A Binary Prototype for Time-Series Surveillance and Intervention

This paper presents a methodological framework for epidemic surveillance and intervention using binary classification approaches applied to time-series data. The authors develop and evaluate a prototype system designed to detect meaningful epidemiological changes and guide intervention decisions in real time. Combining epidemiological modeling, statistical inference, and surveillance analytics, the framework aims to improve outbreak monitoring and support timely public health responses. The study contributes to broader efforts to strengthen data-driven infectious disease surveillance and decision-making systems.