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Hi All,

At Hadean we’ve been working with a predictive digital twin for epidemiology, and modelling the spread of COVID-19. Part of our work has leveraged the Hadean platform to quickly calibrate our model so infection dynamics match the real-world’s. We’ve written up the work that we have done so far in a blog page that I wanted to share: https://hadean.com/blog/modelling-an-evolving-pandemic-keeping-your-digital-twin-calibrated-and-in-sync-with-the-data/

We used Approximate Bayesian Computation, ABC, to ensure that an initial variant had the same infectivity as that found from official case statistics, and then recalibrated as a new strain emerged. For epidemiology, in-particular, we’ve found that it’s critical to keep a model up-to-date with the real world so that predictions are meaningful at point-of-use.

I’m interested in how others keep their predictive digital twins up-to-date. Do you calibrate and synchronise your digital twins? If so, how? If not, why not?

Ping me an email if you’d like a chat -  daniel.gorringe@hadean.com

Edited by Dan
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