Abstract
AbstractDisruptions in routine immunizations due to the COVID-19 pandemic have been a cause of significant concern for health organizations worldwide. This research develops a system science approach to examine the potential risk of 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 at the state level for measles, finer-scale investigation at the zip code level finds three statistically significant underimmunized clusters. To estimate the criticality of these clusters, a stochastic agent-based network epidemic model is used. Results show that different clusters can cause vastly different outbreaks in the region, depending on their size, location, and network characteristics. This research aims to understand why some underimmunized geographical clusters do not cause a large outbreak while others do. A detailed network analysis shows that it is not the average degree of the cluster or the percentage of underimmunized individuals in the cluster but the average eigenvector centrality of the cluster that is important in determining its potential risk.
Publisher
Cold Spring Harbor Laboratory
Reference53 articles.
1. SIS reporting. Virginia student immunization status survey. https://www.vdh.virginia.gov/immunization/sisresultsarchived/, 2021. [Online;accessed 10-Nov-2022].
2. Roy M Anderson and Robert M May . Infectious diseases of humans: dynamics and control. Oxford university press, 1992.
3. ATUS. American time use survey, u.s. bureau of labor statistics. https://www.bls.gov/tus/, 2021. [Online;accessed 15-Dec-2022].
4. Nonmedical Vaccine Exemptions and Pertussis in California, 2010
5. Network science
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