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Indirect interactions influence contact network structure and diffusion dynamics.

Md Shahzamal1,2, Raja Jurdak1,2, Bernard Mans1

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Summary
This summary is machine-generated.

Delayed indirect interactions, or same place different time (SPDT) transmission, significantly increase airborne disease spread. Current models focusing only on direct contact (SPST) underestimate outbreak dynamics and are not reproducible by SPDT models.

Keywords:
contact networkdiffusion processepidemiologyinfluenzamathematical model

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Area of Science:

  • Epidemiology
  • Network Science
  • Computational Biology

Background:

  • Diffusion models typically consider only direct interactions for transmission.
  • Indirect interactions, where transmission occurs after an infected individual leaves, are often overlooked.
  • These indirect pathways can significantly impact disease spread dynamics.

Purpose of the Study:

  • To introduce a novel diffusion model, same place different time (SPDT) transmission, accounting for indirect interactions.
  • To investigate the impact of SPDT transmission on airborne disease spread.
  • To compare SPDT model dynamics with traditional same place same time (SPST) models.

Main Methods:

  • Developed the SPDT diffusion model incorporating transmission links for indirect interactions.
  • Simulated airborne disease spreading on data-driven contact networks.
  • Analyzed network dynamics influenced by varying link infectivity decay rates.

Main Results:

  • The SPDT model demonstrated significantly increased diffusion dynamics and higher transmission rates.
  • SPDT models create denser and stronger network connectivity compared to SPST models.
  • SPST models failed to reproduce SPDT diffusion dynamics, even with identical underlying connectivity.

Conclusions:

  • Indirect transmission links are crucial for realistic modeling of airborne disease outbreaks.
  • SPDT models provide a more accurate representation of disease spread than SPST models.
  • The distinct transmission dynamics of indirect links necessitate their inclusion in predictive models.