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Objective: It is challenging to develop an appropriate surveillance threshold for early warning of seasonal influenza epidemics.We propose a novel moving logistic regression method (MLRM) to determine epidemic thresholds and validate it with Chinese influenza surveillance data.Methods: For a given symmetric epidemic wave represented by weekly positive rates of diagnosed influenza cases,the MLRM establishes the threshold by fitting a series of logistic curves to identify the best-fitting curve to the data.Results: Using surveillance data of seasonal influenza of 30 provinces of China during 2010-2014,we identified 100 roughly symmetric waves from 153 epidemic waves;and 85 of the 100 waves were satisfactorily fitted.Compared to two published approaches,the MLRM identified lower thresholds of seasonal influenza epidemics,leading to about three weeks earlier detection of onset and about four weeks later detection of closure of the epidemics.The potential misclassification proportion of influenza epidemic waves was 6% for the MLRM,comparable to that for two published approaches.Using the MLRM,we established three levels of provincial thresholds: low (2.70%),medium (3.51%),and high (4.23%).Conclusion: The MLRM offers an alternative to existing methods for defining epidemic thresholds of seasonal influenza,particularly useful in early detection of future influenza epidemics.