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ABSTRACT The COVID‐19 pandemic has underscored the critical need for accurate mortality forecasts to shape effective public‐health strategies. Our study introduces an advanced prediction framework that integrates online change point detection with a novel training scheme to enhance the forecasting of COVID‐19 mortality. Empirical analyses across national and state datasets in the United States demonstrate that our methodology not only boosts prediction accuracy but also significantly reduces model training time. The proposed hybrid models, which combine the strengths of various model candidates tailored to specific intervals identified by change points, consistently outperform baseline models. They achieve the lowest sMAPE scores and realize execution times that are over 99% faster than traditional models. This work highlights our approach's adaptability to the dynamic nature of the pandemic, marking a significant advancement in real‐time infectious disease monitoring and supporting public‐health decision‐making.
Seung et al. (Mon,) studied this question.