Xu Fuyu, Beard Kate
School of Computing and Information Science, University of Maine, Orono, ME, United States of America.
PLoS One. 2021 Jun 10;16(6):e0252990. doi: 10.1371/journal.pone.0252990. eCollection 2021.
The outbreak of the COVID-19 disease was first reported in Wuhan, China, in December 2019. Cases in the United States began appearing in late January. On March 11, the World Health Organization (WHO) declared a pandemic. By mid-March COVID-19 cases were spreading across the US with several hotspots appearing by April. Health officials point to the importance of surveillance of COVID-19 to better inform decision makers at various levels and efficiently manage distribution of human and technical resources to areas of need. The prospective space-time scan statistic has been used to help identify emerging COVID-19 disease clusters, but results from this approach can encounter strategic limitations imposed by constraints of the scanning window. This paper presents a different approach to COVID-19 surveillance based on a spatiotemporal event sequence (STES) similarity. In this STES based approach, adapted for this pandemic context we compute the similarity of evolving daily COVID-19 incidence rates by county and then cluster these sequences to identify counties with similarly trending COVID-19 case loads. We analyze four study periods and compare the sequence similarity-based clusters to prospective space-time scan statistic-based clusters. The sequence similarity-based clusters provide an alternate surveillance perspective by identifying locations that may not be spatially proximate but share a similar disease progression pattern. Results of the two approaches taken together can aid in tracking the progression of the pandemic to aid local or regional public health responses and policy actions taken to control or moderate the disease spread.
2019年12月，中国武汉首次报告了新型冠状病毒肺炎（COVID-19）疫情。美国的病例于1月下旬开始出现。3月11日，世界卫生组织（WHO）宣布该疫情为大流行。到3月中旬，COVID-19病例在美国各地蔓延，到4月出现了几个热点地区。卫生官员指出，对COVID-19进行监测对于更好地为各级决策者提供信息以及有效管理人力和技术资源向需求地区的分配非常重要。前瞻性时空扫描统计方法已被用于帮助识别新出现的COVID-19疾病聚集区，但这种方法的结果可能会遇到扫描窗口限制所带来的策略性局限。本文提出了一种基于时空事件序列（STES）相似性的COVID-19监测的不同方法。在这种基于STES的方法中，针对此次大流行背景进行了调整，我们计算了各县每日不断变化的COVID-19发病率的相似性，然后对这些序列进行聚类，以识别COVID-19病例负荷趋势相似的县。我们分析了四个研究期，并将基于序列相似性的聚类与基于前瞻性时空扫描统计的聚类进行比较。基于序列相似性的聚类通过识别可能在空间上不相邻但具有相似疾病进展模式的位置，提供了另一种监测视角。将这两种方法的结果结合起来，可以帮助追踪大流行的进展，以协助地方或区域的公共卫生应对措施以及为控制或减缓疾病传播而采取的政策行动。
