疫苗可预防疾病的关键空间集群。

Jose Cadena, Achla Marathe, Anil Vullikanti
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引用次数: 1

摘要

控制高度传染性疾病(如麻疹)传播的标准公共卫生干预措施是为大部分人口接种疫苗。然而,已经表明,在美国的一些地区,尽管平均疫苗接种率很高,但未接种疫苗人口的地理聚集正在出现。鉴于用于应对的公共卫生资源有限,确定关键群集并对其进行排序,有助于确定优先次序并分配稀缺资源,用于监测和快速干预。我们将集群的临界性量化为如果集群中的免疫率降低而导致的额外感染数量。这种临界性的概念以前没有被研究过,并且,基于先前研究中确定的群集,我们表明当前群集中的免疫不足率与其临界性不相关。我们将我们的方法应用于明尼苏达州的人口模型,在那里我们发现疫苗接种不足的集群具有明显高于其他自然启发式方法获得的临界性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Critical spatial clusters for vaccine preventable diseases.

The standard public health intervention for controlling the spread of highly contagious diseases, such as measles, is to vaccinate a large fraction of the population. However, it has been shown that in some parts of the United States, even though the average vaccination rate is high, geographical clusters of undervaccinated populations are emerging. Given that public health resources for response are limited, identifying and rank-ordering critical clusters can help prioritize and allocate scarce resources for surveillance and quick intervention. We quantify the criticality of a cluster as the additional number of infections caused if the immunization rate in a cluster reduces. This notion of criticality has not been studied before, and, based on clusters identified in prior research, we show that the current underimmunization rate in the cluster, and its criticality are not correlated. We apply our methods to a population model for the state of Minnesota, where we find undervaccinated clusters with significantly higher criticality than those obtained by other natural heuristics.

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