Explainable SIR Forecasting Model for Herd Immunity Prediction in COVID-19

The COVI-19 pandemic has had a profound impact on societies across the globe. Prioritizing the goal of achieving herd immunity is crucial for reducing the spread of the virus, and thus, this research places a strong emphasis on this objective. This study aims to emphasize the importance of understanding and predicting herd immunity levels for COVID-19 using advanced machine learning algorithms to ensure high-quality predictions that are easily accessible. By leveraging various data sources such as vaccination data, population statistics, and daily COVID-19 case counts, this research study has developed predictive models to estimate the threshold required for herd immunity. Our approach involves employing a range of machine-learning techniques, induding regression analysis, and ensemble methods along with an Explainable and Susceptible-Infectious-Recovered (SIR) Forecasting model, to capture the complex interactions between infection rates, vaccination campaigns, and population demographics. The performance of our models has been rigorously evaluated and validated against COVID-19 data. The proposed model combines the interpretability of the $S I R$ model with the predictive power of machine learning and Explainable-AI, providing a transparent and reliable framework for herd immunity estimation. The findings highlight the importance of explainable AI in public health decision-making and provide valuable insights into herd immunity dynamics for COVID-19.