The network level reproduction number for infectious diseases with both vertical and horizontal transmission

Ling Xue1, Caterina Scoglio

  • 1Department of Electrical & Computer Engineering, Kansas State University, KS 66506, USA. lxue@ksu.edu

Insights

This study provides a new formula for the basic reproduction number (R0) of infectious diseases transmitted both vertically and horizontally across multiple species in complex networks. This metric helps predict disease spread in diverse environments.

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Network Science

Background:

  • Infectious diseases spread through vertical and horizontal transmission, often involving multiple species in complex environments.
  • Metapopulation models are used to understand disease dynamics in spatially structured populations.
  • The basic reproduction number (R0) is crucial for predicting disease invasion and spread.

Purpose of the Study:

  • To derive an explicit expression for the basic reproduction number (R0) for diseases with both vertical and horizontal transmission in heterogeneous networks.
  • To analyze the factors influencing R0 in multi-species, multi-node systems.
  • To apply the derived R0 to a relevant zoonotic disease, Rift Valley fever.

Main Methods:

  • Development of a metapopulation model, starting from simple two-species, two-node networks.
  • Derivation of R0 as the spectral radius of a reduced next-generation matrix.
  • Numerical simulations to explore R0 under varying parameters and network structures.

Main Results:

  • An explicit formula for R0 was derived, dependent on vertical and horizontal transmission parameters.
  • The derived R0 has a lower bound equal to the R0 for horizontal transmission alone.
  • Rift Valley fever R0 was calculated, showing livestock movement impacts spread only in heterogeneous networks.

Conclusions:

  • The study provides a versatile R0 calculation for diseases with mixed transmission routes in complex networks.
  • The derived R0 is adaptable for specific diseases, offering insights into their evolutionary dynamics.
  • Understanding R0 in heterogeneous networks is vital for predicting and managing infectious disease spread.

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