#120 Innovations in Infectious Disease Modeling, with Liza Semenova & Chris Wymant

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Takeaways:

• Epidemiology focuses on health at various scales, while biology often looks at micro-level details.
• Bayesian statistics helps connect models to data and quantify uncertainty.
• Recent advancements in data collection have improved the quality of epidemiological research.
• Collaboration between domain experts and statisticians is essential for effective research.
• The COVID-19 pandemic has led to increased data availability and international cooperation.
• Modeling infectious diseases requires understanding complex dynamics and statistical methods.
• Challenges in coding and communication between disciplines can hinder progress.
• Innovations in machine learning and neural networks are shaping the future of epidemiology.
• The importance of understanding the context and limitations of data in research. 

Chapters:

00:00 Introduction to Bayesian Statistics and Epidemiology

03:35 Guest Backgrounds and Their Journey

10:04 Understanding Computational Biology vs. Epidemiology

16:11 The Role of Bayesian Statistics in Epidemiology

21:40 Recent Projects and Applications in Epidemiology

31:30 Sampling Challenges in Health Surveys

34:22 Model Development and Computational Challenges

36:43 Navigating Different Jargons in Survey Design

39:35 Post-COVID Trends in Epidemiology

42:49 Funding and Data Availability in Epidemiology

45:05 Collaboration Across Disciplines

48:21 Using Neural Networks in Bayesian Modeling

51:42 Model Diagnostics in Epidemiology

55:38 Parameter Estimation in Compartmental Models

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström and Stefan.

Links from the show:

• LBS #21, Gaussian Processes, Bayesian Neural Nets & SIR Models, with Elizaveta Semenova: https://learnbayesstats.com/episode/21-gaussian-processes-bayesian-neural-nets-sir-models-with-elizaveta-semenova/
• Liza’s website: https://www.elizaveta-semenova.com/
• Liza on GitHub: https://github.com/elizavetasemenova
• Liza on LinkedIn: https://www.linkedin.com/in/elizaveta-semenova/
• Liza on Google Scholar: https://scholar.google.com/citations?user=jqGIgFEAAAAJ&hl=en
• Chris' page: https://www.bdi.ox.ac.uk/Team/c-wymant
• Chris on GitHub: https://github.com/chrishiv
• Chris on LinkedIn: https://www.linkedin.com/in/chris-wymant-65661274/
• Chris on Blue Sky: https://bsky.app/profile/chriswymant.bsky.social
• Chris on Google Scholar: https://scholar.google.com/citations?user=OJ6t2UwAAAAJ&hl=en
• PriorVAE Paper: Explains how to build an emulator for a GP using a deep generative model (Variational Autoencoder, or VAE) and apply it within MCMC. Link to the paper
• PriorCVAE Paper: Builds on PriorVAE by encoding model parameters along with emulating stochastic process realisations. Includes examples for GPs, ODEs, and double-well models. Link to the paper
• StanCon 2024 Tutorial: A tutorial covering the basics of sequential decision-making, with a demo of Bayesian Optimization using Stan. Link to the tutorial
• Numpyro Course: Materials from a course Liza taught -- great for learning Numpyro. Link to the course
• aggVAE Paper: An application of PriorVAE to the problem of changing boundaries. Link to the paper

Transcript

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