Assessing Models and Simulations of Epidemic Infectious Diseases

The Data Exchange Podcast: Bruno Gonçalves on reading epidemic models and time-series analysis.

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In this episode of the Data Exchange I bring back Bruno Gonçalves, a data scientist working at the intersection of Data Science and Finance. Bruno was a guest on this podcast in April, when the COVID-19 cases were spiking in his home base in NYC. 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.  I wanted to bring him back to get an update on the mathematical models being used to model the global pandemic.

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We discussed how the epidemiological models have performed, and adjustments modelers may have made to their models. We also discussed efforts to augment models with alternative data sources (see “An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time”). We closed with a discussion of time-series modeling, and the use of machine learning and deep learning in time-series.

Here are a few of Bruno’s recent posts on CoVID-19:

Bruno was kind enough to assemble the following pointers to some of the references we discussed during our conversation:

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