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#43 Modeling Covid19, with Michael Osthege & Thomas Vladeck

#43 Modeling Covid19, with Michael Osthege & Thomas Vladeck

FromLearning Bayesian Statistics


#43 Modeling Covid19, with Michael Osthege & Thomas Vladeck

FromLearning Bayesian Statistics

ratings:
Length:
82 minutes
Released:
Jul 8, 2021
Format:
Podcast episode

Description

Episode sponsored by Paperpile: https://paperpile.com/ (paperpile.com)
Get 20% off until December 31st with promo code GOODBAYESIAN21
I 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/ (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 (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
Links from the show:
Tom on Twitter: https://twitter.com/tvladeck (https://twitter.com/tvladeck)
Tom's newsletter: https://tvladeck.substack.com/ (https://tvladeck.substack.com/)
Michael on Twitter: https://twitter.com/theCake (https://twitter.com/theCake)
Michael on GitHub: https://github.com/michaelosthege (https://github.com/michaelosthege)
Rt Live dashboard: https://rtlive.de/global.html (https://rtlive.de/global.html)
Rt Live model tutorial: https://github.com/rtcovidlive/rtlive-global/blob/master/notebooks/Tutorial_model.ipynb (https://github.com/rtcovidlive/rtlive-global/blob/master/notebooks/Tutorial_model.ipynb)
Rt Live model code: https://github.com/rtcovidlive/rtlive-global (https://github.com/rtcovidlive/rtlive-global)
Estimating Rt: https://staff.math.su.se/hoehle/blog/2020/04/15/effectiveR0.html (https://staff.math.su.se/hoehle/blog/2020/04/15/effectiveR0.html)
Great resource on terminology: https://royalsociety.org/-/media/policy/projects/set-c/set-covid-19-R-estimates.pdf?la=en-GB&hash=FDFFC11968E5D247D8FF641930680BD6 (https://royalsociety.org/-/media/policy/projects/set-c/set-covid-19-R-estimates.pdf?la=en-GB&hash=FDFFC11968E5D247D8FF641930680BD6)
Using Hierarchical Multinomial Regression to Predict Elections in Paris districts: https://www.youtube.com/watch?v=EYdIzSYwbSw...
Released:
Jul 8, 2021
Format:
Podcast episode

Titles in the series (100)

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Paris. By day, I'm a data scientist and modeler at the https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)! This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy