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Updated: Aug 7, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
Published on: February 3, 2023
Disease evolution across a range of spatio-temporal scales
Jonathan M Read1, Matt J Keeling
1Department of Biological Sciences and Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK. jonathan.read@warwick.ac.uk
Infectious diseases evolve due to trade-offs between within-host, between-host, and population-level pressures. This complex interplay can lead to multiple stable disease states, challenging traditional evolutionary models.
Area of Science:
- Epidemiology
- Evolutionary Biology
- Mathematical Modeling
Background:
- Traditional models predict infectious diseases evolve to be highly transmissible and benign.
- Observed disease dynamics often deviate from these predictions.
- A multi-scale evolutionary framework is needed.
Purpose of the Study:
- To investigate infectious disease evolution considering multiple adaptive pressures.
- To model pathogen evolution at within-host, between-host, and population levels.
- To explore the emergence of complex epidemiological and evolutionary dynamics.
Main Methods:
- Developed a model integrating within-host pathogen dynamics.
- Incorporated transmission between hosts via an explicit contact network.
- Modeled transmission as a function of host-pathogen-immune interaction and host contact rates.
Main Results:
- Demonstrated that evolutionary pressures at different scales constrain pathogen behavior.
- Revealed complex dynamics arising from feedbacks between epidemiology and evolution.
- Identified the potential for multiple stable disease states.
Conclusions:
- Infectious disease evolution is shaped by multi-scale adaptive trade-offs.
- Feedback loops between epidemiological and evolutionary processes drive complex outcomes.
- Stochastic switching between multiple stable states is a possible consequence.
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