The Data Exchange Podcast: Bruno Gonçalves on the world of epidemic models and the role they play in decision making.
In this episode of the Data Exchange I speak with Bruno Gonçalves, a data scientist working at the intersection of Data Science and Finance. I have known Bruno for several years and we met when I recruited him to teach several extremely popular conference tutorials and talks on machine learning and deep learning. Prior to shifting over to data science, he spent several years as a researcher focused on mathematical models in Epidemiology – a field with a rich history dating as far back as the 1920s. This episode is devoted to tools and techniques for modeling epidemics.
Our conversation covered:
- Bruno’s background and his experience in modeling epidemics.
- The field of epidemic models: what techniques are used, the size of the community of researchers, and how do models get evaluated.
- His two recent posts: “Epidemic Modeling 101 – Or why your CoVID-19 exponential fits are wrong” and “Epidemic Modeling 102 – All CoVID-19 models are wrong, but some are useful”
- The role that epidemic models play in decision making.
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For more on Epidemic Modeling, Bruno Gonçalves kindly sent over links to some of the leading researchers in the field:
- MOBS LAB group at Northeastern University headed by Alessandro Vespignani, the brains behind the large scale “GLEaM” platform.
- Vittoria Colizza at the Sorbonne in Paris.
- Betz Halloran focuses on modeling vaccination campaigns.
- Stefano Merler studies large scale agent based models (individually simulates the entire european union population).
- Ira Longini
- Cecile Viboud