SingularityNET: The Coronavirus and Modeling of its Pandemic – Dr. Yaneer Bar-Yam – COVID-19 Simulation Summit
➡️ COVID-19 Simulation Summit Playlist: https://www.youtube.com/playlist?list=PLAJnaovHtaFR5puHCN4W_4o8cgIHdawDb
👀 About the speaker
Professor Yaneer Bar-Yam holds an SB and PhD in physics from MIT. Since the late 1980s he has contributed to founding the field of complex systems science, introducing fundamental mathematical rigor, real world application, and educational programs for new concepts and insights of this field.
His recent work quantitatively analyzes the origins and impacts of market crashes, social unrest, ethnic violence, military conflict and pandemics, the structure and dynamics of social networks, as well as the bases of creativity, panic, evolution and altruism.
He has advised the U.S. Government on global social unrest and the crises in Egypt and Syria, counterterrorism strategies, military force transformation, market regulation, delivery of disease prevention services and control of hospital infections. He regularly advises NGOs and corporations regarding their use of complex systems science.
He has authored more than 200 journal articles. His work on the causes of the global food crisis was cited among the top 10 scientific discoveries of 2011 by Wired magazine. His scientific visualizations received recognition as “best of” from Wired in 2011 and 2013, and from Motherboard in 2013.
International coverage of his work includes The New York Times, The Wall Street Journal, The Washington Post, The Guardian, The Sunday Times, Die Zeit, and Le Monde, among others.
On April 30th, 2020, the DAIA Foundation has organized an online COVID-19 Simulation Summit. The summit was focused on the use of agent based simulation models for more effective simulation of COVID-19 spread and evaluation of COVID-19 policies. Consisting of live video talks, Q&A sessions, and panel discussions,
the event gathered together scientists with insight and experience in simulation modeling of complex systems (especially but not only agent based modeling) and complex systems dynamics; along with scientists and physicians with specific insight into COVID-19 and related epidemiological issues.
The aim of the COVID-19 Simulation Summit is to gain understanding of
What tools and approaches should be used to provide the most accurate possible models of COVID-19 and its spread and implications, both in the current phase and in future phases of disease propagation?
What assumptions should be made or questioned within COVID-19 simulation models?
What assumptions have been left unquestioned in current models and need to be more thoroughly explored?
What data sources might best be leveraged in constructing models related to COVID-19?
What data should we be gathering that we aren’t currently, and how may this be done in a way that respects personal privacy and data sovereignty?
What real-world test cases might best be used for initial exploration of more accurate simulation models
The epidemiological models of COVID-19 spreading dynamics that are driving current policy decisions are generally well thought out and carefully implemented, however they also tend to be highly simplistic relative to the complexity of the actual situation.
Among many other factors, they don’t generally factor in the impacts of various interventions and control policies, nor do they account for the different behavior patterns of different classes of people.
The agent based simulation paradigm allows a finer-grained sort of modeling, in which a region (or the world as a whole) is modeled as a specific geometry occupied by interacting autonomous agents with a diversity of specific behavior patterns. An in-depth agent based simulation of COVID-19 spreading would allow better-grounded policy choices to be made regarding how to manage, control and cope with the pandemic.
An agent based simulation, like any other model, depends on the underlying assumptions used to structure it. However, the agent based modeling paradigm provides a more flexible approach to evaluating the consequences of various assumptions, and thus exploring their validity.
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