Landscape risk factors for Lyme disease in the eastern broadleaf forest province of the Hudson River valley and the effect of explanatory data classification resolution

Spatial and Spatio-temporal Epidemiology
Kyle P MessierElizabeth D Hilborn

Abstract

This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity) and aggregate classes (e.g., forest, developed). Percent of each landcover type, median income, and centroid coordinates were calculated by census tract. Regression results from individual and aggregate variable models were compared with the dispersion parameter-based R(2) (Rα(2)) and AIC. The maximum Rα(2) was 0.82 and 0.83 for the best aggregate and individual model, respectively. The AICs for the best models differed by less than 0.5%. The aggregate model variables included forest, developed, agriculture, agriculture-squared, y-coordinate, y-coordinate-squared, income and income-squared. The individual model variables included deciduous forest, deciduous forest-squared, developed low intensity, pasture, y-coordinate, y-coordinate-squared, income, and income-squared. Results indicate that regional landscape models for Lyme disease rate are robust to NLCD landcover classification resolution.

References

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Citations

Sep 18, 2016·International Journal of Epidemiology·Kyle P Messier, Marc L Serre
Apr 1, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Juan G SerraAggelos K Katsaggelos
May 10, 2020·Scientific Reports·Amalie Muus AndreasenSigurdur Skarphedinsson
Apr 8, 2021·Urban Forestry & Urban Greening·Yang Ye, Hongfei Qiu

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