Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Shi Y

PROVIDER: S-EPMC11252051 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Publications

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

Shi Yuanhao Y Liao Yilan Y

China CDC weekly 20240701 27

<h4>Introduction</h4>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.<h4>Methods</h4>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 en ...[more]

PMID: 39027630

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Dataset Information

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

Introduction

Methods

Results

Discussion

Publications

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

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