Vector-Borne diseases have proven particularly difficult to study due to the association with host life cycles and behavior. Temporal and spatial variability of both vector-borne pathogens and host species insert additional complexity for study and management practices.  Our research integrates earth observations, infectious disease ecology and public health for the study of vector-born Lyme disease in Southern New Hampshire, through the collection of field data, associating vegetation structural properties with host and tick habitats, and using the spatial and temporal information derived from remote sensing products to drive statistical eco-epidemiological models. Thirty field sites in eight vegetation types were established for data and specimen collection.  Biometric properties of vegetation were collected at all sites.  Sites were visited and dragged for ticks every 21 days from April 2009 to November 2009. Meteorological data was collected at the sites during these visits.  Ticks were identified in our lab for species, life stage and sex and then tested  for Borrelia burgdorferi at the New Hampshire Public Health Laboratory  state laboratory. 

Results/Conclusions

A preliminary exploratory analysis, using Wilcoxon/Kruskal–Wallis Rank Sum tests, was conducted to examine population numbers of ticks and ticks testing positive for Lyme disease. We found a significant difference for total number of female ticks by vegetation type (Wilcoxon test, p < 0.02).  Male ticks and the total number of ticks testing positive for Lyme disease also showed significant differences by vegetation types (Wilcoxon test, p < 0.01). Several remote sensing data products including an updated forest cover classification map, a canopy coverage map (fractional cover), and Synthetic Aperture Radar (SAR)-based forest volume information (biomass) are in the process of development. This spatially comprehensive data is used to drive and parameterize a spatially explicit hierarchical eco-epidemiological model that is capable of exploratory analysis of human Lyme disease cases and can predict and estimate Lyme disease risk which will be communicated to the public.