Wildlife vaccination is an important tool for managing the burden of infectious disease in human populations, domesticated livestock and various iconic wildlife. Although substantial progress has been made in the field of vaccine designs for wildlife, there is a gap in our understanding of how to time wildlife vaccination, relative to host demography, to best protect a population. We use a mathematical model and computer simulations to assess the outcomes of vaccination campaigns that deploy vaccines once per annual population cycle. Optimal timing of vaccination is an important consideration in animals with short to intermediate life spans and a short birthing season. Vaccines that are deployed shortly after the birthing season best protect the host population. The importance of timing is greater in wildlife pathogens that have a high rate of transmission and a short recovery period. Vaccinating at the end of the birthing season best reduces the mean abundance of pathogen‐infected hosts. Delaying vaccination until later in the year can facilitate pathogen elimination. Policy Implications. Tuning wildlife vaccination campaigns to host demography and pathogen traits can substantially increase the effectiveness of a campaign. Our results suggest that, for a fluctuating population, vaccinating at, or shortly after, the end of the birthing season, best protects the population against an invading pathogen. If the pathogen is already endemic, delaying vaccination until after the birthing season is over can help facilitate pathogen elimination. Our results highlight the need to better understand and predict host demography in wildlife populations that are targeted for vaccination.

Zoonotic pathogens spread by wildlife continue to spill into human populations and threaten human lives. A potential way to reduce this threat is by vaccinating wildlife species that harbor pathogens that are infectious to humans. Unfortunately, even in cases where vaccines can be distributed en masse as edible baits, achieving levels of vaccine coverage sufficient for pathogen elimination is rare. Developing vaccines that self-disseminate may help solve this problem by magnifying the impact of limited direct vaccination. Although models exist that quantify how well these self-disseminating vaccines will work when introduced into temporally stable wildlife populations, how well they will perform when introduced into populations with pronounced seasonal population dynamics remains unknown. Here we develop and analyze mathematical models of fluctuating wildlife populations that allow us to study how reservoir ecology, vaccine design, and vaccine delivery interact to influence vaccine coverage and opportunities for pathogen elimination. Our results demonstrate that the timing of vaccine delivery can make or break the success of vaccination programs. As a general rule, the effectiveness of self-disseminating vaccines is optimized by introducing after the peak of seasonal reproduction when the number of susceptible animals is near its maximum.

The ecology and life history of wild animals influences their potential to harbour infectious disease. This observation has motivated studies identifying empirical relationships between traits of wild animals and historical patterns of spillover and emergence into humans. Although these studies have identified compelling broad-scale patterns, they are generally agnostic with respect to underlying mechanisms. Here, we develop mathematical models that couple reservoir population ecology with viral epidemiology and evolution to clarify existing verbal arguments and pinpoint the conditions that favour spillover and emergence. Our results support the idea that average lifespan influences the likelihood of an animal serving as a reservoir for human infectious disease. At the same time, however, our results show that the magnitude of this effect is sensitive to the rate of viral mutation. Our results also demonstrate that viral pathogens causing persistent infections or a transient immune response within the reservoir are more likely to fuel emergence. Genetically explicit stochastic simulations enrich these mathematical results by identifying relationships between the genetic basis of transmission and the risk of spillover and emergence. Together, our results clarify the scope of applicability for existing hypotheses and refine our understanding of emergence risk.