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Explaining the Precautionary Principle and the need for overreaction under certain classes of multiplicative systemic risk.
We discuss common errors and fallacies when using naive "evidence based" empiricism and point forecasts for fat-tailed variables, as well as the insufficiency of using naive first-order scientific methods for tail risk management. We use the COVID-19 pandemic as the background for the discussion and as an example of a phenomenon characterized by a multiplicative nature, and what mitigating policies must result from the statistical properties and associated risks. In doing so, we also respond to the points raised by Ioannidis et al.(2020)
Co-authored piece with Nassim Taleb, Yaneer Bar-Yam... We present a non-naive version of the Precautionary (PP) that allows us to avoid paranoia and paralysis by confining precaution to specific domains and problems. PP is intended to deal with uncertainty and risk in cases where the absence of evidence and the incompleteness of scientific knowledge carries profound implications and in the presence of risks of "black swans", unforeseen and unforeseable events of extreme consequence. We formalize PP, placing it within the statistical and probabilistic structure of ruin problems, in which a system is at risk of total failure, and in place of risk we use a formal fragility based approach. We make a central distinction between 1) thin and fat tails, 2) Local and systemic risks and place PP in the joint Fat Tails and systemic cases. We discuss the implications for GMOs (compared to Nuclear energy) and show that GMOs represent a public risk of global harm (while harm from nuclear energy, while potentially severe, does not present such a global threat of ruin: it is comparatively limited and better characterized otherwise than as ruinous). PP should be used to prescribe severe limits on GMOs.
Using methods from extreme value theory, we examine the major pandemics in history, trying to understand their tail properties. Applying the shadow distribution approach developed by the authors for violent conflicts [5], we provide rough estimates for quantities not immediately observable in the data. Epidemics and pandemics are extremely heavy-tailed, with a potential existential risk for humanity. This property should override conclusions derived from local epidemiological models in what relates to tail events.
BMC Public Health
Using GAM functions and Markov-Switching models in an evaluation framework to assess countries' performance in controlling the COVID-19 pandemicBackground: The COVID-19 pandemic has initiated several initiatives to better understand its behavior, and some projects are monitoring its evolution across countries, which naturally leads to comparisons made by those using the data. However, most "at a glance" comparisons may be misleading because the curve that should explain the evolution of COVID-19 is different across countries, as a result of the underlying geopolitical or socioeconomic characteristics. Therefore, this paper contributes to the scientific endeavour by creating a new evaluation framework to help stakeholders adequately monitor and assess the evolution of COVID-19 in countries, considering the occurrence of spikes, "secondary waves" and structural breaks in the time series. Methods: Generalized Additive Models were used to model cumulative and daily curves for confirmed cases and deaths. The Root Relative Squared Error and the Percentage Deviance Explained measured how well the models fit the data. A local min-max function was used to identify all local maxima in the fitted values. The pure Markov-Switching and the family of Markov-Switching GARCH models were used to identify structural breaks in the COVID-19 time series. Finally, a quadrants system to identify countries that are more/less efficient in the short/long term in controlling the spread of the virus and the number of deaths was developed. Such methods were applied in the time series of 189 countries, collected from the Centre for Systems Science and Engineering at Johns Hopkins University. Results: Our methodology proves more effective in explaining the evolution of COVID-19 than growth functions worldwide, in addition to standardizing the entire estimation process in a single type of function. Besides, it highlights several inflection points and regime-switching moments, as a consequence of people's diminished commitment to fighting the pandemic. Although Europe is the most developed continent in the world, it is home to most countries with an upward trend and considered inefficient, for confirmed cases and deaths. Conclusions: The new outcomes presented in this research will allow key stakeholders to check whether or not public policies and interventions in the fight against COVID-19 are having an effect, easily identifying examples of best practices and promote such policies more widely around the world.
Environmental Science and Policy
The Law of Regression to the Tail: How to Survive Covid-19, the Climate Crisis, and Other Disasters2020 •
Regression to the mean is nice and reliable. Regression to the tail is reliably scary. We live in the age of regression to the tail. It is only a matter of time until a pandemic worse than covid-19 will hit us, and climate more extreme than any we have seen. What are the basic principles that generate such extreme risk, and for navigating it, for government, business, and the public?
