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Two-pathogen model with competition on clustered networks.

Peter Mann1,2,3, V Anne Smith3, John B O Mitchell2

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Clustering in social networks impacts disease spread. This study shows clustering can lower the threshold for a second disease strain

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Biology

Background:

  • Social networks are crucial for disease transmission modeling.
  • Real-world social networks exhibit high clustering, deviating from simple tree-like structures.
  • Understanding how network structure influences epidemic dynamics is vital.

Purpose of the Study:

  • To investigate the impact of network clustering on the spread of sequential pathogen strains.
  • To derive conditions for the coexistence threshold of a second disease strain.
  • To compare these effects in different network models.

Main Methods:

  • Utilizing a generating function formulation for disease spread.
  • Analyzing sequential strains with complete cross-immunity.
  • Comparing results in Poisson and uniform-degree network models.
  • Extending the analysis to multilayer clustered networks.

Main Results:

  • Clustering reduces the coexistence threshold and outbreak size of the second strain in Poisson networks.
  • Clustering increases the coexistence threshold and outbreak size in uniform-degree networks.
  • The second wave of an epidemic is facilitated by population clustering.
  • Multilayer clustered networks show graph fracturing at specific transmissibilities.

Conclusions:

  • Network clustering has a complex, model-dependent effect on epidemic dynamics.
  • The findings highlight the importance of network topology in predicting disease spread.
  • Clustering generally enhances the potential for secondary epidemic waves.