A New Integrated Interpolation Method for High Missing Unstable Disease Surveillance Data—12 Urban Agglomerations,China,2009-2020

Introduction:The prevalence of unstable and incomplete monitoring data significantly complicates syndromic analysis.Many data interpolation methods currently available demonstrate inadequate effectiveness in overcoming this issue.Methods:To improve the accuracy of interpolation,we propose the integration of the SHapley Additive exPlanation model(SHAP)with the structural equation model(SEM),forming a combined SHAP-SEM approach.A case study is then performed to assess the enhanced performance of this novel model compared to traditional methods.Results:The SHAP-SEM model was utilized to develop an interpolation model employing data from the Chinese respiratory syndrome surveillance database.We executed three distinct experiments to establish the model datasets,comprising a total of 100 replicates.The performance of the model was evaluated using the root mean square error(RMSE),correlation coefficient(r),and F-score.The findings demonstrate that the SHAP-SEM model consistently achieves superior accuracy in data interpolation,which is evident across different seasons and in overall performance.Discussion:We conclude that the SHAP-SEM model demonstrates an exceptional capacity for accurately interpolating volatile and incomplete data.This capability is crucial for developing a comprehensive database that is essential for conducting risk assessments related to syndromes.