International Journal on Science and Technology

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COVID-19 Prediction and Forecasting Using Machine Learning

Author(s) B Prathima, Dr. K. G. Chiranjeevi
Country India
Abstract The COVID-19 pandemic has underscored the need for accurate predictive tools to manage pub- lic health crises. This study explores the application of supervised machine learning (ML) techniques, specifically Linear Regression (LR) and Support Vector Machines (SVM), to forecast COVID-19 cases. Utilizing a global dataset of daily confirmed, recovered, and death cases from January 2020 onwards, the research preprocesses the data and trains models to predict trends over a 10-day horizon. Results in- dicate that LR provides consistent and reliable forecasts, estimating an average daily increase of 29,900 confirmed cases globally, while SVM struggles with data fluctuations. These findings highlight the po- tential of ML in enhancing pandemic response strategies, offering actionable insights for healthcare authorities. The study concludes that LR is more suitable for short-term COVID-19 forecasting due to its simplicity and interpretability.
Keywords Machine Learning, COVID-19, Linear Regression, Support Vector Machine, Forecasting, Public Health
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-22
Cite This COVID-19 Prediction and Forecasting Using Machine Learning - B Prathima, Dr. K. G. Chiranjeevi - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2679
DOI https://doi.org/10.71097/IJSAT.v16.i1.2679
Short DOI https://doi.org/g892fk
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