How do host population dynamics impact Lyme disease risk dynamics in theoretical models?

Abstract

Lyme disease is the most common wildlife-to-human transmitted disease reported in North America. The study of this disease requires an understanding of the ecology of the complex communities of ticks and host species involved in harboring and transmitting this disease. Much of the ecology of this system is well understood, such as the life cycle of ticks, and how hosts are able to support tick populations and serve as disease reservoirs, but there is much to be explored about how the population dynamics of different host species and communities impact disease risk to humans. One way in which we can study disease effectively is through the use of theoretical models. These are powerful tools that allow investigation of complex species interactions before staging complicated and expensive studies that may not be productive. We construct a model to investigate how host population dynamics can affect disease risk to humans. The model describes a tick population and a simple community of three species in which mouse populations are made to fluctuate on an annual basis. We tested the model under different environmental conditions to examine the effect of environment on the interactions of host dynamics and disease risk. Results indicate that host dynamics reduce mean nymphal infection prevalence and increase the yearly amplitude of nymphal infection prevalence and the density of infected nymphs. Effects were nonlinear and patterns in the effect of dynamics on amplitude in nymphal infection prevalence varied across locations. These results highlight the importance of further study of the effect of communitiy dynamics on disease risk. This will involve the construction of further theoretical models and collection of robust field data to inform these models. With a more complete understanding of disease dynamics we can begin to better determine how to predict and manage disease risk using these models.