outcome is high enough. Education can hence play a vital role in promoting a better understanding of climate change and in raising awareness about the impacts of human activities.
Figure 2 – Map of study areas with locations of respondents’ homes. The study areas encompassed both more urban and rural areas located in Rizal province towards the East of the National Capital Region
In line with recent efforts of the international community to promote education for sustainable development, our study provides solid empirical evidence confirming the important role of education in climate change mitigation efforts. Investments in education can make an important contribution in raising awareness and ultimately in promoting green behavior helping to reduce the human impacts on the global climate system. In this regard, while it is important to provide learners with the necessary tools and capabilities to undertake pro-environmental actions, it is also key to raise their perceived self-efficacy. Education should thus not only focus on the transfer of knowledge and information, but also highlight the importance of the individual contribution in mitigating the harmful consequences of global environmental change.
Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
By Nadejda Komendantova, researcher in the IIASA Advanced Systems Analysis Program
Nadejda Komendantova discusses how misinformation propagated by different communication mediums influence attitudes towards migrants in Austria and how the EU Horizon 2020 Co-Inform project is fostering critical thinking skills for a better-informed society.
Austria has been a country of immigration for decades, with the annual balance of immigration and emigration regularly showing a positive net migration rate. A significant share of the Austrian population are migrants (16%) or people with an immigrant background (23%). The migration crisis of 2015 saw Austria as the fourth largest receiver of asylum seekers in the EU, while in previous years, asylum seekers accounted for 19% of all migrants. Vienna has the highest share of migrants of all regions and cities in Austria, and over 96% of Viennese have contact with migrants in everyday life.
Scientific research shows that it is however not primarily these everyday situations that are influencing attitudes towards migrants, but rather the opinions and perceptions about them that have developed over the years. Perceptions towards migration are frequently based on a subjectively perceived collision of interests, and are socially constructed and influenced by factors such as socialization, awareness, and experience. Perceptions also define what is seen as improper behavior and are influenced by preconceived impressions of migrants. These preconceptions can be a result of information flow or of personal experience. If not addressed, these preconditions can form prejudices in the absence of further information.
The media plays an essential role in the formulation of these opinions and further research is necessary to evaluate the impact of emerging media such as social media and the internet, and their consequent impact on conflicting situations in the limited profit housing sector. Multifamily housing in particular, is getting more and more heterogeneous and the impacts of social media on perceptions of migrants are therefore strongest in this sector, where people with different backgrounds, values, needs, origins and traditions are living together and interacting on a daily basis. Perceptions of foreign characteristics are also frequently determined by general sentiments in the media, where misinformation plays a role. Misinformation has been around for a long time, but nowadays new technologies and social media facilitate its spread, thus increasing the potential for social conflicts.
Early in 2019, the International Institute for Applied Systems Analysis (IIASA) organized a workshop at the premises of the Ministry of Economy and Digitalization of the Austrian Republic as part of the EU Horizon 2020 *Co-Inform project. The focus of the event was to discuss the impact of misinformation on perceptions of migrants in the Austrian multifamily limited profit housing sector.
Nadejda Komendantova addressing stakeholders at the workshop.
We selected this topic for three reasons: First, this sector is a key pillar of the Austrian policy on socioeconomic development and political stability; and secondly, the sector constitutes 24% of the total housing stock and more than 30% of total new construction. In the third place, the sector caters for a high share of migrants. For example, in 2015 the leading Austrian limited profit housing company, Sozialbau, reported that the share of their residents with a migration background (foreign nationals or Austrian citizens born abroad) had reached 38%.
Several stakeholders, including housing sector policymakers, journalists, fact checkers, and citizens participated in the workshop. Among them were representatives from the Austrian Chamber of Labor, Austrian Limited Profit Housing (ALPH) companies “Neues Leben”, “Siedlungsgenossenschaft Neunkirchen”, “Heim”, “Wohnbauvereinigung für Privatangestellte”, the housing service of the municipality of Vienna, as well as the Austrian Association of Cities and Towns.
The workshop employed innovative methods to engage stakeholders in dialogue, including games based on word associations, participatory landscape mapping, as well as wish-lists for policymakers and interactive, online “fake news” games. In addition, the sessions included co-creation activities and the collection of stakeholders’ perceptions about misinformation, everyday practices to deal with misinformation, co-creation activities around challenges connected with misinformation, discussions about the needs to deal with misinformation, and possible solutions.
During discussions with workshop participants, we identified three major challenges connected with the spread of misinformation. These are the time and speed of reaction required; the type of misinformation and whether it affects someone personally or professionally; excitement about the news in terms of the low level of people’s willingness to read, as well as the difficulties around correcting information once it has been published. Many participants believed that they could control the spread of misinformation, especially if it concerns their professional area and spreads within their networking circles or among employees of their own organizations. Several participants suggested making use of statistical or other corrective measures such as artificial intelligence tools or fact checking software.
The major challenge is however to recognize misinformation and its source as quickly as possible. This requirement was perceived by many as a barrier to corrective measures, as participants mentioned that someone often has to be an expert to correct misinformation in many areas. Another challenge is that the more exciting the misinformation issue is, the faster it spreads. Making corrections might also be difficult as people might prefer emotional reach information to fact reach information, or pictures instead of text.
The expectations of policymakers, journalists, fact checkers, and citizens regarding the tools needed to deal with misinformation were different. The expectations of the policymakers were mainly connected with the creation of a reliable, trusted environment through the development and enforcement of regulations, stimulating a culture of critical thinking, and strengthening the capacities of statistical offices, in addition to making relevant statistical information available and understandable to everybody. Journalists and fact checkers’ expectations on the other hand, were mainly concerned with the development and availability of tools for the verification of information. The expectations of citizens were mainly connected with the role of decision makers, who they felt should provide them with credible sources of information on official websites and organize information campaigns among inhabitants about the challenges of misinformation and how to deal with it.
*Co-Inform is an EU Horizon 2020 project that aims to create tools for better-informed societies. The stakeholders will be co-creating these tools by participating in a series of workshops in Greece, Austria, and Sweden over the course of the next two years.
Adapted from a blog post originally published on the Co-Inform website.
Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.