Publication Date: 2023/06/30
Abstract: COVID-19 is a human-to-human transmissible virus responsible for damage to the human body, and people died all over the world. Bangladesh was affected by COVID-19 on March 8th, 2020. During the pandemic, people and the government struggled to prevent transmission due to an inadequate supply of vaccines and healthcare equipment. Therefore, it is essential to understand the upcoming infected cases for several days. That may help people and the government make pre- decision before the pandemic to save live. In this paper, we proposed a COVID-19 short-term forecasting model using Linear Regression (LR), Least Absolute Shrinkage and Selection Operation (LASSO) Regression, and Support Vector Regression (SVR) to predict the next seven days of COVID-19 infected cases in Bangladesh during the pandemic situation. Here we considered data from 8th May 2021 to 21st July 2021. We analyzed different past data volumes for the model to understand the impact of past data in the model. The result reveals that Support Vector Regression (SVR) performance was better than LR and LASSO in all aspects with high accuracy. The performance also indicated that the high volume of past data helps to increase prediction accuracy.
Keywords: COVID-19, Short-term Forecast, New infected cases, Bangladesh, Supervised Machine Learning, LR, LASSO, SVR.
DOI: https://doi.org/10.5281/zenodo.8098699
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23JUN1007.pdf
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