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Area of Science:

  • Epidemiology
  • Evolutionary Biology
  • Mathematical Modeling

Background:

  • Epidemic dynamics are often simplified using the basic reproduction number (R0).
  • Pathogen evolution during spread can alter R0, potentially causing mutation-driven pandemics.
  • Predicting pandemic emergence requires understanding coupled spread and evolution.

Purpose of the Study:

  • To develop a modeling framework integrating inter-host spreading and intra-host evolution.
  • To investigate how pathogen mutation dynamics influence pandemic phase transitions.
  • To identify conditions enabling initially non-pandemic pathogens to spread widely.

Main Methods:

  • Coupling network-based epidemic spreading models with evolutionary dynamics.
  • Analyzing the impact of mutation rates and selection forces on pandemic potential.
  • Defining a phase diagram influenced by both epidemic and evolutionary timescales.

Main Results:

  • Mutations can fundamentally alter pandemic phase diagrams, even with independent selection.
  • Pandemic transitions depend on R0 and the balance between epidemic and evolutionary timescales.
  • A critical window exists where initially sub-pandemic pathogens can achieve widespread prevalence through mutation.

Conclusions:

  • The interplay between pathogen evolution and epidemic spread is crucial for predicting pandemic emergence.
  • Mutation speed critically influences whether a pathogen can achieve widespread prevalence.
  • Understanding these coupled dynamics can inform public health strategies against evolving pathogens.