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Asymmetric disease dynamics in multihost interconnected networks.

Shai Pilosof1, Gili Greenbaum2, Boris R Krasnov3

  • 1Department of Ecology and Evolution, University of Chicago, 1103 E 57 st, Chicago, 60637, USA.

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Understanding cross-species disease transmission is key for public health. This study reveals how infection probabilities between different host species impact epidemic risk and spread in multihost systems.

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Biology

Background:

  • Epidemic spread in single-host systems is well-studied.
  • Limited knowledge exists on disease transmission across multiple host populations.
  • Cross-species transmission dynamics are crucial for understanding zoonotic diseases.

Purpose of the Study:

  • To analyze epidemic spread in multihost systems using a multilayer network model.
  • To investigate the impact of infection source and inter-host infection asymmetry on disease risk.
  • To quantify outbreak probability and size in a focal host population.

Main Methods:

  • Developed an analytic framework for the SIR (Susceptible-Infected-Recovered) epidemic model.
  • Utilized a multilayer network representing two distinct host populations.
  • Employed numerical simulations to validate analytical findings.

Main Results:

  • Outbreak probability depends on a complex interplay between infection source and between-host transmission probabilities.
  • Outbreak size is primarily influenced by the infection probability from the non-focal to the focal host.
  • Asymmetry in inter-specific infection rates significantly shapes disease dynamics in multihost networks.

Conclusions:

  • Disease risk in multihost systems is shaped by inter-specific transmission asymmetries.
  • Considering multiple measures of disease risk (probability and size) is essential.
  • The study provides a flexible model for multihost disease dynamics, emphasizing the need for empirical data on cross-species infection rates and network structures.