Comparing circular and flexibly-shaped scan statistics for disease clustering detection
The accuracy of spatial clustering detection is crucial for public health policy development and identifying etiological clues. Circular and flexibly-shaped scan statistics are widely used for disease cluster detection, but differences in results arise mainly due to parameter sensitivity and variati...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1432645/full |
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| author | Lina Wang Xiang Li Zhengbin Zhang Haoxun Yuan Pengfei Lu Yaru Li |
| author_facet | Lina Wang Xiang Li Zhengbin Zhang Haoxun Yuan Pengfei Lu Yaru Li |
| author_sort | Lina Wang |
| collection | DOAJ |
| description | The accuracy of spatial clustering detection is crucial for public health policy development and identifying etiological clues. Circular and flexibly-shaped scan statistics are widely used for disease cluster detection, but differences in results arise mainly due to parameter sensitivity and variations in the scanning window shapes. This study aims to analyze the impact of parameter settings on the results of these methods and compare their performance in disease clustering detection. Using tuberculosis data from Wuhan, China (2015–2019), the study identified the optimal parameter settings—MSWS and K-value—for each method to ensure accurate clustering. A comprehensive comparison was made using two quantitative indicators, the LLR value and cluster size, as well as clustering visualizations. The results show that the optimal MSWS parameter for SaTScan is determined through a Gini coefficient-based stepwise-threshold-reduction approach, while a K-value of 30 is ideal for FleXScan. SaTScan tends to produce more regular clusters, while FleXScan often generates more irregular clusters. FleXScan detects fewer clusters but with higher LLR values and larger average cluster sizes, although the maximum cluster size is smaller. These findings provide valuable insights for optimizing disease clustering detection methods and enhancing public health interventions. |
| format | Article |
| id | doaj-art-b0a0f589d2214b3f94e3e12949b72c47 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-b0a0f589d2214b3f94e3e12949b72c472025-01-08T05:10:27ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-01-011210.3389/fpubh.2024.14326451432645Comparing circular and flexibly-shaped scan statistics for disease clustering detectionLina Wang0Xiang Li1Zhengbin Zhang2Haoxun Yuan3Pengfei Lu4Yaru Li5School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaInstitute of Surveying and Mapping, Information Engineering University, Zhengzhou, ChinaTuberculosis Prevention and Control Office, Wuhan Institute for Tuberculosis Control, Wuhan, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaInstitute of Surveying and Mapping, Information Engineering University, Zhengzhou, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaThe accuracy of spatial clustering detection is crucial for public health policy development and identifying etiological clues. Circular and flexibly-shaped scan statistics are widely used for disease cluster detection, but differences in results arise mainly due to parameter sensitivity and variations in the scanning window shapes. This study aims to analyze the impact of parameter settings on the results of these methods and compare their performance in disease clustering detection. Using tuberculosis data from Wuhan, China (2015–2019), the study identified the optimal parameter settings—MSWS and K-value—for each method to ensure accurate clustering. A comprehensive comparison was made using two quantitative indicators, the LLR value and cluster size, as well as clustering visualizations. The results show that the optimal MSWS parameter for SaTScan is determined through a Gini coefficient-based stepwise-threshold-reduction approach, while a K-value of 30 is ideal for FleXScan. SaTScan tends to produce more regular clusters, while FleXScan often generates more irregular clusters. FleXScan detects fewer clusters but with higher LLR values and larger average cluster sizes, although the maximum cluster size is smaller. These findings provide valuable insights for optimizing disease clustering detection methods and enhancing public health interventions.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1432645/fullspatial scan statisticsdisease cluster detectionSaTScanFleXScanGini coefficientlog-likelihood ratio (LLR) |
| spellingShingle | Lina Wang Xiang Li Zhengbin Zhang Haoxun Yuan Pengfei Lu Yaru Li Comparing circular and flexibly-shaped scan statistics for disease clustering detection Frontiers in Public Health spatial scan statistics disease cluster detection SaTScan FleXScan Gini coefficient log-likelihood ratio (LLR) |
| title | Comparing circular and flexibly-shaped scan statistics for disease clustering detection |
| title_full | Comparing circular and flexibly-shaped scan statistics for disease clustering detection |
| title_fullStr | Comparing circular and flexibly-shaped scan statistics for disease clustering detection |
| title_full_unstemmed | Comparing circular and flexibly-shaped scan statistics for disease clustering detection |
| title_short | Comparing circular and flexibly-shaped scan statistics for disease clustering detection |
| title_sort | comparing circular and flexibly shaped scan statistics for disease clustering detection |
| topic | spatial scan statistics disease cluster detection SaTScan FleXScan Gini coefficient log-likelihood ratio (LLR) |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1432645/full |
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