Combining data assimilation of states and parameters for more precise infectious disease prediction.

ABSTRACT: Global escalation of infectious disease outbreak risks necessitates advanced predictive models. Despite methodological advances, errors in initial states and parameters of epidemiological dynamic models remain a key limitation to prediction reliability. To address this limitation, we propose an optimized data assimilation framework for combined state-parameter optimization based on Ensemble Kalman Filter. We design space transformations and adaptive covariance inflation driven by epidemic development and prediction errors, achieving a more stable update process and rapid response to epidemic changes. Through synthetic experiments and real-world case studies, the proposed scheme significantly reduces initial state and parameter errors, leading to a substantial improvement in prediction accu

SUBMITTER: Hao Z 

PROVIDER: S-EPMC12649004 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature