Author: Raj Dandekar; George Barbastathis
Title: Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning Document date: 2020_4_6
ID: 222c1jzv_3
Snippet: Since neural networks can be used to approximate nonlinear functions with a finite set of parameters, they serve as a powerful tool to approximate quarantine effects in combination with the analytical epidemiological models. The downside is that the internal workings of a neural network are difficult to interpret. The recently emerging field of Physics-Informed Neural Networks (Raissi et al. 2019 ) exploits conservation principles, SIR in our cas.....
Document: Since neural networks can be used to approximate nonlinear functions with a finite set of parameters, they serve as a powerful tool to approximate quarantine effects in combination with the analytical epidemiological models. The downside is that the internal workings of a neural network are difficult to interpret. The recently emerging field of Physics-Informed Neural Networks (Raissi et al. 2019 ) exploits conservation principles, SIR in our case, to mitigate overfitting and other related machine learning risks.
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