A New MSGSA-Optimized Dynamic Window of Spatiotemporal Scan Statistics for Disease Outbreak Detection

Abstract

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.

Keywords

• Gravitational search algorithm (GSA)
• mental search
• scan statistics
• spatiotemporal anomaly detection
• spatiotemporal dynamic scanning window