Centre for Infectious Disease Epidemiology and Research, Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, 12 Science Drive 2, Singapore, 117549, Singapore.
Department of Geography, National University of Singapore, Block AS2, 1 Arts Link, Singapore, 117570, Singapore.
Sci Rep. 2022 Jun 13;12(1):9758. doi: 10.1038/s41598-022-12941-8.
Geographical weighted regression (GWR) can be used to explore the COVID-19 transmission pattern between cases. This study aimed to explore the influence from environmental and urbanisation factors, and the spatial relationship between epidemiologically-linked, unlinked and imported cases during the early phase of the epidemic in Singapore. Spatial relationships were evaluated with GWR modelling. Community COVID-19 cases with residential location reported from 21st January 2020 till 17th March 2020 were considered for analyses. Temperature, relative humidity, population density and urbanisation are the variables used as exploratory variables for analysis. ArcGIS was used to process the data and perform geospatial analyses. During the early phase of COVID-19 epidemic in Singapore, significant but weak correlation of temperature with COVID-19 incidence (significance 0.5-1.5) was observed in several sub-zones of Singapore. Correlations between humidity and incidence could not be established. Across sub-zones, high residential population density and high levels of urbanisation were associated with COVID-19 incidence. The incidence of COVID-19 case types (linked, unlinked and imported) within sub-zones varied differently, especially those in the western and north-eastern regions of Singapore. Areas with both high residential population density and high levels of urbanisation are potential risk factors for COVID-19 transmission. These findings provide further insights for directing appropriate resources to enhance infection prevention and control strategies to contain COVID-19 transmission.
地理加权回归（GWR）可用于探索病例间的 COVID-19 传播模式。本研究旨在探索环境和城市化因素的影响，以及新加坡疫情早期期间流行病学相关、不相关和输入性病例之间的空间关系。使用 GWR 模型评估空间关系。分析考虑了从 2020 年 1 月 21 日至 2020 年 3 月 17 日报告的有居住地的社区 COVID-19 病例。温度、相对湿度、人口密度和城市化是用于分析的探索性变量。ArcGIS 用于处理数据并执行地理空间分析。在新加坡 COVID-19 疫情的早期阶段，观察到几个新加坡分区的温度与 COVID-19 发病率之间存在显著但较弱的相关性（显著性 0.5-1.5）。湿度与发病率之间的相关性无法建立。在各个分区中，高居民人口密度和高水平的城市化与 COVID-19 发病率相关。分区内 COVID-19 病例类型（相关、不相关和输入性）的发病率存在差异，尤其是新加坡西部和东北部地区。高居民人口密度和高水平城市化并存的地区是 COVID-19 传播的潜在危险因素。这些发现为指导适当的资源提供了进一步的见解，以加强感染预防和控制策略，以遏制 COVID-19 的传播。
