时间空间集群检测用于COVID-19疫情监测:描述性分析研究研究
Rachel Martonik1, Caitlin Oleson2, Ellyn Marder2
1Deloitte, Arlington, VA, United States.
JMIR public health and surveillance
|October 16, 2024
概括
一个新的时空变换模型系统地检测到华盛顿州的COVID-19集群,改善了在学校和工作场所等高优先级环境中早期识别疫情,以便及时进行公共卫生干预.
科学领域:
- 流行病学 流行病学
- 公共卫生监督 公共卫生监督
- 统计建模 统计建模
背景情况:
- 在2020-2021年冬季,华盛顿州面临众多COVID-19疫情,主要是在工作场所,社区环境和学校.
- 现有的检测方法,如医疗机构的自动地址匹配,不足以系统地识别全州范围内的疫情,导致延迟和不完整的数据.
研究的目的:
- 实施和评估一个系统的,全州的集群检测模型,以便及时识别COVID-19集群.
- 该模型旨在帮助当地卫生司法管辖区 (LHJs) 进行调查和确定资源优先级.
主要方法:
- 一项涉及6个LHJ的试点研究测试了分析类型,地理聚合,集群半径和数据滞后等参数.
- 从7月17日到2021年12月17日,每周一次,LHJ特定的回顾式时空变换模型在全州范围内实施.
- 检测到的集群根据LHJ人口和疾病发病率进行分析,并与报告的疫情进行比较.
主要成果:
- 该模型在全州范围内确定了2874个新的COVID-19集群.
- 超过58%的鉴定集群是及时的,发生在分析后一周内,允许早期干预.
- 虽然363个报告的疫情与SaTScan集群相匹配,但该模型在监控系统中识别它们之前,已经确定了大约三分之一的疫情.
结论:
- 时空变换模型成功地在全州范围内识别了及时和可操作的COVID-19集群,无论人口或发病率如何.
- 该模型在检测学校和社区设置等高优先级设置中的集群方面表现出有效性,匹配报告的疫情.
- 结果表明,与现有方法相比,该模型可能可以在工作场所提前发现疫情爆发.
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