Lyme disease, caused by the bacterium Borrelia burgdorferi, is a prevalent infectious disease spread by black-legged ticks (Ixodes scapularis) in eastern North America. Lyme disease affects over 300,000 individuals in the United States each year, with an increasing trend. Models of Lyme disease transmission among wildlife host species and the tick vector have led to an improved understanding of the role of biodiversity and habitat modification in the risk of Lyme disease transmission to humans. Previous models have focused on the importance of species diversity and variation in host abundance and competence to define the dilution effect of biodiversity, confirming that locations with more potential host species have lower Lyme disease levels than those with fewer host species. Our agent-based model, constructed in NetLogo, builds upon these previous models by adding biological realism in the form of host species movement. We also reduce the number of assumptions by introducing the probability of contact between ticks and hosts, which itself can be a function of habitat or host movement. The model will be used to gain a better understanding of the impact of host to host contact by exploring homing ranges.
Results/Conclusions
The use of an agent-based model has the potential to improve the accuracy of Lyme disease transmission rate indicators such as nymphal infection prevalence and the density of infected nymphs within a habitat or region. The results of our agent-based model suggest that host movement influences nymphal infection prevalence. The addition of host species specific movement led to an increase in nymphal infection prevalence and density of infected nymphs across all white-footed mice densities in comparison to a previous equation based model. Our results also confirm previous research demonstrating the presence of the dilution effect and rescue effect in regulating nymphal infection prevalence. These investigations improve our understanding of Lyme disease transmission and will potentially help reduce the number of humans infected each year.