Small Samples, New Viruses, Inputs for Decision-Making and Methodology: Bootstrap and Smote

This study presents a comprehensive methodology that combines resampling and oversampling techniques to address the challenges of limited and unbalanced data, specically in the context of viral emergencies such as the COVID-19 pandemic. Utilizing advanced statistical techniques like Bootstrap and SMOTE, the study conducts a retrospective analysis of COVID-19 patients, identifying those at higher risk of mortality. The proposed methodology not only enhances the accuracy of predictions in scenarios with limited data but also facilitates better decision-making in clinical triage systems. By applying these methods, the study achieves early and accurate identication of high-risk individuals, optimizing resource allocation and timely medical interventions. The results demonstrate that this combination of statistical techniques effectively improves health systems and responses to new viral threats, providing a robust foundation for informed decision-making in medical emergencies.