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• W4366292022 abstract "Disruptions 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." @default.
• W4366292022 created "2023-04-20" @default.
• W4366292022 creator A5017986637 @default.
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• W4366292022 date "2023-04-17" @default.
• W4366292022 modified "2023-10-18" @default.
• W4366292022 title "Are all underimmunized measles clusters equally critical?" @default.
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• W4366292022 doi "https://doi.org/10.1101/2023.04.11.23288263" @default.
• W4366292022 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37131740" @default.
• W4366292022 hasPublicationYear "2023" @default.
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