Population dynamics in mammal hosts impact disease risk in mathematical models of Lyme disease

Background/Question/Methods

Lyme disease is the most common vector borne disease in the United States and is rapidly expanding in North America due to changing climates and disturbed environments. Theoretical models are useful tools for testing expensive management strategies and predicting disease risk across time and space. A number of mathematical models have been created which model disease dynamics in a community of tick vectors and vertebrate hosts. The dynamics of tick populations are highly temporal and instrumental to modeling disease risk. Meanwhile, few if any models consider host dynamics, and rather keep host densities constant. This is surprising, as many vertebrate populations, notably mice, undergo significant fluctuations in density intra- and interannually, and the impact of these population dynamics on disease risk is unknown. Many measures of disease risk may be affected by host dynamics, importantly tick density and disease prevalence, which may shift in response to changes in host densities as well as how birth and death rates are modeled. I have developed a discrete model which simulates Lyme disease dynamics through a population of ticks and several hosts, including mice. Mice respond dynamically to simulated resource levels, allowing for analysis of the system under constant or variable population densities.

Results/Conclusions

Preliminary results indicate that host dynamics have important implications for measures of disease risk. Dynamic host populations negatively affect the tick population, which cannot respond quickly to changing host densities. Variable birth and death rates among hosts result in reduced infection prevalence during periods of increased host recruitment, and likewise increased prevalence during periods of decreased recruitment. Simulations run under varying climates demonstrate that environmental variability interacts with temporal host dynamics on local scales and suggests research into the spatial heterogeneity of host species, a further layer of variability which is likely to impact disease dynamics. This work demonstrates the importance of host populations when modeling disease risk and indicates the need to study the impact of the vertebrate community on the Lyme disease system more fully. This will guide the creation of informative and robust models and increase understanding of the disease system.