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A Deep Learning Method to Forecast COVID-19 Outbreak.
Satyabrata Dash1, Sujata Chakravarty2, Sachi Nandan Mohanty3
1Department of Computer Science and Engineering, Ramachandra College of Engineering, Eluru, Andhra Pradesh India.
New Generation Computing
|July 26, 2021
Summary
This study compares Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) networks for predicting pandemic behavior. LSTM models demonstrated more realistic pandemic predictions, particularly in the Indian context.
Area of Science:
- Epidemiology
- Computational Science
- Machine Learning
Background:
- The COVID-19 pandemic, emerging in late 2019, has profoundly disrupted global lifestyles.
- Accurately predicting pandemic trajectories and end-points remains challenging due to evolving parameters.
Purpose of the Study:
- To develop and compare prediction models for pandemic behavior using machine learning techniques.
- To evaluate the efficacy of Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) networks for pandemic simulation.
Main Methods:
- Support Vector Regression (SVR) was employed, utilizing a function to estimate input-to-real number mapping based on a training model.
- Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN), were utilized for their ability to learn long-term dependencies.
- Both SVR and LSTM techniques were applied to simulate the behavior of the pandemic.
Main Results:
- Simulation results indicated that LSTM models provided more realistic pandemic behavior predictions.
- The study specifically highlights the effectiveness of LSTM in the Indian scenario.
Conclusions:
- LSTM networks offer a promising approach for accurate pandemic prediction due to their capacity for learning long-term dependencies.
- The findings suggest LSTM as a valuable tool for understanding and forecasting pandemic dynamics, especially in specific regional contexts like India.
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