Epidemiology and Infection
Drawing transmission graphs for COVID-19 in the perspective of network scienceWhen we consider a probability distribution about how many COVID-19-infected people will transmit the disease, two points become important. First, there could be super-spreaders in these distributions/networks and second, the Pareto principle could be valid in these distributions/networks regarding estimation that 20% of cases were responsible for 80% of local transmission. When we accept that these two points are valid, the distribution of transmission becomes a discrete Pareto distribution, which is a kind of power law. Having such a transmission distribution, then we can simulate COVID-19 networks and find super-spreaders using the centricity measurements in these networks. In this research, in the first we transformed a transmission distribution of statistics and epidemiology into a transmission network of network science and second we try to determine who the super-spreaders are by using this network and eigenvalue centrality measure. We underline that determination of transmis...
2021 •
ABSRACT The epidemiology has recently witnessed great advances based on computational models. Its scope and impact are getting wider thanks to the new data sources feeding analytical frameworks and models. Besides traditional variables considered in epidemiology, large-scale social patterns can be now integrated in real time with multi-source data bridging the gap between different scales. In a hyper-connected world, models and analysis of interactions and social behaviors are key to understand and stop outbreaks. Big Data along with apps are enabling for validating and refining models with real world data at scale, as well as new applications and frameworks to map and track diseases in real time or optimize the necessary resources and interventions such as testing and vaccination strategies. Digital epidemiology is positioning as a discipline necessary to control epidemics and implement actionable protocols and policies. In this review we address the research areas configuring curr...
2020 •
In the COVID-19 crisis, compartmental models have been largely used to predict the macroscopic dynamics of infections and deaths and to assess different non-pharmaceutical interventions aimed to contain the microscopic dynamics of person-to-person contagions. Evidence shows that the predictions of these models are affected by high levels of uncertainty. However, the link between predictions and interventions is rarely questioned and a critical scrutiny of the dependency of interventions on model assumptions is missing in public debate. In this article, I have examined the building blocks of compartmental epidemic models so influential in the current crisis. A close look suggests that these models can only lead to one type of intervention, i.e., interventions that indifferently concern large subsets of the population or even the overall population. This is because they look at virus diffusion without modelling the topology of social interactions. Therefore, they cannot assess any targeted interventions that could surgically isolate specific individuals and/or cutting particular person-to-person transmission paths. If complex social networks are seriously considered, more sophisticated interventions can be explored that apply to specific categories or sets of individuals with expected collective benefits. In the last section of the article, I sketch a research agenda to promote a new generation of network-driven epidemic models.
2020 •
European Journal of Futures Research
Long-term cost-effectiveness of interventions for loss of electricity/industry compared to artificial general intelligence safetyTÜBA Assessment Report on COVID-19 Global Outbreak
TÜBA Assessment Report on COVID-19 Global Outbreak2020 •
Humanities and Social Sciences Communications
Science communication as a preventative tool in the COVID19 pandemic2020 •
Ikatan Pustakawan Indonesia
COVID-19 : Sumber dan Rujukan Karya Ilmiah2020 •
2020 •
The Central European Review of Economics and Management
COVID-19 – reflections on the surprise of both an expected and unexpected event2021 •
Church, Communication and Culture
British government communication during the 2020 COVID-19 pandemic: learning from high reliability organizationsEnvironmental Chemistry Letters
COVID-19 transmission, vulnerability, persistence and nanotherapy: a reviewJournal of Physics: Complexity
Beyond COVID-19: network science and sustainable exit strategiesTechnological Forecasting and Social Change
The struggle SARS-CoV-2 vs. homo sapiens–Why the earth stood still, and how will it keep moving on?Golden Meteorite Press
Under the Weather: COVID-19 Biosocial System Dynamics2020 •
Bulletin of the Atomic Scientists
Assessing the US government response to the coronavirusWildlife and Emerging Zoonotic …
Introduction: conceptualizing and partitioning the emergence process of zoonotic viruses from wildlife to humans2007 •
Reviews in Cardiovascular Medicine
Pandemic lockdown, healthcare policies and human rights:integrating opposed views on COVID-19 public healthmitigation measuresInternational Journal of Engineering Research and Technology (IJERT)
IJERT-A Survey on Forecasting Models for Corona Virus (Covid-19)2021 •
Medical Hypotheses
Airborne route and bad use of ventilation systems as non-negligible factors in SARS-CoV-2 transmission2020 •
Infection Control & Hospital Epidemiology
COVID-19 Research Agenda for Healthcare Epidemiology13 Perspectives on the pandemic Thinking in a state of exception
Antisemitism on Social Media in Times of Corona2020 •
Journal of Evolution of Medical and Dental Sciences
Quarantine Exercises in the Time of Covid-19- A ReviewThe World After the Pandemic-Science&Technology
The World After the Pandemic - Science & Technology2021 •