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Controlling Lyme Disease by Modifying the Density and Species Composition of Tick Hosts

Van Buskirk, Josh, Ostfeld, Richard S.·1995·Ecological Applications

Abstract

We used computer simulation of Lyme disease to test the hypothesis that infection rates of ticks and risk to humans could be reduced by controlling populations of the vertebrate hosts of vector ticks. The model was patterned after the life cycle of the deer tick (Ixodes scapularis). Annual survival and fecundity were dependent on the availability of hosts. The likelihood of ticks becoming infected depended on the reservoir competence of hosts and the previous exposure of hosts to infected ticks. Our results demonstrated that the density of ticks was more sensitive to the availability of hosts for juveniles than hosts for adults, suggesting that control of Lyme disease by reducing host densities would prove difficult, since the numerous alternative hosts for juvenile ticks are not amenable to control. The infection rate of ticks depended critically on the reservoir competence of host species parasitized by juveniles. We conclude that measures that alter the species composition of small mammals and birds, and hence modify the average reservoir competence of hosts for juveniles, could be used to manipulate the risk of Lyme disease without actually changing the density of ticks.

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Metadata

Authors
Van Buskirk, J., & Ostfeld, R. S.
Venue
Ecological Applications
Publisher
Wiley
Published
1995-11
Volume
5
Issue
4
Pages
1133-1140
ISSN
  • 1051-0761 (ISSN-L)
  • 1939-5582
OpenAlex
W2111393919
Cited by
169
References
0
FWCI
1.13
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Controlling Lyme Disease by Modifying the Density and Species Composition of Tick Hosts. https://app.cochat.ai/papers/10.2307/2269360
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Van Buskirk, J., & Ostfeld, R. S. (1995). Controlling Lyme Disease by Modifying the Density and Species Composition of Tick Hosts. Ecological Applications. https://doi.org/10.2307/2269360

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.