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Declining vaccine coverage increases disease risk. Our study shows preferential mixing limits breakthrough infections, contrary to random mixing models, highlighting the need for proactive vaccination strategies.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Declining vaccine coverage in the US heightens the risk of vaccine-preventable disease outbreaks.
  • Conventional epidemic theory predicts a nonlinear relationship between vaccine coverage and breakthrough infections, potentially fueling vaccine hesitancy.

Purpose of the Study:

  • To test predictions of conventional epidemic theory regarding breakthrough infections.
  • To investigate the role of preferential mixing (assortativity) in measles outbreaks.
  • To evaluate the impact of assortativity on breakthrough infection dynamics and population risk.

Main Methods:

  • Analysis of measles outbreak data from seven US states.
  • Development and application of a compartmental disease model incorporating preferential mixing.
  • Leveraging school-level MMR (measles, mumps, and rubella) vaccine coverage data from sixteen states.

Main Results:

  • Observed fraction of breakthrough infections was lower than predicted by random mixing models.
  • A model accounting for preferential mixing accurately predicted observed breakthrough infection rates.
  • Substantial assortativity was found in school-level MMR coverage data across states.
  • The model predicts breakthrough infections peak at intermediate coverage levels, below herd immunity.

Conclusions:

  • Preferential mixing significantly reduces breakthrough infections compared to random mixing assumptions.
  • Current MMR coverage levels place many US counties at risk for increased breakthrough infections if coverage declines.
  • Proactive vaccination outreach and monitoring are crucial to contextualize breakthrough infections and prevent disease resurgence.