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
By Nicole Arbour, external relations manager in the IIASA Communications and External Relations department
As Canadian expats in Austria, one of the things that has particularly struck my family and I is the orderliness with which the country is dealing with the pandemic. As quarantine policies were put into place, we saw panic toilet paper hoarding in other countries, but here in Austria people were (amazingly) compliant and seemed to obey instructions and timelines provided by the authorities. We never worried about our basic needs. Grocery stores were always well stocked, public transit was always there and on time – and masks were readily available when required as physical barrier to protect others.
Expert opinions, governments, and publics are making it clear that there is no one-size-fits-all solution to this pandemic. What works in Austria might not be what worked for South Korea; and likely not the same as what works in other parts of Europe. Consider the Canadian landscape. There is huge variation in sociopolitical and cultural dynamics between and within provinces and territories. What works for some parts of Canada (virtual home schooling, grocery shopping) is impossible for others (Canada’s North). Cultural norms (multigenerational living, child/elder care) vary across the vast landscape. The “At Home on the Land” initiative – aimed at the particular needs of Indigenous communities is an example of a culturally-grounded way to address the pandemic. Finding solutions isn’t always as intuitive as we might like.
Humans tend to look for the easiest way out – we want simple solutions to complex problems. We don’t seem to want to think about the problems, we want them magically disappear. And thinking “outside of the box” isn’t always appreciated. Hand washing, clean water and the advent of antibiotics have made enormous leaps in our ability to tackle public health outbreaks – significant results. Where the bubonic plague is estimated to have killed 30%-60% of Europe’s population in the Middle Ages, modern outbreaks are now quickly identified and contained (were you even aware of the 2017 outbreak in Madagascar?). Understanding transmission routes has significantly impacted public health outcomes. The identification of tainted water as a vector for cholera transmission by John Snow led to the advent of modern epidemiology. But, as we find solutions to larger challenges, those that remain are more complex with increasing numbers of variables making solutions harder to come by.
There is some global agreement: lots of testing, quick results/containment, use of masks/physical barriers for community protection, social distancing, data collection. However, certain measures work better in some jurisdictions than others. What policies and practices are working and why are they working in these contexts? What is applicable in different contexts?
Our current global situation, has reminded me of a presentation I saw on the 2014 Ebola outbreak (Professor Melissa Leach, IDS), and how important it is to remember the human factor in crises. She discussed how the key elements that made the Ebola pandemic so persistent – despite the best efforts of global public health engagement – was a/the failure to understand how historic context, trust, cultural dynamics played into the spread of the virus. Those providing interventions did not appreciate how historic context (i.e. post-colonialism, slavery, medical testing scandals) and mistrust in the intentions of Western interventions factored into the willingness of the local population to accept the solutions provided. Awareness of social structures, influencers and leaders, and co-creation were also important to developing solutions that would be adopted by affected communities.
Evidence is more than the numbers of tests, infections and deaths. It is understanding the social context of communities, society writ large, and how they interact within and between. It’s about understanding historical context and how it feeds into local culture, social interactions and trust relationships. It’s about community dynamics, power struggles and the struggle for some to meet basic survival needs. It’s about timing of decision-making, political landscapes and different ways of leading. As with many of our global challenges, it’s a complex and multifaceted systems problem – in which the human factor is a huge driver.
As we strive for solutions to this global crisis – bring on innovation, research and science funding. We will need these – but please, also bring along those who study the complexity that is humanity: epidemiologists, anthropologists, economists, ethicists, political scientists, sociologists, futurists, etc. In an era where evidence is being questioned, fake news is rampant and anti-science sentiments are strong, it is crucial that we remember that one piece to engaging with this and the world’s other wicked problems is our relationships with our communities – the ones we are trying to protect. Public trust, built on understanding of the importance of human dynamics is key to broad acceptance and uptake. Solutions need to be palatable to society, or they won’t be adopted.
As we focus on the virus, let’s not forget the humans.
