SingularityNET: COVID-19: A Front-Line Physician's Perspective – Ray & Roger Ng, MDs – COVID-19 Simulation Summit

➑️ COVID-19 Simulation Summit Playlist:
πŸ‘€ About the speakers
Roger Ng, MD is cardiologist board certified in cardiovascular diseases and internal medicine passionate about bridging technology applications and AI for healthcare. He specializes in diagnostic imaging and treatment of congenital heart disease, valvular heart disease, coronary heart disease, heart failure, and arrhythmias. He is trained in cardiac imaging with multiple modalities including nuclear cardiology, echocardiography, coronary angiography, and coronary cardiac CT.

Ray Ng, MD was born a polymath and entrepreneur: physician by day and avid technology geek, coder, and blockchain developer by night. MD with board certification and licensure in Canada and US. Ray is the former owner of a family medical practice in Vancouver, BC. With a passion for entrepreneurship, healthtech innovation, and improving access to medical knowledge and insights, Ray’s experience and background falls beyond the norm of a conventional doctor. Ray has now returned to his roots on the West Coast of Canada to re-focus on his medical practice and study mindfulness, meditation, and other holistic forms of wellness that compliment his board certification in Integrative Medicine.

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
Agent-Based Simulation
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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