The impact of heterogeneous contact structure on the evolution of pathogen virulence

Xander O'Neill1, Andy White1, Matthew J Silk2

  • 1Maxwell Institute for Mathematical Sciences and Department of Mathematics, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom.

Insights

Pathogen evolution is shaped by host interactions. Superspreading, common in real-world diseases, drives the evolution of less virulent pathogens when local population structure is considered.

Area of Science:

  • Evolutionary biology
  • Epidemiology
  • Disease ecology

Background:

  • Classical pathogen evolution models assume homogeneous hosts and random interactions.
  • Real biological systems feature local interactions and heterogeneity, with 'superspreaders' dominating transmission.
  • Previous research indicated superspreading had limited evolutionary impact without affecting host survival.

Purpose of the Study:

  • To investigate the evolutionary impact of superspreading in structured host populations.
  • To develop a more realistic model incorporating both local interactions and transmission heterogeneity.
  • To determine if superspreading drives pathogen virulence evolution.

Main Methods:

  • Incorporated local population structure and transmission heterogeneity into theoretical models.
  • Analyzed evolutionary dynamics under these combined factors.
  • Utilized social network analysis principles.

Main Results:

  • When local population structure is considered, heterogeneity significantly drives virulence evolution.
  • Superspreading selects for pathogens that transmit more slowly and cause less host damage (i.e., are less virulent).
  • This contrasts with models assuming random transmission, where superspreading's evolutionary effect is minimal.

Conclusions:

  • Superspreading plays a crucial, potentially overlooked, role in pathogen evolution, distinct from its epidemiological impact.
  • Accounting for population structure and heterogeneity provides a more accurate framework for predicting disease evolution.
  • Findings are relevant to understanding numerous wildlife and human infectious diseases.

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