By Finn Laurien, researcher in the IIASA Risk and Resilience Program and Reinhard Mechler, Acting Program Director IIASA Risk and Resilience Program
The global COVID-19 crisis is challenging the social fabric of countries and communities across the globe. Interventions such as lockdowns, social distancing measures, and economic stimulus packages have been introduced to reinforce societal resilience. The resilience of national health systems is particularly in the spotlight – primarily keeping occupancy numbers of intensive care beds under a critical threshold, as well as improving access to basic health services for people infected with the virus, and ensuring that infections do not spread further.
At the same time, many COVID-19 affected regions and communities are confronted with additional multiple threats, including disaster and climate risks like flooding. For example, South Asia will be facing the monsoon season soon, and cyclones have already ravaged islands in the Pacific. So the question becomes, how do we support communities in preparing for and building resilience to such compound events like disasters AND infectious diseases?
Resilience has emerged as a system-based concept that explains how systems respond to shocks. IIASA has a long history of conceptualizing and assessing resilience. In partnership with members of the Zurich Flood Resilience Alliance (ZFRA), IIASA has co-developed an innovative approach called the Flood Resilience Measurement for Communities (FRMC) that measures the various facets of what builds resilience against flood risk at community levels. The FRMC consists of a holistic framework and an indicator-based assessment tool. It measures resilience before and after disasters at the community level – where people feel the impacts acutely and work together to take action. We define resilience widely in terms of a systems-thinking and development-centric conceptualization: “The ability of a system, community or society to pursue its social, ecological and economic development and growth objectives, while managing its disaster risk over time in a mutually reinforcing way.”
The FRMC measures resilience across a number of indicators that are collected through humanitarian and development NGO Alliance partners in communities in Asia, Europe, Latin America, and North America. It provides vital information for decision makers by prioritizing the resilience-building measures most needed by a community. At community and higher decision-making levels, measuring resilience also provides a basis for improving the design of public or privately funded programs to strengthen disaster resilience.
One of the seven themes that has been defined as a key aspect from the FRMC systems thinking approach is “Life and Health”, which is also relevant when looking at COVID-19 and includes access to and availability of healthcare facilities; strategies to maintain or quickly resume interrupted healthcare services; safety knowledge and Water and Sanitation (WASH).
Insights into dealing with COVID-19
In a recent research paper we analyzed FRMC data collected in 118 communities across nine countries in Asia, Latin America, and the US and explored which capacities or capitals contribute most to community disaster resilience. We identified multiple interactions, for instance, how action on bolstering health also contributes to social capital. There are two takeaways from this research that are relevant to other compound events, including the COVID-19 pandemic.
First, fair and functioning health systems play a key role in building resilience against compound risk – against flood as well as against other stresses that lead to negative health outcomes. Strategies that enable interrupted health systems to quickly resume are critical, and need to be in place before a disaster strikes.
In the communities where ZFRA conducted FRMC studies, disaster resilience and the health component scored relatively low at the beginning. However, when interventions such as household health-related trainings in Mexico, or hospital capacity assessments in Nepal, were implemented (with our measurement tool running), the health component increased for almost all countries (except for the USA) (see blue line in Figure 1). As the health component is a key part of resilience it contributed to disaster resilience overall, including ‘compound risks’ (green line in Figure 1). This means that (further) accelerating investments into health services (e.g., as part of COVID-19 response and recovery packages) leads to additional benefits for other shocks.
Figure 1: Between 2013 and 2018, increased community resilience can be attributed to resilience against compound risk (green line) and includes a health component (blue line). The difference between the two lines indicates the attribution of the increase in specific resilience to flood hazard.
A second takeaway is that through a so-called ‘multifunctionality’ effect, co-benefits are induced. This provides evidence of a virtuous cycle effect where higher resilient capacity in one area fosters communities’ resilience capacity for other functions. As community functions and outcomes are connected in a community system, improved access to health services can generate co-benefits (e.g., healthier individuals attain higher levels of livelihoods and build more social networks, which again build resilience during a shock). This has been well understood in the theoretical literature, and our analysis for the first time provides needed evidence at community level for flood and disaster risk.
If these co-benefit effects are taken into account, we find evidence that Food and Water strategies (see Figure 2) can be most efficient in building resilience to both adverse flood and health events. In fact, our sources of resilience indicate that the capacity in the Food and Water dimensions also foster health resilience.
