Aug 11, 2020 | COVID19, Data and Methods, Science and Policy, Women in Science
By Sibel Eker, researcher in the IIASA Energy Program
IIASA researcher Sibel Eker explores the usefulness and reliability of COVID-19 models for informing decision making about the extent of the epidemic and the healthcare problem.
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In the early days of the COVID-19 pandemic, when facts were uncertain, decisions were urgent, and stakes were very high, both the public and policymakers turned not to oracles, but to mathematical modelers to ask how many people could be infected and how the pandemic would evolve. The response was a plethora of hypothetical models shared on online platforms and numerous better calibrated scientific models published in online repositories. A few such models were announced to support governments’ decision-making processes in countries like Austria, the UK, and the US.
With this announcement, a heated debate began about the accuracy of model projections and their reliability. In the UK, for instance, the model developed by the MRC Centre for Global Infectious Disease Analysis at Imperial College London projected around 500,000 and 20,000 deaths without and with strict measures, respectively. These different policy scenarios were misinterpreted by the media as a drastic variation in the model assumptions, and hence a lack of reliability. In the US, projections of the model developed by the University of Washington’s Institute for Health Metrics and Evaluation (IHME) changed as new data were fed into the model, sparking further debate about the accuracy thereof.
This discussion about the accuracy and reliability of COVID-19 models led me to rethink model validity and validation. In a previous study, my colleagues and I showed that, based on a vast scientific literature on model validation and practitioners’ views, validity often equates with how good a model represents the reality, which is often measured by how accurately the model replicates the observed data. However, representativeness does not always imply the usefulness of a model. A commentary following that study emphasized the tradeoff between representativeness and the propagation error caused by it, thereby cautioning against an exaggerated focus on extending model boundaries and creating a modeling hubris.
Following these previous studies, in my latest commentary in Humanities and Social Sciences Communications, I briefly reviewed the COVID-19 models used in public policymaking in Austria, the UK, and the US in terms of how they capture the complexity of reality, how they report their validation, and how they communicate their assumptions and uncertainties. I concluded that the three models are undeniably useful for informing the public and policy debate about the extent of the epidemic and the healthcare problem. They serve the purpose of synthesizing the best available knowledge and data, and they provide a testbed for altering our assumptions and creating a variety of “what-if” scenarios. However, they cannot be seen as accurate prediction tools, not only because no model is able to do this, but also because these models lacked thorough formal validation according to their reports in late March. While it may be true that media misinterpretation triggered the debate about accuracy, there are expressions of overconfidence in the reporting of these models, even though the communication of uncertainties and assumptions are not fully clear.
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The uncertainty and urgency associated with pandemic decision-making is familiar to many policymaking situations from climate change mitigation to sustainable resource management. Therefore, the lessons learned from the use of COVID models can resonate in other disciplines. Post-crisis research can analyze the usefulness of these models in the discourse and decision making so that we can better prepare for the next outbreak and we can better utilize policy models in any situation. Until then, we should take the prediction claims of any model with caution, focus on the scenario analysis capability of models, and remind ourselves one more time that a model is a representation of reality, not the reality itself, like René Magritte notes that his perfectly curved and brightly polished pipe is not a pipe.
References
Eker S (2020). Validity and usefulness of COVID-19 models. Humanities and Social Sciences Communications 7 (1) [pure.iiasa.ac.at/16614]
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.
Apr 29, 2020 | Austria, COVID19, Systems Analysis
By Tamás Krisztin, researcher in the IIASA Ecosystems Services and Management Program
Tamás Krisztin discusses the air travel restrictions instituted by governments across the globe and how effective they really are in terms of curbing the spread of COVID-19.
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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.
Oct 15, 2019 | IIASA Network, Science and Policy, South Africa
Bettina Greenwell, communications officer at IIASA, talks to Dorsamy (Gansen) Pillay, Deputy CEO: Research and Innovation Support and Advancement (RISA), National Research Foundation (NRF), and IIASA council member for South Africa, about the NRF’s statement on ethical research and scholarly publishing practices. The statement was jointly issued in August 2019 with South African partners within the National System of Innovation (NSI) in South Africa.
Dorsamy (Gansen) Pillay, Deputy CEO: Research and Innovation Support and Advancement (RISA), National Research Foundation (NRF), and IIASA council member for South Africa
What is ethics in research and why is it so important?
Research is a quest for truth. The research must be well conceptualized with a clear research question(s) which can lead to new knowledge. Good ethics and integrity dictate that the truth must be presented in its absolute form, and the findings need to be appropriately interpreted and should be reproducible.
