Nonlinear control of Covid-19 pandemic based on the SIRD model

Ghadami, Seyyed Mostafa; Seyyedi, Seyyed Masoud; Rituraj, R.

doi:10.1109/sisy56759.2022.10036274

Cited by 1 publication

(1 citation statement)

References 61 publications

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“…Many extensions of SIRD epidemic model have been proposed to model the COVID-19 pandemic. Most of the epidemic models assume that relevant model parameters, e.g., the infection rate and the recovery rate are time-invariant, and several approaches have been proposed in the literature for tuning or estimating them [10], [29]. Fanelli and Piazza [6] have analysed the COVID-19 spread dynamics in China, France, and Italy between 22 Jan, 2020 to 15 March, 2020.…”

Section: Related Workmentioning

confidence: 99%

Epidemic Modeling using Hybrid of Time-varying SIRD, Particle Swarm Optimization, and Deep Learning

Kumar,

Susan

2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Epidemiological models are best suitable to model an epidemic if the spread pattern is stationary. To deal with nonstationary 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.

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Section: Related Workmentioning

confidence: 99%