Predictive Modeling Techniques for Anticipating Vaccine Demand

Infant vaccine stock prediction is a critical aspect of health care logistics management aimed at ensuring the availability of essential vaccines to safeguard children against preventable diseases. With an emphasis on newborn requirements, this study suggests employing cutting-edge machine learning algorithms to forecast vaccine supply requirements. Traditional forecasting methods often struggle to adapt to the dynamic and complex nature of vaccine demand, leading to inefficient stock management and potential shortages. To address these challenges, this research explores the efficacy of advanced machine learning algorithms such as Gradient Boosting Machines (GBM), Long Short-Term Memory (LSTM) networks, and Support Vector regression (SVR) in predicting infant vaccination stock needs. These algorithms provide the capacity to examine past immunization data, spot trends, and more reliably and accurately proj ect future demand. Through extensive experimentation and evaluation using real-world vaccination data sets, the proposed algorithms demonstrate promising results in accurately predicting stock requirements for infant vaccines. The results of this study help to improve the efficacy and efficiency of vaccination supply chains, guaranteeing infants across the world will have timely access to vaccinations that can save their lives. The present investigation employed machine learning models, namely the linear regression model, Random Forest model, GBM and SVR model, and deep learning models, such as ANN and LSTM, to forecast future trends in essential infant vaccines. The models' performances were compared using the evaluation metrics RMSE and R-Square, as well as by comparing their predictions.