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_33
Snippet: Since Q(t) does not follow from first principles and is highly dependent on local quarantine policies, we devised a neural network-based approach to approximate it. Recently, it has been shown that neural networks can be used as function approximators to recover unknown constitutive relationships in a system of coupled ordinary differential equations (Rackauckas et al. 2020 (Rackauckas et al. , 2019 . Following this principle, we represent Q(t) a.....
Document: Since Q(t) does not follow from first principles and is highly dependent on local quarantine policies, we devised a neural network-based approach to approximate it. Recently, it has been shown that neural networks can be used as function approximators to recover unknown constitutive relationships in a system of coupled ordinary differential equations (Rackauckas et al. 2020 (Rackauckas et al. , 2019 . Following this principle, we represent Q(t) as a n layer-deep neural network with weights W 1 , W 2 . . . W n , activation function r and the input vector U = (S(t), I(t), R(t), T (t)) as Q(t) = r (W n r (W n−1 . . . r (W 1 U ))) ≡ NN(W, U ) (4.11)
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