Epidemiology, Game Theory, and Evolutionary Rescue

Understanding How Outbreaks Impact Population Viability

  • Brandon Grandison University of Tennessee
  • Hannah Yin University of Tennessee
  • Ana Kilgore University of Tennessee
  • Matthew J. Young University of Tennessee
  • Jing Jiao University of Tennessee
  • Nina H. Fefferman University of TN, Knoxville
Keywords: conservation epidemiology, infectious selective pressure, evolutionary rescue, life-history-epidemiology trade-offs


Evolutionary game theory (EGT) analyzes the stability of competing strategies for withstanding selective pressures within a population over generations. Under rapid shifts in selective pressures (e.g., introduction of a novel pathogen), evolutionary rescue may preserve a population, but how it may re-stabilize over generations is also critical for estimations of population persistence. Here, we present a simple model that couples EGT with epidemiology to investigate evolutionary rescue under a novel and epidemiologically-driven dynamic selective pressure from an infectious outbreak. We consider a hypothetical population where payoffs from competing wild-type and mutant strategies reflect immune-reproductive trade-offs. Our study shows evolutionary rescue occurs under higher wild-type fecundity and a lower-bounded boost in mutant immunity prolongs the timescale of evolutionary rescue. Higher disease-induced mortality in the wild-type and a larger mutant immunity significantly reinforce the pattern. This model reveals transient synergies between epidemiological and evolutionary dynamics during evolutionary rescue during novel infectious outbreaks.

How to Cite
Grandison, Brandon, Hannah Yin, Ana Kilgore, Matthew Young, Jing Jiao, and Nina Fefferman. 2023. “Epidemiology, Game Theory, and Evolutionary Rescue”. Letters in Biomathematics 10 (1), 75–86. https://doi.org/10.30707/LiB10.1.1684158870.855659.