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

作　　者：Yuanhao Shi Yilan Liao 

机构地区：[1]The State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing,China [2]University of Chinese Academy of Science,Beijing,China

出　　处：《China CDC weekly》2024年第27期670-676,I0005-I0012,共15页中国疾病预防控制中心周报（英文）

基　　金：Supported by the Foundation of China(grant number 42171419);National Science and Technology Major Project of China(grant number 2018ZX10713001).

摘　　要：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.

关 键 词：INTERPOLATION INCOMPLETE exceptional 

分 类 号：R18[医药卫生—流行病学]