Afroj Moon Sifat, Marathe Achla, Vullikanti Anil
Network Systems Science and Advanced Computing, Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
R Soc Open Sci. 2023 Aug 16;10(8):230873. doi: 10.1098/rsos.230873. eCollection 2023 Aug.
This research develops a novel system science approach to examine the potential risk of outbreaks caused by geographical clustering of underimmunized individuals for an infectious disease like measles. We use an activity-based population network model and school immunization records to identify underimmunized clusters of zip codes in the Commonwealth of Virginia. Although Virginia has high vaccine coverage for measles at the state level, finer-scale investigation at the zip code level finds three statistically significant underimmunized clusters. This research examines why some underimmunized geographical clusters are more critical in causing outbreaks and how their criticality changes with a possible drop in overall vaccination coverage. Results show that different clusters can cause vastly different outbreaks in a region, depending on their size, location, immunization rate and network characteristics. Among the three underimmunized clusters, we find one to be critical and the other two to be benign in terms of an outbreak risk. However, when the vaccine coverage among children drops by just 5% (or 0.8% overall in the population), one of the benign clusters becomes highly critical. This work also examines the demographic and network properties of these clusters to identify factors that are responsible for affecting the criticality of the clusters. Although this work focuses on measles, the methodology is generic and can be applied to study other infectious diseases.
本研究开发了一种新颖的系统科学方法,以检验对于麻疹等传染病而言,未充分免疫个体的地理聚集所引发疫情的潜在风险。我们使用基于活动的人口网络模型和学校免疫记录,来识别弗吉尼亚州邮政编码区域内未充分免疫的聚集区。尽管弗吉尼亚州在州层面麻疹疫苗接种覆盖率较高,但在邮政编码区域层面进行的更精细调查发现了三个具有统计学意义的未充分免疫聚集区。本研究探讨了为何一些未充分免疫的地理聚集区在引发疫情方面更为关键,以及随着总体疫苗接种覆盖率可能下降,它们的关键程度如何变化。结果表明,不同的聚集区在一个地区可能引发截然不同的疫情,这取决于它们的规模、位置、免疫率和网络特征。在这三个未充分免疫的聚集区中,我们发现其中一个在疫情风险方面至关重要,而另外两个则较为良性。然而,当儿童疫苗接种覆盖率仅下降5%(或总体人口中下降0.8%)时,其中一个良性聚集区就会变得高度关键。这项工作还研究了这些聚集区的人口统计学和网络特性,以确定影响聚集区关键程度的因素。尽管这项工作聚焦于麻疹,但该方法具有通用性,可应用于研究其他传染病。