A framework using large time series model for early warning of infectious diseases.

Objective: Infectious diseases controlling system is indispensable for weaken the damage to the people's life and property security caused by infectious diseases. An effective infectious diseases controlling system must incorporate an early warning mechanism designed to detect abnormal rising trends (outbreak) in spatial-temporal series. However, existing anomaly detection methods are often constrained by the quality and quantity of available data in specific application scenarios, particularly in infectious diseases early warning scenarios. Methods: The emergence of generative pre-trained large time series models-hereafter referred to as large time series models-may provide a solution to this challenge. Based on these models, we propose an effective early warning framework. Results: We compared the framework with statistic and deep learning methods on real-world infectious diseases datasets and related derived datasets. Our framework has a better performance and requires less data. Conclusion: We propose a readily deployable early warning framework characterized by strong generalization ability and exceptional performance, which would enlighten the epidemic modeling researchers.