South Africa was awarded the right to host the 7th World Conference on Research Integrity (WCRI) in 2021 in Cape Town – the first time it will be hosted on an African continent. Why is this statement important for the science community in South Africa?
Firstly, it is a privilege to host this conference and South Africa feels very honored. The statement on ethical research and scholarly publishing practices is an important contribution to this conference. We have noticed that South African academics and researchers, especially new and emerging researchers, are under a lot of pressure to publish their work for a variety of reasons. In some instances, ethical principles have been violated. This included the dissemination of research through predatory journals. However, this was not unique to South Africa only as other countries also faced similar challenges. The NRF as a science granting foundation felt compelled to respond to this challenge. The NRF sees itself as a custodian and guardian of research ethics and integrity. Through our peer-review processes, we ensure that research proposals for funding have been robustly interrogated, and the highest ethical principles upheld. As a consequence the NRF developed and issued a joint statement on ethical research and scholarly publications in collaboration with the Department of Science and Innovation (DSI), the Department of Higher Education and Training (DHET), the Council on Higher Education (CHE), the Academy of science of South Africa (ASSAf), Universities South African (USAf) and the NRF. In addition, the NRF has previously issued a statement on predatory publishing.
What do you think will be the key scientific challenges to face South Africa in the next few years? And how do you envision IIASA helping South Africa to tackle these?
There are several challenges, and some of the most pressing ones are poverty and inequality, population migration and unemployment. Given the systems analysis approach, we feel we can draw on IIASA’s expertise to address these challenges. IIASA has used South Africa as a laboratory for its population studies research over several years. It is now time to ensure that this research is translated into policies so that it may impact positively on society.
Housing is also a problem in South Africa. There is a lack of decent, affordable housing for people. The new IIASA strategic plan focuses on smart cities – this could play a role in addressing these housing challenges.
IIASA’s expertise is a systems analysis approach which can be applied to complex issues. The important part of the work is when scientific results are turned into policy – that’s when there is an actual, tangible societal benefit.
South Africa has been an IIASA member since 2007. What have been the highlights of the South Africa-IIASA membership until now?
We see the South Africa IIASA membership as a partnership, and many benefits have accrued through this partnership over the past decade. An example is the Southern African Young Scientists Summer Program (SA-YSSP), which was inspired by the success of the IIASA YSSP. This program ran from 2012 to 2015, and trained the next generation of young scientists.
Another example is the Southern African Systems Analysis Centre (SASAC) initiative, which focused on expanding systems analysis expertise in Southern Africa. Both initiatives were endorsed by the South African Department of Science and Innovation.
About NRF and Dorsamy (Gansen) Pillay
As an entity of the Department of Science and Technology (DST), the NRF promotes and supports research through funding, human resource development and the provision of National Research Facilities in all fields of natural and social sciences, humanities and technology. Dr Dorsamy (Gansen) Pillay is currently the Deputy Chief Executive Officer (DCEO): Research and Innovation Support and Advancement (RISA) of the National Research Foundation (NRF) of South Africa. His thirty-year career in research, teaching, management and leadership includes academic, management and leadership positions at the former University of Durban-Westville and at the Durban University of Technology. His research has focused on both prokaryotic and eukaryotic microorganisms, from human diseases to bacterial plant diseases with particular emphasis on elucidating the molecular architecture of the causal microorganisms with a view to understanding genetic diversity, extra-chromosomal elements and developing rapid disease diagnoses. He is currently Vice Chair of the IIASA Council.
Notes:
Please click on the link to read the statement on ethical research and scholarly publishing.
More information on IIASA and South Africa.
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.
Oct 1, 2019 | Alumni, IIASA Network, Systems Analysis
By Christoph E. Mandl, IIASA alumnus and Senior Lecturer at the University of Natural Resources and Life Sciences in Vienna
Apprehensive about ever growing crises of corporate and political governance, I wrote a book titled, Managing complexity in social systems: Leverage points for policy and strategy, that addresses these crises and appropriate actions from a complex systems, system dynamics, and systems thinking perspective. The premise of the book is that more and more policies and strategies tend to fail and it is based on my personal experiences and the stories of many policymakers.
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In her disconcerting booklet, The collapse of Western civilization: A view from the future Naomi Oreskes stated: “Analysts agree that the people of Western civilizations knew what was happening to them but were unable to stop it. Indeed, the most startling aspect of this collapse is just how much these people knew, and how unable they were to act upon what they knew.”
So, what can be done about this? How can the complexity of modern societies be managed? Naturally, answers to these questions are anything but trivial. Insights from complexity science, system dynamics, system theory, and systems thinking may not give a full answer but could perhaps point us in the right direction.
In writing my book aimed at closing these societal knowing-doing gaps, four IIASA alumni shaped and influenced my thinking:
The first was Thomas Schelling, who was key for me in showing how, in the context of segregation, a social system’s macro-behavior emerges that is quite different to the micro-motives of the individuals.
Brian Arthur’s book, Increasing returns and path dependence in the economy, revealed to me a totally new perspective on the dynamics of social systems where disequilibrium is not only possible, but normal.
Through John Sterman’s article Bathtub dynamics: Initial results of a systems thinking inventory, I understood how important the distinction between stocks and flows is for decision making in dynamic environments.
Lastly, when I first came across Donella Meadows’ article, Places to intervene in a system, its impact on me was profound. In my view, it was the first publication that addressed decision making from a strictly dynamic point of view. This article and her publication Chicken Little, Cassandra, and the real wolf, forever changed and inspired my thinking about what it means to manage and to make decisions.
Without the insights of these four outstanding IIASA alumni, my book would never have been written. Thank you, IIASA, for bringing them all to Laxenburg!
More updates from IIASA alumni or information on the IIASA network may be found here.
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.
Aug 8, 2019 | Data and Methods, Economics, IIASA Network, Russia
By Serguei Kaniovski, Economist with the Austrian Institute of Economic Research (WIFO)
Serguei Kaniovski and colleagues from IIASA and the Steklov Mathematical Institute of the Russian Academy of Sciences revisited a classic growth model in resource economics using recent advances in optimal control theory.
The late 1960s and early 1970s gave rise to Doomsday Models that predicted a collapse of Western Civilization under the pressure of over-population and environmental pollution. The very influential 1972 Club of Rome’s report on the “Limits to Growth” painted a gloomy picture, sparking an ongoing debate. One question was whether the scarcity of natural resources like fossil fuels would limit growth and cause a substantial decline in people’s standard of living.
The Doomsday reasoning was met with doubt by the economists of that time, leading the future Nobel Prize laureate and growth theorist, Robert Solow, to state that “the various Doomsday Models are worthless as science and as guides to public policy“. In a combined effort, economists developed a class of growth models with resource constraints. The conclusions they reached using the Dasgupta-Heal-Solow-Stiglitz (DHSS) modeling framework offered a more optimistic outlook.
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Economic applications have been well ahead of the mathematical theory used for identifying optimal economic policies, leaving some model solutions unexposed and some technical issues unsettled. The theory that allows us to identify optimal policies and describe the model dynamics was originally developed in the 1950s for engineering applications but has since become the main tool for analyzing economic growth models. These models however contain many features that are not standard to optimal control theory – a subfield of mathematics that deals with the control of continuously operating dynamic systems – which makes a fully rigorous analysis difficult. The key theoretical challenges are infinite planning horizons and nonstandard control constraints.
In our latest paper we offer a complete and rigorous analysis of the welfare-maximizing investment and depletion policies in the DHSS model with capital depreciation and arbitrary (decreasing, constant, and increasing) returns to scale. The investment policy specifies the portion of the final output to be invested in capital. A depletion policy says how fast a finite stock of exhaustible resources should be used. We prove the existence of a solution and characterize the behavior of solutions for all combinations of the model parameters using necessary rather than sufficient (Arrow’s theorem) optimality conditions.
In the main case of decreasing, constant, or weakly increasing returns to scale, the optimal investment and depletion policies converge to a constant share of output invested in capital and a constant rate of depletion of the natural resource. The optimal investment ratio decreases with the longevity of capital and impatience. The relationship between the optimal investment ratio and the output elasticity of produced capital is ambiguous. The performed analytical analysis identifies those relationships among model parameters that are critical to the optimal dynamics. In this, it differs from more conventional scenario-based approaches. From a practical point of view, application of the model to real data could be helpful for evaluating actual depletion and investment policies.
Strongly increasing returns to scale make it optimal to deplete the resource without investing in produced capital. Whether a zero-investment strategy is followed from the outset, from an instant of time, or asymptotically will depend on the sizes of the capital and resource stocks. In some special cases of increasing returns, welfare-maximizing investment and extraction policies may not exist under strong scale effects in resource use. This occurs when an initial stock of capital is small relative to the initial resource stock. It implies that it would have been impossible to formulate a welfare-maximizing policy in the early history of humanity, when produced capital was scarce and resources were abundant.
Reference
Aseev S, Besov K, & Kaniovski S (2019). Optimal Policies in the Dasgupta—Heal—Solow—Stiglitz Model under Nonconstant Returns to Scale. Proceedings of the Steklov Institute of Mathematics 304 (1): 74-109. [pure.iiasa.ac.at/15946]
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.
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