Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning.

ABSTRACT: Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management, yet existing methods are often context-specific, require a long preparation time, and non-outbreak prediction remains understudied. To address this gap, we propose a novel framework using a feature-based time series classification (TSC) method to forecast outbreaks and non-outbreaks. We tested our methods on synthetic data from a Susceptible-Infected-Recovered (SIR) model for slowly changing, noisy disease dynamics. Outbreak sequences give a transcritical bifurcation within a specified future time window, whereas non-outbreak (null bifurcation) sequences do not. We identified incipient differences, reflected in 22 statistical features and 5 early warning signal indicators, in time series

SUBMITTER: Gao S 

PROVIDER: S-EPMC11835380 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature