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Projecting social contact matrices to different demographic structures.

Sergio Arregui1,2, Alberto Aleta1,2, Joaquín Sanz3,4

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This study introduces a method to adjust contact matrices for varying demographics, crucial for accurate epidemic modeling. Ignoring demographic shifts significantly impacts disease incidence predictions, highlighting the need for dynamic models.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Accurate epidemic modeling relies on detailed contact network data.
  • Age is a primary factor influencing individual contact patterns, reflected in age-specific contact matrices.
  • Existing models often overlook how demographic changes affect contact matrices over time and across regions.

Purpose of the Study:

  • To develop a methodology for transforming contact matrices between different demographic structures.
  • To enable extrapolation of contact patterns to regions with unknown empirical data.
  • To analyze the impact of demographic-driven temporal evolution of contact matrices on epidemic modeling.

Main Methods:

  • Developed a transformation method for age-dependent contact matrices based on demographic structure.
  • Applied a Susceptible-Exposed-Infected-Recovered (SEIR) model across 16 countries/regions.
  • Quantified the effect of time-varying contact patterns driven by demographic dynamics on disease incidence.

Main Results:

  • Simulated disease incidence rates, both aggregated and age-specific, were significantly influenced by demographic-driven contact structure variations.
  • The methodology allows for adapting empirical contact matrices to different demographic profiles and tracking their temporal evolution.
  • Neglecting the temporal evolution of mixing patterns leads to considerable inaccuracies in epidemic projections.

Conclusions:

  • The study provides a method to correct for demographic variations and temporal dynamics in contact matrices for improved epidemic modeling.
  • This approach can eliminate technical biases in evaluating future epidemic threats.
  • It is crucial to account for demographic evolution and geographic variations when using contact matrices for disease modeling.