1. Lyme Disease: Analyzing Temporal, Land Use, Acorn
Distribution, Population, and Climate as Factors
Contributing to Cases in the Northeast United
States
from 2007 to 2011
Created By: Matt Weik
GEOG 522: Final Project
Date: December 11, 2014
2. Introduction
• Background
– Lyme disease is a vector, arthropod, borne disease
• Requires ticks in order to complete infection cycle
– Borrelia burgdorferi:
• Spirochete (spiral shaped) bacteria
• Double membrane.
– Ixodes spacularis:
• Commonly known as the “deer tick”
• Feed off of blood: can survive 2 years without a blood meal.
• Three life stages (larvae, nymph, and adult):takes 3 years to complete cycle
• Different host at each stage
– Hosts:
• Immature ticks (larvae and Nymph): mice, chipmunks, birds and other small animals
• Mature ticks (adult): Deer, dogs, birds, larger mammals and warm blooded animals.
– Symptoms
• Chills, fever, headache, pain in large joints, swollen lymph nodes, and a bulls-eye rash (Erythema
Migrans)
– Treatment
• By mouth antibiotics
• No treatment within 6 months= chance of Chronic Lyme
3. Introduction
• Previous Research
– Climate factors
• Growing degree days for nymph ticks growing season , March 1st- September 30th
– Ticks and hosts factors
• White tail deer are being represented by using the area of forest in each county
– If area has enough woodland to support deer population
• Acorn abundance was represented by using the distribution of Oak Tree species
– If area can supply a population of small rodents: common host for immature ticks
– Human factors
• Lyme disease reservoir presence: previous cases show the strength of presence of Lyme-
disease reservoir
• Urban areas: the more urban space has shown it reduces interaction between people
and ticks
– However it has been theorized that more urban area reduced host populations; forcing ticks to
seek humans.
4. Methodology and Data
• Process
– Gather GIS data into county variables
– Run Principle Component Analysis
– Run regression on PCA components
– Run spatial regression and look for improvement
– Define components that are make up spatial regression
• Data Gathering and Calculating
– Lyme-disease: cases were organized by column: needed FIPS column for unique join
– Oak tree species variation: If the distribution of a species of the Oak tree genus then a value of one was added to the variable;
for example three different types of oaks had a value of 3.
– Land use data and GDD: data were both raster; required extensive use of GIS to manipulate to county level variable.
• GDD had to be averaged for each county based on points representing values.
• Variables:
– Dependent Variable
• P_LD_07_11: The percent of population of Lyme-disease cases between 2007 and 2011
– Independent Variables:
• TGDD_2012: Growing degree days before 2012, or 50 degrees Fahrenheit from March 1:- Sept
• P_LD_97-01: The number of cases per county, normalized by population for years including the previous census data, represents the
temporal factors of the Lyme-disease cycle.
• In addition it can represent the size and prevalence of a disease reservoir in each county.
• Oak_Var: the number of different oak tree species located within the county.
• used to measure acorn distribution, and therefore the region’s carrying capacity of preferred immature tick hosts.
• pforest: The percent of each county that is forest.
• Represents the distribution of white-tailed deer in the northeast area.
• purban: The percent of county that is urban, or developed
» Area void of suitable hosts.
5. Results
• Principle Component Analysis (PCA)
– Component
• 1: The percent of urban land
• 2:The percent of woodland with small negative effect of GDD= Northern forested land
• 3:The percent of grassland
• 4:The percent of population with Lyme disease between 1997-2001; the status of
disease-reservoir 10 years ago
• 5: Mix- GGD (.823) and Oak species variation (.491)
– Long or very warm growing season, southern located, places with higher diversity of Oak tree
species
• 6:Mix – Oak species variation (.773) and GGD (.456)
– Places with a higher diversity of oak species, and a longer or warmer growing season
Rotated Component Matrix
a
Component
1 2 3 4 5 6
Oak_Var .143 -.275 .115 .072 .823 .456
TGDD_2012 .228 -.318 .092 -.001 .491 .773
Pforest -.230 .895 -.112 -.014 -.248 -.269
Purban .959 -.195 -.004 -.027 .124 .164
P_LD_97_01 -.024 -.011 .176 .983 .042 .009
Pgrass -.002 -.090 .973 .184 .082 .071
Extraction Method: Principal Component Analysis.
Rotation Method: Equamax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
6. Results
• R squared: 0.52
• Components 1- 4 significant
at 0.01 level
• Component 5 is significant at
0.05 level
• Component 6 is at the 0.1
• Moran’s I: 8.97; p-value: 0.00
– There is spatial autocorrelation
8. Results
• Spatial Regression
– R squared: 0.69
– Component 2,3, and 4 are significant
– Components 1, 5, and 6 not significant
9. Discussion
• Coefficient Analysis
– From the analysis of both the components created using PCA and then tested using a spatial regression:
• Variables Purban, GGD_2012, and Oak_Var are accounted for in principle components that were not significantly different from 0 ,
– therefore they virtually do not contribute to the variation in the percent of people who became infected with Lyme-disease between 2007 and 2011.
• Variables Pforest, Pgrass, and P_LD_97_01 are accounted for in significant principle components
– Coefficients/Variable not significant
• 1: The percent of urban land
• 5: Mix- GGD (.823) and Oak species variation (.491)
• 6:Mix – Oak species variation (.773) and GGD (.456)
– From PCA and spatial regression:
• Component 2:The percent of woodland with small negative effect of GDD= Northern forested land
• Component 3:The percent of grassland
• Component 4:The percent of population with Lyme disease between 1997-2001; the status of disease-reservoir 10 years ago
• Limitations and Error
– Many more climate and environmental factors exist, but there is limited data: regression does not explain that much variation
(R squared = 0.69)
– Many issues with data set, even after PCA and spatial regression: Failed tests of both normaility and heteroscedasity
– Many of the variables used were based of prior research, but did not have same units or measurement
• Oak tree variation does not necessarily equal abundance of acorns
10. Conclusion
• While Lyme-disease is rare, and poorly understood, it can
cause debilitating and potentially chronic symptoms if
untreated.
• Complicated cycle of Lyme disease infection, and
differences in host preference for each stages of Tick
lifecycle cause a large number of possible environmental
factors.
• From this study, it seems out of the variables examined that
the percent area in a county that is vegetation (both forest
or grassland), and areas that have a strong disease
reservoir (high number of cases between 1997-2000);
however the percent of urban area in a county, the climate
(growing season or GDD), and variation in Oak species do
not effect this relationship.
