Forecasting Covid-19 Trends Utilizing Linear Regression for Predictive Modelling Of Daily Incidence, Mortality and Recovery Rates - Saloni Bharat Gangar; Pratibha C. Kaladeep (Pratibha S. Yalagi) (International Journal of Scientific Research in Science, Engineering and Technology, 2025)
The dissemination of the Coronavirus across various regions worldwide has led to a substantial number of fatalities and has triggered a decline in the global economy. This situation continues to serve as a significant warning regarding public health and is recognized as one of the major pandemics in the annals of history. The purpose of this project is to offer an in-depth exploration of how different Machine Learning models can be effectively utilized in practical situations. Forecasting techniques based on data analytics have proven their value in predicting perioperative outcomes, which aids in making informed decisions regarding future actions. Regression models in data analysis have been applied in numerous contexts that necessitate the identification and highlighting of adverse factors that pose risks. Several predictive methodologies are primarily utilized to tackle forecasting issues. This research showcases the potential of models to estimate the number of forthcoming COVID-19 patients, which is currently viewed as a significant threat to public health. In particular, linear regression, a standard forecasting model, has been employed in this study to assess the cautionary factors related to COVID-19. The regression analysis models yield three distinct predictions: the anticipated number of new cases, recoveries, and deaths over the next 14 day
Keywords: COVID-19; Machine learning; Models; Prediction; Time series forecasting; linear Regression; Database; algorithm