Modelling evolution of virulence in populations with a distributed parasite load

Area of Science:

• Evolutionary Biology
• Epidemiology
• Mathematical Modelling

Background:

• Host-parasite interactions are crucial in evolutionary biology, with virulence evolution being a key research area.
• Existing models often simplify infected populations, overlooking variable infection loads within individuals.
• Individual infection load empirically influences mortality and infectiousness, with load distribution varying over time.

Purpose of the Study:

• To investigate the impact of distributed infection load on the evolution of virulence in host-parasite systems.
• To compare evolutionary outcomes in disease-structured populations versus unstructured models.
• To explore the conditions under which evolutionary stable strategies can be achieved.

Main Methods:

• Developed a mathematical SI model using a von Förster-type equation to describe infected subpopulation dynamics, with infection load as the 'age' variable.
• Employed the adaptive dynamics framework to predict evolutionary trajectories and outcomes.
• Analyzed simple trade-off functions between virulence, disease transmission, and parasite growth rates.

Main Results:

• Distributed infection load significantly alters virulence evolution compared to unstructured models.
• Multiple evolutionary attractors can emerge under simple trade-off scenarios.
• Achieving an evolutionary stable strategy becomes possible by altering a single ecological parameter (parasite growth rate) in structured populations.

Conclusions:

• The distribution of parasite load within an infected population is a critical factor mediating virulence evolution.
• Disease structuring introduces novel evolutionary dynamics and potential stable states not seen in simpler models.
• Understanding parasite load distribution is essential for predicting evolutionary responses in host-parasite systems.