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Epidemic spreading in random rectangular networks.

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Area of Science:

  • Epidemiology and Network Science

Background:

  • Classical epidemiological models often overlook spatial factors.
  • Network theory, particularly random geometric graphs (RGGs), incorporates spatial properties.
  • Real-world disease spread, like in plants, is influenced by field geometry.

Purpose of the Study:

  • To generalize RGGs for disease propagation in non-square fields.
  • To analyze disease spread on random rectangular graphs.
  • To determine the epidemic threshold's lower bound in these networks.

Main Methods:

  • Utilizing network theory and random rectangular graphs.
  • Implementing susceptible-infected-susceptible (SIS) or susceptible-infected-recovered (SIR) models.
  • Conducting extensive numerical simulations and analytical derivations.

Main Results:

  • Network elongation increases the epidemic threshold.
  • More elongated fields enhance disease spread resilience.
  • Results align with empirical data on plant disease propagation.

Conclusions:

  • Field shape significantly impacts disease propagation dynamics.
  • Elongated plots offer greater resistance to epidemics.
  • This research provides insights for disease management in spatially structured populations.