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利用时空数据库分析 COVID-19 期间的跨国疫情关联:网络分析。

Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis.

机构信息

Department of Social Sciences, The Education University of Hong Kong, Hong Kong, China (Hong Kong).

Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Hong Kong, China (Hong Kong).

出版信息

JMIR Public Health Surveill. 2021 Mar 29;7(3):e27317. doi: 10.2196/27317.

Abstract

Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

摘要

传染病(包括 COVID-19)对全球公共卫生构成重大威胁。为了有效遏制传染病的传播,及时监测和预测大流行风险至关重要。本研究旨在分析免费和公开可用的数据,以构建除常见描述性统计数据之外的有用旅行数据记录。本研究通过时间序列图和时空图的分析结果来描述大流行关联性的分析发现,以说明或可视化大流行关联性。我们分析了从基于网络的民用航空公共卫生事件预防和管理协作安排仪表盘中检索的数据,该仪表盘中包含了根据机场、国家、城市、纬度和经度对来自 193 个国家政府的民用航班的最新和全面的元信息。我们使用该数据库通过旅行数据收集、网络构建、数据聚合、旅行统计计算以及时间序列图和时空图的可视化来展示大流行关联性。我们观察到 2019 年和 2020 年 1 月至 3 月初全球每日航班的时间序列图中存在相似的模式。2020 年 3 月中旬记录的每日航班数量大幅减少,这可能与 COVID-19 大流行期间大规模的航空旅行限制有关。地点之间的连通水平是大流行风险的有力指标。自全球首例 COVID-19 病例报告以来,2020 年 3 月初高网络密度和相互性作为 COVID-19 大流行的早期信号,与 2020 年 3 月中旬 COVID-19 病例的快速增加有关。2020 年 3 月 13 日欧洲连通性的时空图显示了欧洲国家之间的最高连通性,这反映了 2020 年 3 月底和 4 月初 COVID-19 的严重爆发。作为质量控制措施,我们使用 2020 年 4 月至 10 月的国际航班汇总数量,将国际民用航空组织正式报告的国际航班数量与从民用航空公共卫生事件预防和管理协作安排仪表盘中收集的数据进行比较,我们观察到这两个数据集之间具有高度一致性。数据库的灵活设计使用户能够根据需要通过各种网络统计计算方法访问不同时期、地点和空间水平的网络连通性。这种分析有助于及早识别当前传染病大流行的风险以及未来新出现的传染病。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8d2/8088858/6e8be89a7173/publichealth_v7i3e27317_fig1.jpg

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