Risk awareness is hazard-specific but can be integrated into packages that tackle risk generally. For example, health relevant interventions for infectious diseases (e.g., appropriate hygiene measures) can be integrated into flood evacuation plans. A best-practice example from our work are the campaigns and fairs carried out in Mexico and Nepal targeting educational awareness on health-related impacts during flood events.
Going forward with resilience thinking
Figure 2: Attribution of flood resilience to health component. Some dimensions show a similar pattern in building both flood and health resilience. Other flood-related efforts are too specific and cannot be attributed to resilience against COVID-19.
There is growing recognition by researchers, policymakers, and practitioners of the need to address compound risks in a development-centric way, tackling multiple threats with a focus on human wellbeing, rather than on hazards only. The COVID-19 crisis calls for donors, national governments, civil society, and communities to invest in comprehensive approaches that create multiple benefits.
Our system-based resilience research shows that using a systems resilience assessment at community level can identify direct short- and longer-term benefits. Investing in capacities builds resilience against compound risk such as flooding and infectious diseases. Investment into programs that ramp up health systems and WASH creates multiple benefits in terms of tackling COVID-19 and disaster and climate risks simultaneously. In the context of the upcoming monsoon and hurricane season, this means COVID-19 response and recovery packages need to invest in measures that also reduce social and economic impacts from COVID-19 under flood hazards. Additionally, diversifying household income strategies is high among the measures that unlock multiple co-benefits against compound risks. As action on COVID-19 (hopefully) moves from crisis response to recovery, such measures should be part and parcel of a post-COVID-19 recovery process, reducing the risk of vulnerable groups falling into poverty traps.
Keating, A., Campbell, K., Mechler, R., Magnuszewski, P.,Mochizuki, J.,Liu, W., Szoenyi, M., McQuistan, C. (2016). Disaster resilience: What it is and how it can engender a meaningful change in development policy. Development Policy Review 35 (1): 65-91
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.
Many Western countries are reaching, or have reached, the peak of COVID-19 infections, and policymakers are increasingly turning their attention to the next critical question: how to lift lockdown restrictions responsibly, while at the same time making sure that trade and travel can be restored to as close to “normal” as possible? Our research indicates that stoppage of airline traffic and border closures, which were some of the first modes of transport to be restricted, should also be some of the last to be restored because of their critical role in spreading infections.
Governments began to restrict airline traffic at the end of January this year, and by 21 March, over half of the EU had implemented flight suspensions. Our research confirms that this was a timely and necessary step. In the early stages of the pandemic, international flight linkages were actually the main transmission channel for the virus. In fact, flight connections proved to be an even more accurate predictor of infection spread between two countries than the presence of common land borders or trade connections. As country after country enacted travel bans, our research also shows a corresponding decrease in cross-country spillovers of the virus.
In Austria, for instance, our model demonstrates that if the shutdown of cross border traffic (flight connections and car border crossings) had been delayed by only 16 days, (25 March instead of 10 March), about 7,200 additional people would have been infected (see Figure 1).
Figure 1: Additional infections in Austria without border closures (Note: Shaded areas correspond to the 68th and 90th quantiles, respectively).
Additionally, our modeling shows the increased importance of flight connections over the initial period of the crisis, as seen in Figure 2. The top panel visualizes the relative importance of connectivity measures and demonstrates that, particularly in the beginning phases of the pandemic, flight connections were of the highest importance. The bottom panel shows infection spread between countries. Around the middle of March, when most border closure policies were implemented, the line drops to zero, indicating that these measures significantly reduced cross-border infections.
Figure 2: Importance of connectivity (top panel) and spatial spillovers (bottom panel)
Given the importance of air travel as a means for transmission of COVID-19, it stands to reason that governments and policymakers will have to continue to restrict air travel to prevent a second wave of the virus. As some parts of the world begin slowly to lift restrictions and ease lockdowns, while others are only now beginning to near the peak of the pandemic, it is likely that air travel will continue to be severely limited to prevent cross-border spread.
