A Comprehensive System for COVID-19 Analysis Combining Data Collection, Machine Learning, and Forecasting Models

Abstract

The COVID-19 pandemic has created an urgent need for effective systems to monitor, detect, and predict the spread of the virus. This study proposes an integrated data-driven framework that combines real-time data collection, machine learning, and epidemiological modeling for comprehensive COVID-19 analysis. Data is collected using web scraping techniques from reliable sources and preprocessed for model development. Convolutional Neural Networks (CNN) are employed for disease detection using medical images, while time-series and epidemiological models such as LSTM and SEIR are used for forecasting infection trends. The system also considers socio-environmental and behavioral factors to improve prediction accuracy. Experimental results demonstrate that the proposed approach enhances early detection and supports informed decision-making. This framework provides a scalable solution for managing current and future pandemics.