灵活的扫描统计数据,具有有限的概率比率,用于优化COVID-19监测
Ernest Akyereko1, Frank B Osei2, Kofi M Nyarko3
1Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, The Netherlands; University of Environment and Sustainable Development, PMB, Somanya, ER. e.akyereko@utwente.nl.
Geospatial health
|November 28, 2024
概括
这项研究使用空间分析确定了加纳的高风险COVID-19集群. 针对这些地区的有针对性的监测,特别是在东南部和中部/东北部地区,对于早期发现和控制变种至关重要.
科学领域:
- 流行病学 流行病学
- 空间分析 空间分析
- 公共卫生 公共卫生
背景情况:
- 根据世界卫生组织的建议,有效的疾病监测对于检测新的COVID-19变种至关重要.
- 将COVID-19监测与其他呼吸道疾病整合起来,需要确定高风险地区,这是发展中国家经常缺少的信息.
- 加纳的常规分析缺乏COVID-19发病率和病例死亡率 (CFR) 的空间风险数据.
研究的目的:
- 通过扫描统计集群分析,揭示加纳COVID-19发病率和CFR的空间模式.
- 确定高风险地区进行有针对性的哨兵或基因组监测.
- 检查共变量对COVID-19发病率和CFR的空间集群的影响.
主要方法:
- 采用灵活的空间扫描统计数据,对集群分析的概率比率受到限制.
- 分析了加纳的COVID-19数据,涵盖了四次大流行浪潮 (2020年3月至2022年2月).
- 根据共同变量进行调整:地震中心的距离,65岁以上的人口,男性比例和城市比例.
主要成果:
- 在所有四个波段中确定了56个重要的发病率和26个CFR的空间集群.
- 发病率最有可能的集群 (MLC) 位于加纳东南部;CFR集群位于中部和东北部地区.
- 靠近地震中心和高城市人口比例增加了发病率;高比例的65岁以上的人增加了CFR.
结论:
- 迦纳的COVID-19发病率和CFR在空间上聚集在一起,受到城市人口,男性比例,老年人口和靠近地震中心的影响.
- 确定高风险地区可以作为加强监控的关键地点.
- 未来的控制措施应整合医疗保健获取改善和城市人口增长管理.
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