Epidemic Modeling using Hybrid of Time-varying SIRD, Particle Swarm
  Optimization, and Deep Learning
Naresh Kumar Seba Susan
Epidemiological models are best suitable to model an epidemic if the spread
pattern is stationary. To deal with non-stationary patterns and multiple waves
of an epidemic, we develop a hybrid model encompassing epidemic modeling,
particle swarm optimization, and deep learning. The model mainly caters to
three objectives for better prediction: 1. Periodic estimation of the model
parameters. 2. Incorporating impact of all the aspects using data fitting and
parameter optimization 3. Deep learning based prediction of the model
parameters. In our model, we use a system of ordinary differential equations
(ODEs) for Susceptible-Infected-Recovered-Dead (SIRD) epidemic modeling,
Particle Swarm Optimization (PSO) for model parameter optimization, and
stacked-LSTM for forecasting the model parameters. Initial or one time
estimation of model parameters is not able to model multiple waves of an
epidemic. So, we estimate the model parameters periodically (weekly). We use
PSO to identify the optimum values of the model parameters. We next train the
stacked-LSTM on the optimized parameters, and perform forecasting of the model
parameters for upcoming four weeks. Further, we fed the LSTM forecasted
parameters into the SIRD model to forecast the number of COVID-19 cases. We
evaluate the model for highly affected three countries namely; the USA, India,
and the UK. The proposed hybrid model is able to deal with multiple waves, and
has outperformed existing methods on all the three datasets.