82 minutes | Jul 8, 2021

#43 Modeling Covid19, with Michael Osthege & Thomas Vladeck

Episode sponsored by Paperpile: paperpile.comGet 20% off until December 31st with promo code GOODBAYESIAN21I don’t know if you’ve heard, but there is a virus that took over most of the world in the past year? I haven’t dedicated any episode to Covid yet. First because research was moving a lot — and fast. And second because modeling Covid is very, very hard.But we know more about it now, so I thought it was a good time to pause and ponder — how does the virus circulate? How can we model it and, ultimately, defeat it? What are the challenges in doing so?To talk about that, I had the chance to host Michael Osthege and Thomas Vladeck, who both were part of the team who developed the Rt-live model, a Bayesian model to infer the reproductive rate of Covid19 in the general population. As you’ll hear, modeling the evolution of this virus is challenging, fascinating, and a perfect fit for Bayesian modeling! It truly is a wonderful example of Bayesian generative modeling.Tom is the Managing Director of Gradient Metrics, a quantitative market research firm, and a Co-Founder of Recast, a media mix model for modern brands.Michael is a PhD student in laboratory automation and bioprocess optimization at the Forschungszentrum Jülich in Germany, and a fellow PyMC core-developer. As he works a lot on the coming brand new version 4, we’ll take this opportunity to talk about the current developments and where the project is headed.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode and Patrick Kelley.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Tom on Twitter: https://twitter.com/tvladeckTom's newsletter: https://tvladeck.substack.com/Michael on Twitter: https://twitter.com/theCakeMichael on GitHub: https://github.com/michaelosthegeRt Live dashboard: https://rtlive.de/global.htmlRt Live model tutorial: https://github.com/rtcovidlive/rtlive-global/blob/master/notebooks/Tutorial_model.ipynbRt Live model code: https://github.com/rtcovidlive/rtlive-globalEstimating Rt: https://staff.math.su.se/hoehle/blog/2020/04/15/effectiveR0.htmlGreat resource on terminology: https://royalsociety.org/-/media/policy/projects/set-c/set-covid-19-R-estimates.pdf?la=en-GB&hash=FDFFC11968E5D247D8FF641930680BD6Using Hierarchical Multinomial Regression to Predict Elections in Paris districts: https://www.youtube.com/watch?v=EYdIzSYwbSwLBS #34, Multilevel Regression, Post-stratification & Missing Data, with Lauren Kennedy: https://www.learnbayesstats.com/episode/34-multilevel-regression-post-stratification-missing-data-lauren-kennedy mrmp - Multilevel Regression and Marginal Poststratification: https://rdrr.io/github/jwyatt85/MRmP/man/mrmp.htmlAutomating daily runs for rt.live’s COVID-19 data using Airflow & ECS: https://medium.com/@mikekrieger/automating-daily-runs-for-rt-lives-covid-19-data-dcda26ed2e2eLBS #23, Bayesian Stats in Business and Marketing Analytics, with Elea McDonnel Feit: https://www.learnbayesstats.com/episode/23-bayesian-stats-in-business-and-marketing-analytics-with-elea-mcdonnel-feitThis podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy
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