Tao Wang¹, Yitong Zhao², Yonglin Lei³, Mei Yang⁴ and Shan Mei⁵, ^1,3,4,5National University of Defence Technology, China and ²Troop of PLA, China

Spatial cluster detection is widely used for disease surveillance, prevention and containment. However, the commonly used clustering methods cannot resolve the conflicts between the accuracy and efficiency of the detection. This paper proposes an improved method for flexiblyshaped spatial scanning, which can identify irregular spatial clusters more accurately and efficiently. By using a genetic algorithm, we also accelerate the detection process. We convert geographic information to a network structure, in which nodes represent the regions and edges represent the adjacency relationship between regions. According to Kulldorff’s spatial scan statistics, we set the objective function. A constraint condition based on the spectral graph theory is employed to avoid disconnectedness or excessive irregularity of clusters. The algorithm is tested by analysing the simulation data of H1N1 influenza in Beijing. The results show that compared with the previous spatial scan statistic algorithms, our algorithm performs better with shorter time and higher accuracy.

Spatial cluster detection, flexibly-shaped spatial scanning, H1N1 influenza in Beijing