Summary of Epidemic Modeling Using Hybrid Of Time-varying Sird, Particle Swarm Optimization, and Deep Learning, by Naresh Kumar et al.
Epidemic Modeling using Hybrid of Time-varying SIRD, Particle Swarm Optimization, and Deep Learning
by Naresh Kumar, Seba Susan
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); Physics and Society (physics.soc-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel hybrid epidemiological model is introduced that effectively handles non-stationary spread patterns and multiple waves of an epidemic. This model combines system of ordinary differential equations (ODEs) for Susceptible-Infected-Recovered-Dead (SIRD) modeling, particle swarm optimization (PSO) for parameter optimization, and stacked-LSTM for forecasting. The model’s three objectives are: periodic estimation of parameters, incorporation of multiple factors through data fitting and parameter optimization, and deep learning-based prediction of parameters. By periodically estimating and optimizing parameters using PSO, the model accurately forecasts COVID-19 cases in highly affected countries like the USA, India, and the UK. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict how diseases spread is developed. This method uses a combination of math, computer science, and machine learning to make more accurate predictions. The goal is to understand how diseases move through populations over time. The model looks at three things: what’s happening now, what happened in the past, and what might happen in the future. It also takes into account many different factors that can affect how a disease spreads. This method was tested on data from countries like the USA, India, and the UK and showed that it can be more accurate than other methods. |
Keywords
* Artificial intelligence * Deep learning * Lstm * Machine learning * Optimization
