Department of Biochemistry, University of Western Ontario, London, Ontario, N6A 5C1, Canada.
CytoGnomix Inc, London, Ontario, N5X 3X5, Canada.
F1000Res. 2021 Dec 23;10:1312. doi: 10.12688/f1000research.75891.2. eCollection 2021.
This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran's I spatial autocorrelation, and Local Moran's I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The , which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources. This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.
本研究旨在实时制作加拿大安大略省确诊 COVID-19 病例的社区级地理空间映射，以支持决策。这是通过区域间地统计学分析、时空整合以及 COVID-19 阳性个体的空间插值来实现的。COVID-19 病例和位置是从 2020 年 3 月到 2021 年 6 月，对应于感染的第一、第二和第三波，为地统计学分析而精心挑选的。根据指定的前向分类区（FSA）和市政区域汉密尔顿、滑铁卢/基奇纳、伦敦、渥太华、多伦多和温莎/埃塞克斯县的邮政编码（PC），每日病例进行了汇总。热点是通过区域间测试识别的，包括 Getis-Ord Gi*、全局 Moran's I 空间自相关、局部 Moran's I 不对称聚类和异常值分析。病例计数也通过经验贝叶斯克里金插值跨地理区域进行，该方法本地化 COVID-19 阳性测试的高浓度，独立于 FSA 或 PC 边界。该软件是免费提供的，它自动识别这些区域，并为公共卫生专业人员生成数字地图，以协助接触者追踪和其他资源的分配等大流行管理。本研究通过对 COVID-19 发病率数据进行创新的地理空间分析，实时提供了可能的社区级疾病传播的指标。市政和省级结果通过与长期护理和其他高密度住宅以及农场的已知疫情爆发进行比较得到验证。PC 级分析以比公共 FSA 报告更高的地理空间分辨率揭示了热点，而且通常更早。不同测试和克里金的结果进行了比较，以确定热点分配之间的一致性。毗邻热点的并发或连续热点表明，通过对相邻 PC 的集群和异常值分析以及通过克里金，可以从社区传播 COVID-19。结果还按基于人口的类别（性别、年龄和是否存在合并症）进行分层。更早地识别热点可以减轻 COVID-19 的公共卫生负担，并加快接触者追踪。
