Computational Tracking and Estimating the Covid-19 Dynamic Broadcast Based on Machine Learning Model

Abstract

The COVID-19 pandemic has reshaped global health and societal norms, necessitating effective tracking and forecasting methods. We propose a novel machine learning model that integrates diverse datasets, including epidemiological, demographic, and environmental variables. By employing advanced techniques such as deep learning and time-series analysis, our model accurately predicts the pandemic's trajectory, considering factors like non-pharmaceutical interventions and variant emergence. Through comprehensive data integration and feature engineering, our approach provides actionable insights for policymakers, aiding in proactive response strategies and adaptive tracking of COVID-19 propagation dynamics.