CAJ | 학술논문

　　空间扫描统计量方法是公共卫生监测领域应用非常广泛的空间聚集探测快速算法。其利用传染病监测数据可探测到病例异常增多的局部区域，对可能的传染病暴发做出早期预警。候选聚集区域的预先生成是该方法的一个关键步骤。将现有的候选聚集区域生成方法应用到包含子区域较多的大区域时，可能导致大量候选聚集区域的遗漏，影响探测结果的准确性；或可能生成大量重复的候选聚集区域，导致随后空间扫描计算时间的延长。本文在原有候选聚集区域生成方法的基础上，提出了一种新的快速算法。它以格网点间隔的优化选择，可减少对可能候选聚集区域的遗漏；同时，基于多重排序算法可在较短的时间之内，删除掉原始候选聚集区域集合中的大量重复。通过山东省西南部608个乡镇点的候选聚集区域生成测试，改进的方法可减少候选聚集区域的遗漏，并在较短的时间内删除掉所有的重复候选聚集。
　　공간소묘통계량방법시공공위생감측영역응용비상엄범적공간취집탐측쾌속산법。기이용전염병감측수거가탐측도병례이상증다적국부구역，대가능적전염병폭발주출조기예경。후선취집구역적예선생성시해방법적일개관건보취。장현유적후선취집구역생성방법응용도포함자구역교다적대구역시，가능도치대량후선취집구역적유루，영향탐측결과적준학성；혹가능생성대량중복적후선취집구역，도치수후공간소묘계산시간적연장。본문재원유후선취집구역생성방법적기출상，제출료일충신적쾌속산법。타이격망점간격적우화선택，가감소대가능후선취집구역적유루；동시，기우다중배서산법가재교단적시간지내，산제도원시후선취집구역집합중적대량중복。통과산동성서남부608개향진점적후선취집구역생성측시，개진적방법가감소후선취집구역적유루，병재교단적시간내산제도소유적중복후선취집。
Spatial scan statistic method is a widely adopted spatial cluster detection method in the field of public health surveillance. It can detect a sub-zone where the number of disease cases rises abnormally, based on infec-tious disease surveillance data, and thus is able to make early warning on possible outbreak of infectious disease. Chinese Center for Disease Control and Prevention (China CDC) launched China Infectious Disease Automat-ed-alert and Response System (CIDARS) in 2004, which handles the infectious disease surveillance data of all of the counties of China to detect possible case clusters. The making of candidate clusters is a key step to this meth-od, which to some extent determines the accuracy and time efficiency of the spatial scan statistic method. There are two deficiencies if the existing candidate clusters making method is applied to a very big research area with a lot of sub-regions. The first is that, the inappropriate separation distance of grid points might miss a lot of possi-ble candidate clusters, which affects the accuracy of detected result. The second is that, the existing method might duplicate a great number of candidate clusters, which could prolong the computing time of subsequent spa-tial scan operation. In this paper a new efficient method is proposed according to the former existing candidate clusters making method. Based on the correct setting to the separation distance of grid points, the new method could greatly reduce the possibility of missing of some possible candidate clusters. At the same time, applying multiple-sort arithmetic, the proposed new method could find and delete a great number of duplicate clusters in the original-making candidate clusters in a shorter time. Finally, the paper applies and tests the proposed method for the making of candidate clusters in 608 counties in southwest Shandong Province and proves that the method works satisfactorily in both two aims, that is, it reduced the computing time and reduced the missing of candidate clusters.