Early Disease Outbreak Detection in Spatio-Temporal Data Using Predictive Modeling and Extreme Value Theory

Early detection of outbreaks is crucial for reducing their impact on public health. Static manual thresholds have been used for traditional detection methods, which fail to capture extreme events in dynamic transmission patterns. The aim of this study is to introduce a generalized framework that integrates feature engineering, predictive modeling, and Extreme Value Theory (EVT) for dynamic thresholding in Spatio-temporal data. This generalized framework is capable of adapting to different diseases and regions, enabling more accurate outbreak detection across different datasets. This generalized framework applied to dengue and Covid-19 disease cases data and the proposed method outperformed traditional approaches by achieving higher accuracy, precision, and F1 scores. The EVT based method gives a more reliable solution for identifying outbreaks in irregularly distributed data, enhancing public health response capabilities.