The 80:20 rule in Lyme disease dynamics: causes and consequences of variable tick burdens on mice and chipmunks.
Brunner, Jesse*,1, Ostfeld, Richard1, 1 Institute of Ecosystem Studies, Millbrook, NY
ABSTRACT- Pathogen vectors, such as mosquitoes and ticks, tend to be aggregated on their hosts such that a small fraction of hosts feed the majority of the vectors while most hosts feed relatively few. Those hosts that are frequently bitten or highly infested are generally going to be infected earlier, more often, and with heavier pathogen loads than the rest of the population, and will therefore be responsible for infecting the bulk of new and naïve vectors. Identifying these hosts is therefore important for understanding transmission dynamics and also for designing effective intervention strategies. We were interested in the distribution of blacklegged ticks, Ixodes scapularis, on small mammals, and how aggregation on these hosts might influence transmission of the Lyme disease spirochete, Borrelia burgdorferi, both to these rodents and to humans. We used a likelihood approach to identify host characteristics associated with large tick burdens in an 11-year dataset of larval and nymphal tick counts on white-footed mice (Peromyscus leucopus) and eastern chipmunks (Tamias striatus) in an oak-dominated forest in southeastern New York. Once year-to-year variability was taken into account, individual host characteristics, such as sex, age, and size, were strong predictors of tick burdens, whereas population-level characteristics such as population density had little support. There was also evidence of a positive correlation between nymphal and larval burdens, indicating that those individuals that feed the next generation of ticks are also the most likely to be infectious. We then used some simple models to examine how the strongly clumped tick distribution influences Borrelia transmission from nymphal ticks to mice and chipmunks to larval ticks, and how different control strategies might alter the prevalence of Borrelia infection.
Key words: disease ecology, aggregation and transmission, likelihood
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