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.
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
Together with a group of demographers from Latin America and the Caribbean (LAC), and endorsed by more than 250 individuals from the academic community, I contributed to a statement urging governments, the World Health Organization, and the Pan American Health Organization, to take immediate action to drastically increase the coverage of COVID-19 tests in the region. This call for action was disseminated by the British Society for Population Studies, Asociación Latino Americana de Población, Sociedad Mexicana de Demografía, Associação Brasileira de Estudos Populacionais, and the Population Association of America, among other important institutions.
I joined this initiative by invitation from Dr. Enrique Acosta and other colleagues, because I firmly believe that the prospects for the COVID-19 pandemic in the LAC region are rather dramatic. Several studies document that, apart from being globally recognized for its high levels of economic and social inequality, the region also suffers from institutional coordination failures and poor governance, a lack of appropriate resources, and presents a unique epidemiological and demographic profile of its population that escalates the negative prospects of the pandemic. I wanted to explore in more detail why these features of LAC are a source of major concern and require immediate action.
Social and economic inequality in LAC will hamper the enforcement of social distancing and isolation measures, which have proven to mitigate the COVID-19 epidemic in other settings. More than half of the population is in the informal labour market and does not have access to social safety nets. For those covered by the social security system, the benefits already proposed by a few governments of the region such as Brazil, fall short of the daily needs of families. In addition to economic inequality, social inequality, which leads to a high degree of cohabitation between adults and the elderly, increases the exposure of those with the highest risk of complications and death.
In addition, with the closure of schools, children who do not have access to day-care centres and the public- or private education system, often rely on the help of their grandparents, which again brings greater vulnerability to families. Not to mention that these children won’t have ensured their learning opportunities, because their parents are often working and not able to home-school them, thus compromising their education outcomes.
Moreover, LAC is facing a rapid demographic transition and aging process, which is temporarily increasing the prevalence of a young population, meaning that the population age-structure of potential infected individuals differs from that of other settings. However, unlike the more developed countries, LAC’s epidemiologic transition, that is, the transition in which the prevalence of infectious diseases is “substituted” by chronic and degenerative diseases, is not complete. Paradoxically, the region exhibits both the prevalence of diseases that have long been eradicated in more developed contexts (such as malaria, dengue, and tuberculosis) and diseases of richer countries (such as hypertension, diabetes, and neoplasms).
On top of all the above-mentioned vulnerabilities, crisis-management efforts in the region are uncoordinated, and lacking transparency and commitment. Taking Brazil as an example: while some mayors and governors adopt measures of social isolation and prevention against COVID-19, parts of the federal executive power not only disdain the problem, but encourages the population not to meet the requirements established by the Ministry of Health. Such conflicting rules are bound to cause misunderstandings among the LAC population. The COVID-19 pandemic is a crucial moment for institutional coordination to ensure the effective management of the crisis.
As an important and urgent call to action for the pandemic in the region, myself and other LAC researchers are calling for an increase in test coverage and measures of social isolation. As reported in the non-specialized media under the slogan “help to flatten the curve”, social isolation allows the rate of contagion of the virus to be reduced, in order to prevent overloading the capacity of the health system. Existing literature documents that while the virus does not cause major damage to health for the majority of infected persons, it brings a high cost to the health system. Furthermore, the impacts on the later lives of individuals who were hospitalized due to the disease are not yet known. Not to mention, of course, the human tragedy and the costs in terms of lives lost to the disease.
Finally, imperative and immediate action against COVID-19 in LAC will depend on the widespread and low-cost application of tests. This is required because the former rigorous isolation measures mentioned above are highly ineffective if not accompanied by aggressive strategies to detect cases of COVID-19. This highlights the relevance of data collection to better inform policymakers and provide researchers with clear diagnoses of the conditions in the region.
Deaton A (2013). Cap. 3. Escaping death in the Tropics. In The Great Escape: Health, Wealth, and the Origins of Inequality. Princeton University Press.
As researchers, the majority of our work – even if it is applied research – requires deep insight and plenty of reading and writing, which sometimes takes years. When we initiate a new method development project, for example, we never know if it will eventually prove to be useful in real life, except on very rare occasions when we are willing to step out of our academic comfort zones and explore if we are able to address the challenges that decision makers are faced with right now.
I would like to encourage my colleagues and our network to try and answer the call when decision makers ask for our help. It however requires courage to produce fast results with no time for peer review, to explore the limits of our knowledge and capabilities of our tools, and to run the risk of failure.
I share two examples with you in this blog. The first one describes a situation that played out years ago, while the second one is happening today.
When the first signs of a potential refugee crisis became visible late in 2014, the Finnish Prime Minister’s Office contacted the IIASA Advanced Systems Analysis Program (ASA) and asked whether we could produce an analysis for them. The ASA team had an idea to develop a new method for qualitative systems analysis based on an application of causal-loop-diagrams and we decided to test the approach with an expert team of 14 people from different Finnish ministries. I have to admit that the process was not exactly the best example of rigorous science, but it was able to produce results in only eight weeks.
“Experts that participated in the process from the government side accepted that the process was a pilot and exploratory in nature. In the end, the group was however able to develop a shared language for the different aspects of the refugee situation in Finland. The method produced comprised a shared understanding of the events and their interdependencies and we were able to assess the systemic impact of different policies, including unintended consequences. That was a lot in that situation,” said Sari Löytökorpi, Secretary General and Chief Specialist of the Finnish Prime Minister’s Office when reflecting on that experience recently.
The second case I want to describe here is the current coronavirus pandemic. The COVID-19 virus reached Finland at the end of January when a Chinese tourist was diagnosed. The first fatality in Finland was recorded on 20 March. This time, the challenge we are presented with is to look beyond the pandemic. The two research questions presented to us by the Prime Minister’s Office and the Ministry of Economic Affairs are: ‘How can the resilience of the national economy be enhanced in this situation?’ and secondly ‘What will the world look like after the pandemic?’
Pekka Lindroos, Director of Foresight and Policy Planning in the Finnish Ministry of Economic Affairs is confident, “We know that the pandemic will have a huge impact on the economy. The global outcome of current national policy measures is a major unknown and traditional economic analysis is not able to cover the dynamics of the numerous dimensions of the rupture. That is why we are exploring a combination of novel qualitative analysis and foresight methods with researchers in the IIASA ASA Program.”
I have been working on the implementation of the systems perspective to the coronavirus situation with a few close colleagues around the world who are experts in resilience and risk. We were able to deliver the first report on Friday, 27 March. Among other things, it emphasized the role of social capital and society’s resilience. A more detailed report is currently in production.
A simple systems map (causal loop diagram) representing a preliminary understanding of the world after COVID-19 from a one country perspective.
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.