Covid-19 expert Karl Friston: 'Germany may have more immunological "dark matter"' | Wor... - 0 views
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Our approach, which borrows from physics and in particular the work of Richard Feynman, goes under the bonnet. It attempts to capture the mathematical structure of the phenomenon – in this case, the pandemic – and to understand the causes of what is observed. Since we don’t know all the causes, we have to infer them. But that inference, and implicit uncertainty, is built into the models
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That’s why we call them generative models, because they contain everything you need to know to generate the data. As more data comes in, you adjust your beliefs about the causes, until your model simulates the data as accurately and as simply as possible.
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A common type of epidemiological model used today is the SEIR model, which considers that people must be in one of four states – susceptible (S), exposed (E), infected (I) or recovered (R). Unfortunately, reality doesn’t break them down so neatly. For example, what does it mean to be recovered?
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