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A machine learning forecasting model for COVID-19 pandemic in India
R Sujath1, Jyotir Moy Chatterjee2, Aboul Ella Hassanien3
1Vellore Institute of Technology, Vellore, India.
Stochastic Environmental Research and Risk Assessment : Research Journal
|August 25, 2020
Summary
This study presents a predictive model for COVID-19 spread in India using regression and machine learning. The model forecasts disease trends based on confirmed, death, and recovered cases, aiding future planning.
Area of Science:
- Epidemiology
- Computational Biology
- Public Health
Background:
- Coronavirus disease (COVID-19) is a global pandemic causing respiratory illness.
- Predictive modeling is crucial for understanding and managing epidemic spread.
- Existing models may have inherent biases.
Purpose of the Study:
- To develop and present a novel model for predicting the spread of COVID-19 in India.
- To forecast the epidemiological patterns and case progression of COVID-19.
- To utilize available data for estimating future trends.
Main Methods:
- Application of linear regression, Multilayer Perceptron, and Vector Autoregression.
- Utilizing COVID-19 Kaggle dataset specific to India.
- Analysis based on confirmed, death, and recovered case data over time.
Main Results:
- The study successfully predicted potential trends of COVID-19 impact in India.
- The chosen methods provided insights into the epidemiological dynamics of the disease.
- Forecasting capabilities were demonstrated using historical case data.
Conclusions:
- The presented model can assist in predicting the future trajectory of COVID-19 in India.
- Consistent data collection and case definitions are vital for accurate epidemiological forecasting.
- The findings support data-driven strategies for pandemic management.
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