Revolutionizing COVID-19 Case Forecasting through Machine Learning

This study presents an innovative framework for predicting COVID-19 cases in India at the state level, leveraging machine learning techniques. With the COVID-19 pandemic posing a significant public health challenge, ac-curate and timely predictions are crucial for effective decision-making. Our framework combines historical COVID-19 data, state-specific features, and machine-learning models to provide real-time forecasts. Results indicate that this approach can be a valuable tool for policymakers, enabling them to allocate resources and implement targeted interventions to combat the virus. In response to the unprecedented challenges posed by the COVID-19 pandemic in India, this study delves into the transformative potential of machine learning for state-wise COVID-19 case forecasting. India’s diverse population, variable healthcare infrastructure, and regional disparities necessitate a tailored approach for pre-case predictions. Leveraging extensive datasets and advanced machine learning algorithms, this research addresses the demand for real-time, accurate forecasting at the state level. This research study provides insights into the background, data sources, methodology, and significant results achieved. Our findings demonstrate that machine learning models significantly enhance accuracy in state-wise COVID-19 predictions, enabling timely and targeted public health interventions, optimizing resource allocation, and supporting data-driven policy decisions. In summary, this research underscores the power of machine learning in revolutionizing COVID-19 forecasting, offering a critical tool for more effective pandemic management in India. The results of our study reveal promising performance in predicting COVID-19 cases state-wise in India. Machine learning models exhibit a high degree of accuracy in forecasting, with low root mean square error (RMSE) and strong R-squared values. Our models capture the temporal dynamics and variations between states, making them suitable for re-al-time monitoring and decision support.