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.
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.
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.
Tran Thi Vo-Quyen, IIASA guest research scholar from the Vietnam Academy of Science and Technology (VAST), talks to Professor Dr. Ninh Khac Ban, Director General of the International Cooperation Department at VAST and IIASA council member for Vietnam, about achievements and challenges that Vietnam has faced in the last 5 years, and how IIASA research will help Vietnam and VAST in the future.
Professor Dr. Ninh Khac Ban, Director General of the International Cooperation Department at VAST and IIASA council member for Vietnam
What have been the highlights of Vietnam-IIASA membership until now?
In 2017, IIASA and VAST researchers started working on a joint project to support air pollution management in the Hanoi region which ultimately led to the successful development of the IIASA Greenhouse Gas – Air Pollution Interactions and Synergies (GAINS) model for the Hanoi region. The success of the project will contribute to a system for forecasting the changing trend of air pollution and will help local policy makers develop cost effective policy and management plans for improving air quality, in particular, in Hanoi and more widely in Vietnam.
IIASA capacity building programs have also been successful for Vietnam, with a participant of the 2017 Young Scientists Summer Program (YSSP) becoming a key coordinator of the GAINS project. VAST has also benefited from two members of its International Cooperation Department visiting the IIASA External Relations Department for a period of 3 months in 2018 and 2019, to learn about how IIASA deals with its National Member Organizations (NMOs) and to assist IIASA in developing its activities with Vietnam.
What do you think will be the key scientific challenges to face Vietnam in the next few years? And how do you envision IIASA helping Vietnam to tackle these?
In the global context Vietnam is facing many challenges relating to climate change, energy issues and environmental pollution, which will continue in the coming years. IIASA can help key members of Vietnam’s scientific community to build specific scenarios, access in-depth knowledge and obtain global data that will help them advise Vietnamese government officials on how best they can overcome the negative impact of these issues.
As Director General of the International Cooperation Department, can you explain your role in VAST and as representative to IIASA in a little more detail?
In leading the International Cooperation Department at VAST, I coordinate all collaborative science and technology activities between VAST and more than 50 international partner institutions that collaborate with VAST.
As the IIASA council representative for Vietnam, I participate in the biannual meeting for the IIASA council, I was also a member of the recent task force developed to implement the recommendations of a recent independent review of the institute. I was involved in consulting on the future strategies, organizational structure, NMO value proposition and need to improve the management system of IIASA.
In Vietnam, I advised on the establishment of a Vietnam network for joining IIASA and I implement IIASA-Vietnam activities, coordinating with other IIASA NMOs to ensure Vietnam is well represented in their countries.
You mentioned the development of the Vietnam-IIASA GAINS Model. Can you explain why this was so important to Vietnam and how it is helping to improve air quality and shape Vietnamese policy around air pollution?
Air pollution levels in Vietnam in the last years has had an adverse effect on public health and has caused significant environmental degradation, including greenhouse gas (GHG) emissions, undermining the potential for sustainable socioeconomic development of the country and impacting the poor. It was important for Vietnam to use IIASA researchers’ expertise and models to help them improve the current situation, and to help Vietnam in developing the scientific infrastructure for a long-lasting science-policy interface for air quality management.
The project is helping Vietnamese researchers in a number of ways, including helping us to develop a multi-disciplinary research community in Vietnam on integrated air quality management, and in providing local decision makers with the capacity to develop cost-effective management plans for the Hanoi metropolitan area and surrounding regions and, in the longer-term, the whole of Vietnam.
About VAST and Ninh Khac Ban
VAST was established in 1975 by the Vietnamese government to carry out basic research in natural sciences and to provide objective grounds for science and technology management, for shaping policies, strategies and plans for socio-economic development in Vietnam. Ninh Khac Ban obtained his PhD in Biology from VAST’s Institute of Ecology and Biological Resources in 2001. He has managed several large research projects as a principal advisor, including several multinational joint research projects. His successful academic career has led to the publication of more than 34 international articles with a high ranking, and more than 60 national articles, and 2 registered patents. He has supervised 5 master’s and 9 PhD level students successfully to graduation and has contributed to pedagogical texts for postgraduate training in his field of expertise.
Notes: More information on IIASA and Vietnam collaborations. This article gives the views of the authors, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.