The document discusses a study on modeling tick bite dynamics using volunteer data, focusing on factors influencing tick bites and tick abundance. It highlights the analysis of various data sources, including volunteer observations, remote sensing, and weather data, to understand human exposure to tick bites and establish patterns. The study aims to inform direct prevention campaigns and identify conditions under which humans are more frequently exposed to tick bites.

1. Modelling tick bites dynamics
using volunteer data
Irene Garcia-Martí
PhD Candidate
Dept. of Geo-Information Processing (GIP)
ITC – University of Twente
25th May 2015

2. Ticks
2

3. Distribution of Lyme disease
http://en.wikipedia.org/wiki/Lyme_disease
Lyme, CT
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4. Source:DutchNationalAtlas
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5. Tick bites and Lyme cases 1994 – 2009
Source: Dutch National Atlas & press releases
(2014 estimated)
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6. Volunteer data collection
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7. Important factors on tick densities
• Start questing season
• Survival through winterTemperature
• Increases tick survival
• Prevent tick dessicationPrecipitation
• Keeps soil moisture high
• Prevent tick dessicationVegetation
• Sustains tick populationWildlife
7

8. Important factors on tick bites
• Recreational pressure in nature
• Low perception of tick bite risk
Humans
• High temperatures, more
people outTemperature
• Rainy days, less people outsidePrecipitation
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9. Problem Conceptualization
TB = f(TA, HA, ENV, CLI)
TB = Tick Bites
TA = Tick Abundance
HA = Human Activity (Volunteer data, soil type, land use)
ENV = Environmental factors (Vegetation indices)
CLI = Weather factors (temperature and precipitation)
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10. Data
 Four types of data:
 Volunteer data on tick bites
 Remote sensing data
 Weather data
 Official data on land use and soil type
 Influence tick ecology
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11. Data
 Volunteer tick bite collection
(2006 – 2014)
 Quality check:
 Remove observations without
coordinate
 Remove observations outside
Netherlands
 Total amount:
 28.865 observations
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12. Data
 Remote-sensed data
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13. Data
 Weather data:
 Daily temperature and precipitation raster files
 Provided by KNMI
 Official data:
 Land use
 Soil type
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14. Data preparation
 Objective:
 Characterize tick bites observations in function of human,
environmental and climatic indices
 Big try-and-error factor
 Procedure:
 Create multidimensional table in function of data available
 Find features that are related with tick ecology
 Set a temporal scale to aggregate data
 Tools: Python and Javascript to process data
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15. Data preparation
Observations Vegetation
Indices
Temperature
Indices
Precipitation
Indices
Human
indices
1
2
3
…
28.865
Multidimensional Table (MDT)
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16. Data preparation
 Why are we doing this?
 Find conditions where humans are more exposed to tick bites that are
frequent in data
 Find clusters of these conditions and study spatial patterns
 Direct prevention campaigns
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17. Analysis overview
 Analysis:
 MDT: Input for modeling with data mining algorithms
 Three experiments:
 1st: Clustering and classification techniques
 2nd: Frequent pattern mining (today!)
 3rd: Decision Trees (on-going)
 Visualization with maps and ringmaps
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18. What is frequent pattern mining?
 FPM is:
 Technique to find statistically relevant patterns in data
 Try to find the longest combination of elements with a frequency above
a threshold
 Multiple algorithms:
 Apriori
 Eclat
 FP-Growth
 Toy example with supermarket list
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19. How does Apriori work?
19
ID Items ID Items
1 Apple, Chicken, Donut 11 Donut
2 Apple, Chicken 12 Chicken, Donut
3 Chicken, Bread, Apple 13 Apple
4 Donut, Bread 14 Apple, Donut
5 Donut, Chicken, Bread 15 Apple, Chicken
6 Apple, Donut 16 Bread
7 Bread, Chicken 17 Bread, Chicken
8 Apple, Donut 18 Donut
9 Break, Chicken 19 Apple, Chicken
10 Chicken, Donut 20 Bread, Chicken, Donut

20. How does Apriori work?
 Key idea: A pattern is frequent if
its subsets are also frequent
 User sets a threshold to consider
a pattern frequent
 Pattern generation is bottom-up
 Computationally expensive:
 One level per different item
 Multiple passes on data
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21. How does Apriori work?
21
ID Items ID Items
1 Apple, Chicken, Donut 11 Chicken
2 Apple, Bread 12 Chicken, Donut
3 Apple, Bread, Chicken 13 Apple
4 Bread, Donut 14 Apple, Donut
5 Bread, Chicken, Donut 15 Apple, Chicken
6 Apple, Donut 16 Bread
7 Apple, Chicken 17 Bread, Chicken
8 Apple, Donut 18 Chicken
9 Bread, Chicken 19 Apple, Donut
10 Chicken, Donut 20 Bread, Chicken, Donut

22. Back to the analysis
 No more supermarket items
 Reality:
 Apriori:
 Will scan combinations of 39 different items
 Will receive 28.865 rows as an input
 Data on temperature, precipitation, vegetation and human indices
 Thousands of patterns may be generated
 Depending on the threshold
 Challenge for visualization
 Patterns combined using ringmaps
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23. Analysis at a seasonal scale
Classified MDT
(Jenks Natural
Breaks)
Apply Apriori
Explore frequent
patterns
MDT
+ 39 columns
+ 28.865 obs.
+ 2006 - 2014
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24. Analysis at seasonal scale
Patterns
length 3
Patterns
length 4
• Combining 1500 patterns
• Length 3 and 4
• Frequency +20% 24

25. Analysis at a seasonal scale
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26. 26

27. Conclusions
 Identified combinations of indices where tick bites occurred:
 Adding human-related variables seem to produce meaningful results
 Still no spatial pattern:
 Humans are a biasing factor.
 It suggests there are ticks and humans everywhere
 Further work:
 Converge to a suitable temporal scale for tick dynamics and humans
 Spatial aggregation of observations (forest, neighborhood, grid cell)
 More human-related indices, less nature-related indices
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28. Discussion
 Do any of you knew Lyme disease before?
 Do any of you have experience in modeling species?
 What else would you include in the analysis?
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29. Thanks
Questions?