Disease invasion risk in a growing population

Sanling Yuan1, P van den Driessche2, Frederick H Willeboordse3

  • 1College of Science, Shanghai University for Science and Technology, Shanghai, China.

Insights

Infectious disease spread in growing populations can be unpredictable. The basic reproduction number may peak before the population reaches its maximum, impacting disease control strategies.

Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Biology

Background:

  • Classic epidemic models often assume homogeneous mixing, leading to simplified assumptions about disease spread based on population size.
  • Standard incidence and mass action models predict different relationships between population size and disease transmission dynamics.
  • These models may not accurately reflect disease spread in populations with evolving contact structures.

Purpose of the Study:

  • To investigate how disease spread dynamics, specifically the basic reproduction number, are affected by population growth on evolving contact networks.
  • To challenge the assumptions of classic epidemic models in the context of dynamic populations.
  • To provide insights for more effective disease control planning in growing populations.

Main Methods:

  • Modeling the dynamics of the average excess degree of a random contact network.
  • Incorporating population dynamics such as constant input, proportional deaths, and preferential attachment.
  • Analyzing the spectral radius of the contact matrix as a proxy for the basic reproduction number.

Main Results:

  • The basic reproduction number can reach its maximum during population growth, not necessarily at maximum population size.
  • Average excess degree serves as a good approximation for the spectral radius of the contact matrix.
  • Findings hold for both preferential and uniform attachment of incoming contacts, and various population dynamics.

Conclusions:

  • Disease control strategies based on maximum population size may be insufficient for growing populations.
  • Understanding transmission dynamics on evolving networks is crucial for predicting and managing epidemics.
  • Early intervention based on transmission potential during growth phases is essential.

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