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

Abstract

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.

Published
2023-05-10
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.
Section
Research