Extra: COVID-19 Network Epidemiology with Michelle Girvan
In this short extra episode, Jim talks to Michelle Girvan about the network dynamics of COVID-19 spread, fat-tailed risks, unintuitive network insights, social distancing dynamics, efficiency vs robustness, challenges of modeling the backside of the curve, the need for testing, economic analysis, potential corporate roles, the Network Epidemiology Online Workshop Series, and more.
Episode Transcript
JRS: Extra: On COVID-19 Strategies with Robin Hanson
JRS: Extra: On COVID-19 Opportunities with Jessica Flack
Network Epidemiology Online Workshop Series
COMBINE Network Biology at UMD on YouTube
Michelle Girvan is an Associate Professor in the Department of Physics and the Institute for Physical Science and Technology at the University of Maryland, College Park. She is also a member of the External Faculty at the Santa Fe Institute. Her research operates at the intersection of statistical physics, nonlinear dynamics, and computer science and has applications to social, biological, and technological systems. More specifically, her work focuses on complex networks and often falls within the fields of computational biology and sociophysics. While some of the research is purely theoretical, Girvan has become increasingly involved in using empirical data to inform and validate mathematical models.
Episode Transcript
JRS: Extra: On COVID-19 Strategies with Robin Hanson
JRS: Extra: On COVID-19 Opportunities with Jessica Flack
Network Epidemiology Online Workshop Series
COMBINE Network Biology at UMD on YouTube
Michelle Girvan is an Associate Professor in the Department of Physics and the Institute for Physical Science and Technology at the University of Maryland, College Park. She is also a member of the External Faculty at the Santa Fe Institute. Her research operates at the intersection of statistical physics, nonlinear dynamics, and computer science and has applications to social, biological, and technological systems. More specifically, her work focuses on complex networks and often falls within the fields of computational biology and sociophysics. While some of the research is purely theoretical, Girvan has become increasingly involved in using empirical data to inform and validate mathematical models.
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