Are all underimmunized measles clusters equally critical?

Sifat Afroj Moon, Achla Marathe, Anil Vullikanti

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Article number230873
JournalRoyal Society Open Science
Volume10
Issue number8
DOIs
StatePublished - Aug 16 2023

Funding

This work is partially supported by National Institutes of Health (NIH) grant no. R01GM109718, NSF (National Science Foundation) grant nos. IIS-1955797, ACI-1443054, OAC-1916805, NSF Expeditions in Computing grant no. CCF-1918656, CCF-1917819, US Centers for Disease Control and Prevention 75D30119C05935, DTRA (Defense Threat Reduction Agency) subcontract/ARA S-D00189-15-TO-01-UVA, and a collaborative seed grant from the UVA Global Infectious Disease Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsoring agencies. This work is partially supported by National Institutes of Health (NIH) grant no. R01GM109718, NSF (National Science Foundation) grant nos. IIS-1955797, ACI-1443054, OAC-1916805, NSF Expeditions in Computing grant no. CCF-1918656, CCF-1917819, US Centers for Disease Control and Prevention 75D30119C05935, DTRA (Defense Threat Reduction Agency) subcontract/ARA S-D00189-15-TO-01-UVA, and a collaborative seed grant from the UVA Global Infectious Disease Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsoring agencies. Acknowledgements

Keywords

  • agent-based model
  • anomaly detection
  • immunization
  • pattern recognition
  • simulation

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