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.org, http://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
,
, and
were optimized based off of available data
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.
