On real-time calibrated prediction for complex model-based decision support in pandemics: Part 2

2025-05-16 infectious diseases Title + abstract only

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Calibration of complex stochastic infectious disease models is challenging. These often have high-dimensional input and output spaces, with the models exhibiting complex, non-linear dynamics. Coupled with a paucity of necessary data, this results in a large number of non-ignorable hidden states that must be handled by the inference routine. Likelihood-based approaches to this missing data problem are very flexible, but challenging to scale, due to having to monitor and update these hidden states...

On real-time calibrated prediction for complex model-based decision support in pandemics: Part 2

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