PS 44-103 - Spatial and temporal analysis of vector-borne Lyme disease in New Hampshire

Wednesday, August 10, 2011
Exhibit Hall 3, Austin Convention Center
Christina Czarnecki1, Michael Palace1, Ernst Linder2, Peter Ingraham3, William Salas3, Chengwei Yuan4, Michael Routhier1, Nate Torbick3, David Bartlett2, Rosemary Caron2, Xiangming Xiao5 and Bobby Braswell6, (1)Complex System Research Center, University of New Hampshire, (2)University of New Hampshire, (3)Applied GeoSolutions, (4)Department of Mathematics & Statistics, University of New Hampshire, (5)Department of Microbiology and Plant Biology, University of Oklahoma, (6)Atmospheric Environmental Research

Between 1992 and 2007, the number of reported cases of Lyme disease in New Hampshire has almost tripled, and it is now considered an emerging infectious disease. It is the most prevalent vector-borne disease in the U.S., and is the most common tick-borne disease in the world. Because of the complicated parasitic relationship of  the black-legged tick, understanding the cycle and persistence of Lyme disease within the New Hampshire landscape requires knowledge of ecological, spatial, and temporal variability of the multiple host species, specifically habitat use and availability, which is directly tied to vegetation structure and landscape characteristics. Between two field seasons in 2009-2010, 570 ticks were collected from 36 field sites and tested for Lyme disease. Biometric properties of vegetation were also collected, as well as environmental conditions such as air and soil temperature, soil moisture, and air humidity. Several remote sensing data products, including a forest cover classification map, a canopy coverage map, and Synthetic Aperture Radar-based forest volume information, were used to generate landscape metrics such as forest edge density and patchiness.


Collected data was used to drive and parameterize a spatially explicit hierarchical eco-epidemiological model that is capable of exploratory analysis of Lyme disease risk in the forested landscape. A Poisson regression (with logistic link function) was used to analyze tick abundance per site for all ticks found over two field seasons. This was able to explain over 90% of the variability in our model. The most important factors in the model were forest edge density, forest patch cohesion, patch richness density, and to a lesser degree, the effects of air moisture. We found a significant difference for total number of female ticks by vegetation type. Also, a logistic regression was used to analyze incidence of Lyme disease per site. Preliminary results indicate that disease incidence was influenced by forest patch size, air/soil moisture, and Julian day.

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