A New MSGSA-Optimized Dynamic Window of Spatiotemporal Scan Statistics for Disease Outbreak Detection
The spatiotemporal scan statistics proposed by Kulldorff have been applied to detect numerous disease clusters, and scan statistics based on heuristic algorithm optimization have also been utilized for disease cluster detection. The gravitational search algorithm (GSA) and the recent human mental se...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9541095/ |
| _version_ | 1818753657427984384 |
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| author | Haiqi Wang Haoran Kong Bin Yan Liuke Li Jianbo Xu Zhihai Wang Qiong Wang |
| author_facet | Haiqi Wang Haoran Kong Bin Yan Liuke Li Jianbo Xu Zhihai Wang Qiong Wang |
| author_sort | Haiqi Wang |
| collection | DOAJ |
| description | The spatiotemporal scan statistics proposed by Kulldorff have been applied to detect numerous disease clusters, and scan statistics based on heuristic algorithm optimization have also been utilized for disease cluster detection. The gravitational search algorithm (GSA) and the recent human mental search (HMS) possess superior performance in comparison with several heuristic algorithms, and neither algorithm has yet been applied in spatiotemporal scan statistics. However, the size of the spatiotemporal scanning window utilized in disease applications is constant in the time dimension, and it is difficult to detect changes in the size of an anomalous cluster over time. In this study, we proposed a dynamic cylinder with a variable radius as a spatiotemporal scanning window. In addition, we proposed an improved GSA based on mental search (MSGSA), and the MSGSA was utilized to optimize the dynamic scanning window to detect spatiotemporally anomalous clusters. The performance of the MSGSA was verified on 23 benchmark functions in comparison with the GSA and HMS. Simulated experiments based on the MSGSA and SaTScan showed that the MSGSA-optimized dynamic window yielded better performance based on the obtained accuracies and error rates. Finally, we utilized the MSGSA-optimized dynamic window and other methods to detect spatiotemporally anomalous clusters of hand-foot-and-mouth disease (HFMD) in China (2016) and Guangdong (2009), and the MSGSA-optimized dynamic window yielded better performance on both HFMD datasets. Moreover, the conclusions obtained with the MSGSA-optimized dynamic window were consistent with those of relevant researchers, indicating that the MSGSA possesses certain disease outbreak detection ability. |
| first_indexed | 2024-12-18T05:10:50Z |
| format | Article |
| id | doaj.art-e3c4d6b674554513b99481c1be9d34fd |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-18T05:10:50Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-e3c4d6b674554513b99481c1be9d34fd2022-12-21T21:19:53ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-0114108211083410.1109/JSTARS.2021.31137859541095A New MSGSA-Optimized Dynamic Window of Spatiotemporal Scan Statistics for Disease Outbreak DetectionHaiqi Wang0https://orcid.org/0000-0002-9684-1722Haoran Kong1https://orcid.org/0000-0003-1318-8404Bin Yan2Liuke Li3Jianbo Xu4Zhihai Wang5Qiong Wang6College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, ChinaState Key Laboratory of Resources and Environmental Information System and the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, ChinaThe spatiotemporal scan statistics proposed by Kulldorff have been applied to detect numerous disease clusters, and scan statistics based on heuristic algorithm optimization have also been utilized for disease cluster detection. The gravitational search algorithm (GSA) and the recent human mental search (HMS) possess superior performance in comparison with several heuristic algorithms, and neither algorithm has yet been applied in spatiotemporal scan statistics. However, the size of the spatiotemporal scanning window utilized in disease applications is constant in the time dimension, and it is difficult to detect changes in the size of an anomalous cluster over time. In this study, we proposed a dynamic cylinder with a variable radius as a spatiotemporal scanning window. In addition, we proposed an improved GSA based on mental search (MSGSA), and the MSGSA was utilized to optimize the dynamic scanning window to detect spatiotemporally anomalous clusters. The performance of the MSGSA was verified on 23 benchmark functions in comparison with the GSA and HMS. Simulated experiments based on the MSGSA and SaTScan showed that the MSGSA-optimized dynamic window yielded better performance based on the obtained accuracies and error rates. Finally, we utilized the MSGSA-optimized dynamic window and other methods to detect spatiotemporally anomalous clusters of hand-foot-and-mouth disease (HFMD) in China (2016) and Guangdong (2009), and the MSGSA-optimized dynamic window yielded better performance on both HFMD datasets. Moreover, the conclusions obtained with the MSGSA-optimized dynamic window were consistent with those of relevant researchers, indicating that the MSGSA possesses certain disease outbreak detection ability.https://ieeexplore.ieee.org/document/9541095/Gravitational search algorithm (GSA)mental searchscan statisticsspatiotemporal anomaly detectionspatiotemporal dynamic scanning window |
| spellingShingle | Haiqi Wang Haoran Kong Bin Yan Liuke Li Jianbo Xu Zhihai Wang Qiong Wang A New MSGSA-Optimized Dynamic Window of Spatiotemporal Scan Statistics for Disease Outbreak Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Gravitational search algorithm (GSA) mental search scan statistics spatiotemporal anomaly detection spatiotemporal dynamic scanning window |
| title | A New MSGSA-Optimized Dynamic Window of Spatiotemporal Scan Statistics for Disease Outbreak Detection |
| title_full | A New MSGSA-Optimized Dynamic Window of Spatiotemporal Scan Statistics for Disease Outbreak Detection |
| title_fullStr | A New MSGSA-Optimized Dynamic Window of Spatiotemporal Scan Statistics for Disease Outbreak Detection |
| title_full_unstemmed | A New MSGSA-Optimized Dynamic Window of Spatiotemporal Scan Statistics for Disease Outbreak Detection |
| title_short | A New MSGSA-Optimized Dynamic Window of Spatiotemporal Scan Statistics for Disease Outbreak Detection |
| title_sort | new msgsa optimized dynamic window of spatiotemporal scan statistics for disease outbreak detection |
| topic | Gravitational search algorithm (GSA) mental search scan statistics spatiotemporal anomaly detection spatiotemporal dynamic scanning window |
| url | https://ieeexplore.ieee.org/document/9541095/ |
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