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.
Who would have imagined at the beginning of 2020, when the United Nations Department of Economic and Social Affairs was still projecting global economic growth at 2.5%, that within a few months the same department would have to release a new briefing stating that the global economy is now projected to shrink by 0.9% in 2020 due to a pandemic. This is mainly due to sudden restrictions and disruptions in global supply chains and international trade. COVID-19 is already having a lasting impact on the global economy; nearly 100 countries have closed their national borders during the past month, and the movement of people and tourism flows have come to a screeching halt.
In some countries, the COVID-19 pandemic has peaked in terms of the number of new infections, however, many countries are yet to reach the peak. Countries that seem to have crossed the peak are looking for ways to lift restrictions gradually, while keeping an eye on infection rates to avoid a second wave of infections. These actions by governments are being watched closely by people around the globe and trigger various kinds of emotional reactions.
Visualizing Twitter reactions in real time
I was curious about the possibility of visualizing these reactions, or sentiments, on a real-time basis as we crawl through these unprecedented times of the COVID-19 pandemic. It led me to create a real-time dashboard to visualize sentiments about the lifting of pandemic restrictions expressed or evident on the social media platform Twitter.
Twitter has application program interfaces (APIs) that enable developers to pull data from Twitter in a machine-readable format. This data is the same as the data shown when you open your Twitter account in either a browser or a mobile application and search for specific words. I decided to utilize this data using search key words like “lifting lockdown” and ”lifting restriction” and assign sentiment scores to tweets relating to these keywords using sentiment140.
Sentiment140 is a program created by computer science graduates from Stanford University that allows you to discover the sentiment of a brand, product, or topic on Twitter. It automatically classifies the sentiment of Twitter messages by using classifiers built using machine learning algorithms, and provides transparency for the classification results of individual tweets. Twitter uses complex algorithms to get the results for key words. These tweets are pulled continuously in real time and sent to sentiment140 APIs where they are assigned sentiment scores: 0 for negative, 2 for neutral and 4 for positive.
Below is an example of this scoring:
Why are people so eager to end lockdown and lift restrictions… for a second wave and then moan again… the mind boggles!!
Iran begins lifting restrictions after brief coronavirus lockdown
Germany has now begun to lift restrictions to visit one another and open businesses soon because we actually listened and stayed at home. Germany has now been marked the 2nd Safest country during the pandemic
From April 12th 2020 to April 21st 2020, a total of 208,220 tweets were scored and analyzed, this total number of tweets is growing daily as new tweets come in. The tweets are analyzed (sentiment scored) in real time and aggregated hourly. The above examples are taken from the analyzed tweets. For simplicity and to have a wholistic view of all relevant tweets, replies to tweets and re-tweets are all scored as people may react days after the initial tweet. For this experiment, only English language tweets are considered.
The scores assigned are aggregated every hour, stored in cloud storage, and are shown in the website dashboard. The dashboard shows the status of the current day’s scores and is updated every hour, it also shows the previous four days’ sentiment score results.
I can see a trend where most of the tweets fall under neutral scores as we are in the early days of restrictions being lifted. Many people are concerned about whether the measures will work. As the days progress I expect the neutral scores to reduce and convert into either positive or negative scores. This all depends on how infection rates either rise or fall in the days to come. Ideally, if everything turns out as planned, the positive sentiments will grow, and negative and neutral sentiments will shrink.
The scored tweets are not country specific but are captured globally, the reason being that less than 1-2% of Tweets are geo-tagged and for a real time experiment, I thought it would be too little data per hour. Since very few countries have crossed the peak of the curve, the current results show that the neutral and negative scores form the major share as we progress and hopefully, if infection rates do not increase drastically with the ease of lockdown restrictions, we might see positive sentiment scores taking the major share.
This is a sample experiment that I am running in the Microsoft Azure cloud using Azure Event Hubs and Azure Stream Analytics for real-time processing of Twitter data. I am storing the aggregated score results in Azure Blob Stores – you can read more about the setup here. The aggregated results are shown using a simple react java script application, which is again hosted in Microsoft Azure cloud. Do contact me for further details.