A New Integrated Interpolation Method for High Missing Unstable Disease Surveillance Data-12 Urban Agglomerations, China, 2009-2020
文献类型：期刊论文
| 作者 | Shi, Yuanhao1,2; Liao, Yilan1 |
| 刊名 | CHINA CDC WEEKLY
 |
| 出版日期 | 2024-07-05 |
| 卷号 | 6期号:27 |
| DOI | 10.46234/ccdcw2024.124 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | DIAGNOSIS |
| WOS研究方向 | Public, Environmental & Occupational Health |
| WOS记录号 | WOS:001287589500004 |
| 出版者 | Chinese Center for Disease Control and Prevention |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206934]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Liao, Yilan |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Yuanhao,Liao, Yilan. A New Integrated Interpolation Method for High Missing Unstable Disease Surveillance Data-12 Urban Agglomerations, China, 2009-2020[J]. CHINA CDC WEEKLY,2024,6(27). |
| APA | Shi, Yuanhao,&Liao, Yilan.(2024).A New Integrated Interpolation Method for High Missing Unstable Disease Surveillance Data-12 Urban Agglomerations, China, 2009-2020.CHINA CDC WEEKLY,6(27). |
| MLA | Shi, Yuanhao,et al."A New Integrated Interpolation Method for High Missing Unstable Disease Surveillance Data-12 Urban Agglomerations, China, 2009-2020".CHINA CDC WEEKLY 6.27(2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
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