AlQadi Hadeel, Bani-Yaghoub Majid, Wu Siqi, Balakumar Sindhu, Francisco Alex
Department of Mathematics and Statistics, University of Missouri-Kansas City, Kansas City, MO 64110, USA.
Department of Mathematics, Jazan University, 45142 Jazan, Saudi Arabia.
Epidemiol Infect. 2022 Mar 9;151:e178. doi: 10.1017/S0950268822000462.
Kansas City, Missouri, became one of the major United States hotspots for COVID-19 due to an increase in the rate of positive COVID-19 test results. Despite the large numbers of positive cases in Kansas City, MO, the spatial-temporal analysis of data has been less investigated. However, it is critical to detect emerging clusters of COVID-19 and enforce control and preventive policies within those clusters. We conducted a prospective Poisson spatial-temporal analysis of Kansas City, MO data to detect significant space-time clusters of COVID-19 positive cases at the zip code level in Kansas City, MO. The analysis focused on daily infected cases in four equal periods of 3 months. We detected temporal patterns of emerging and re-emerging space-time clusters between March 2020 and February 2021. Three statistically significant clusters emerged in the first period, mainly concentrated in downtown. It increased to seven clusters in the second period, spreading across a broader region in downtown and north of Kansas City. In the third period, nine clusters covered large areas of north and downtown Kansas City, MO. Ten clusters were present in the last period, further extending the infection along the State Line Road. The statistical results were communicated with local health officials and provided the necessary guidance for decision-making and allocating resources (e.g., vaccines and testing sites). As more data become available, statistical clustering can be used as a COVID-19 surveillance tool to measure the effects of vaccination.
由于新冠病毒检测呈阳性率上升，密苏里州堪萨斯城成为美国新冠疫情的主要热点地区之一。尽管密苏里州堪萨斯城有大量阳性病例，但对数据的时空分析却较少受到研究。然而，检测新冠病毒新出现的聚集性病例并在这些聚集性病例范围内实施防控和预防政策至关重要。我们对密苏里州堪萨斯城的数据进行了前瞻性泊松时空分析，以在邮政编码层面检测密苏里州堪萨斯城新冠病毒阳性病例的显著时空聚集性。该分析聚焦于四个为期3个月的相等时间段内的每日感染病例。我们检测到了2020年3月至2021年2月期间新出现和再次出现的时空聚集性的时间模式。在第一个时间段出现了三个具有统计学意义的聚集性，主要集中在市中心。在第二个时间段增加到七个聚集性，分布在堪萨斯城市中心和北部更广泛的区域。在第三个时间段，九个聚集性覆盖了密苏里州堪萨斯城北部和市中心的大片区域。在最后一个时间段有十个聚集性，沿着州际公路进一步扩大了感染范围。统计结果与当地卫生官员进行了沟通，并为决策和资源分配（如疫苗和检测点）提供了必要指导。随着更多数据可用，统计聚类可作为一种新冠疫情监测工具来衡量疫苗接种的效果。
