Horiike Ryo, Itatani Tomoya, Nakai Hisao, Nishioka Daisuke, Kataoka Aoi, Ito Yuri
Department of Public Health Nursing, Osaka Medical and Pharmaceutical University, Osaka, Japan.
Division of Home Care Nursing, Department of Fundamental and Community Nursing Science, School of Nursing, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan.
JMA J. 2024 Jul 16;7(3):319-327. doi: 10.31662/jmaj.2023-0208. Epub 2024 Jun 10.
This study evaluated the detection of monthly human mobility clusters and characteristics of cluster areas before the coronavirus disease 2019 (COVID-19) outbreak using spatial epidemiological methods, namely, spatial scan statistics and geographic information systems (GIS).
The research area covers approximately 10.3 km, with a population of about 350,000 people. Analysis was conducted using open data, with the exception of one dataset. Human mobility and population data were used on a 1-km mesh scale, and business location data were used to examine the area characteristics. Data from January to December 2019 were utilized to detect human mobility clusters before the COVID-19 pandemic. Spatial scan statistics were performed using SaTScan to calculate relative risk (RR). The detected clusters and other data were visualized in QGIS to explore the features of the cluster areas.
Spatial scan statistics identified 33 clusters. The detailed analysis focused on clusters with an RR exceeding 1.5. Meshes with an RR over 1.5 included one with clusters for 1 year which is identified in all months of the year, one with clusters for 9 months, three with clusters for 6 months, three with clusters for 3 months, and four with clusters for 1 month. September had the highest number of clusters (eight), followed by April and November (seven each). The remaining months had five or six clusters. Characteristically, the cluster areas included the vicinity of railway stations, densely populated business areas, ball game fields, and large-scale construction sites.
Statistical analysis of human mobility clusters using open data and open-source tools is crucial for the advancement of evidence-based policymaking based on scientific facts, not only for novel infectious diseases but also for existing ones, such as influenza.
本研究使用空间流行病学方法，即空间扫描统计法和地理信息系统（GIS），评估了2019冠状病毒病（COVID-19）疫情爆发前每月人类流动聚集情况及聚集区域特征。
研究区域面积约10.3平方公里，人口约35万。分析使用公开数据，但有一个数据集除外。人类流动和人口数据采用1公里网格尺度，商业地点数据用于考察区域特征。利用2019年1月至12月的数据检测COVID-19大流行前的人类流动聚集情况。使用SaTScan进行空间扫描统计以计算相对风险（RR）。在QGIS中对检测到的聚集区和其他数据进行可视化处理，以探索聚集区域的特征。
空间扫描统计识别出33个聚集区。详细分析聚焦于RR超过1.5的聚集区。RR超过1.5的网格中，有一个全年各月均存在聚集区，持续1年；一个存在9个月的聚集区；三个存在6个月的聚集区；三个存在3个月的聚集区；四个存在1个月的聚集区。9月的聚集区数量最多（8个），其次是4月和11月（各7个）。其余月份有5个或6个聚集区。典型的聚集区域包括火车站附近、人口密集的商业区、棒球场和大型建筑工地。
利用公开数据和开源工具对人类流动聚集情况进行统计分析，对于推进基于科学事实的循证决策至关重要，不仅适用于新型传染病，也适用于流感等现有疾病。
