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Mathematical models for communicable disease spread now incorporate network science and data analysis. This study introduces a framework accounting for both individual connectivity and population age structure, crucial for accurate epidemic threshold determination and intervention strategies.

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Biology

Background:

  • Advancements in data analysis and network science have improved communicable disease modeling.
  • Current models often fail to capture population heterogeneities like individual connectivity and age structure.

Purpose of the Study:

  • To develop a mathematical framework integrating individual connectivity and age structure for disease modeling.
  • To compare epidemic thresholds across different modeling scenarios, including homogeneous mixing, social mixing, connectivity alone, and a multilayer approach.

Main Methods:

  • Analytical derivations of epidemic thresholds for four distinct modeling scenarios.
  • Extensive numerical simulations to assess the impact of heterogeneity on epidemic dynamics.

Main Results:

  • Epidemic thresholds vary significantly across the four considered scenarios.
  • Contact network heterogeneity is critical for determining the epidemic threshold.
  • Population age structure becomes more influential after the initial outbreak phase.

Conclusions:

  • Both individual connectivity and age structure are vital for accurate modeling of infectious disease spread and evaluating interventions like vaccination.
  • Ignoring these heterogeneities can lead to significant errors in predicting epidemic outcomes and planning public health strategies.