Finding Spatial Clusters Susceptible to Epidemic Outbreaks due to Undervaccination

Area of Science:

• Epidemiology
• Public Health
• Computational Biology

Background:

• Geographical clusters of undervaccinated populations present a significant public health challenge in the US.
• Current surveillance and intervention strategies are resource-intensive, necessitating efficient prioritization methods.

Purpose of the Study:

• To develop and evaluate efficient algorithms for identifying and rank-ordering critical clusters of undervaccinated populations.
• To quantify cluster criticality based on the potential increase in infections due to underimmunization.

Main Methods:

• Formulated the problem as maximizing a submodular function on a graph with connectivity constraints.
• Developed efficient approximation algorithms to identify clusters with maximum criticality.
• Applied the methods to vaccination data from Minnesota.

Main Results:

• The developed algorithms identified clusters with significantly higher criticality compared to existing heuristics.
• Demonstrated the effectiveness of the computational approach in prioritizing public health interventions.
• Highlighted the potential for improved resource allocation in disease surveillance.

Conclusions:

• Efficient algorithms can accurately identify critical undervaccinated clusters, improving public health resource allocation.
• The submodular maximization framework provides a robust method for epidemiological cluster analysis.
• Prioritizing interventions in high-criticality clusters can enhance disease prevention efforts.