Lyme disease (LD) is the most important tick-associated disease in the United States. A variety of prevention and control methods, including personal protective measures, habitat modification, applications of biological and natural compounds, and host-targeted control measures, have been examined for reducing tick abundance and risk for LD. The primary aim of this proposal is to develop, implement, and evaluate individual and integrated tick management (ITM) approaches to reduce the entomological risk of LD. In addition, an enhanced LD decision support system will be constructed by incorporating the new findings obtained from the proposed project. The practical application of an effective tick bite protection program requires easy public access to decision support tools for promoting widespread usage of tick control strategies. We propose to examine the efficacy and associated costs of several existing and new tick control measures individually as well in the framework of the ITM approach to reduce the number of infected ticks in the inland LD endemic neighborhoods. An economic cost-benefit analysis for tick management and LD will involve costs associated with medical expenses for diagnosis, treatment, and productivity losses versus costs of prevention and control. The proposed ITM approach is a novel combination of applications of biological (insect pathogenic fungus) and natural less-toxic compounds, pesticides, habitat modification, and host reduction. We will improve upon the timing and frequency of spray applications by analyzing the residual activity of natural compounds in comparison with synthetic products. In addition, we have developed molecular techniques to identify hosts of tick vectors to directly estimate vector-contact with different vertebrate species, which is essential for evaluating their relative importance as reservoir hosts for LD and other tick-associated diseases. Furthermore, by taking advantage of a molecular epidemiological approach, we will track the temporal distribution of the LD- causing spirochetes and other pathogens in tick populations and reservoir hosts in conjunction with our tick management program. In short, by adapting a novel ITM strategy, we will examine and compare shared and dissimilar components of an ITM approach for LD control. By incorporating entomological data and other information acquired by the proposed project into our decision support system, we will provide guidance on the most effective control measures that will help stakeholders to protect themselves in an environmentally safe manner. The approach and findings generated from this project will easily be extended to other LD endemic regions. Keywords: blacklegged tick;ITM;Lyme disease;biological control;landscape management;blood-meal analysis;pathogen screening;cost-benefit analysis;deer management;decision support system.

Public Health Relevance

The proposed Integrated Tick Management project will be conducted in the Town of Redding in Fairfield County, Connecticut;an area highly endemic for Lyme disease (LD). This project combines the expertise of researchers at The Connecticut Agricultural Experiment Station (CAES), Yale University (Yale), the University of Rhode Island (URI), and White Buffalo, Inc. (WBI). We will provide quantitative assessment of the reduction in the prevalence of ticks infected with the LD agent, and other pathogens by the application of synthetic and least-toxic compounds for tick control, landscape management through the control of Japanese barberry, and reduction of white-tailed deer population density. In order to evaluate the various strategies for managing disease risk, we will conduct a cost-benefit economic analysis that builds on existing science, new information from the project, and the economics associated with the disease. A LD decision support system will be developed to provide residents guidance on determining the best individual and ITM approaches.

National Institute of Health (NIH)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZCK1)
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Connecticut Agricultural Experiment Sta
New Haven
United States
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