Reconstructing epidemic cascades with autoregressive neural networks
In the case of inference of epidemic spreading of a disease, we usually have missing or partial information on an epidemic cascade.
In order to understand, characterize and mitigate the spreading of diseases, it is fundamental to reconstruct the missed information. In this talk, the presenter will discuss an approach, based on a Bayesian framework, in which a model governing the spreading process and the contacts between individuals are given.
This approach employs generative neural networks (Autoregressive Neural Network, ANN) to find the most probable epidemic cascades compatible with the given observations. It can be applied to several problems in epidemics, such as discovering the patient zero, or performing epidemic risk assessment. It is also possible, given the Bayesian nature of our approach, to infer the parameters of the epidemic model. In this talk, we will also show the performance of our framework in both synthetic cases and real case scenario.
Presenter: Fabio Mazza
Location: Microsoft Teams – click here to join
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