Neural Network aided quarantine control model estimation of global Covid-19 spread

Dandekar, Raj, and George Barbastathis. “Neural Network Aided Quarantine Control Model Estimation of Global Covid-19 Spread.” ArXiv:2004.02752 [Physics, q-Bio], Apr. 2020. arXiv.orghttp://arxiv.org/abs/2004.02752.

Used a neural network to augment a SEIR model (pg 2)

Quarantine Strength as a function of time

SEIR/SIR models assume “homogenous mixing among the subpopulations” (pg 8)

Model 1: W/O quarantine control (pg 8-9)

  • Initial conditions:
    • I_0=500 cases
    • S_0=11,000,000 (Pop. of Wuhan)
    • E_0=20 x I_0
    • R_0≈10
  • \beta, \lambda, and \gamma were optimized based off of available data
Model 1. Pg 3

Model 2: With quarantine control (pg 9)

  • Initial Conditions:
    • I_0=500 cases
    • S_0=11,000,000 (Pop. of Wuhan)
    • E_0=20 x I_0
    • R_0≈10
  • This model used a multilayer neural network to adjust quarantine strength over time.
Model 2. Pg 3
Model 2. Pg 5

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