Slides used during the guest lecture in the KIT & ITC course on "Using Geographic Information Systems in disease control programs". Link: https://www.kit.nl/health/training/using-geographic-information-systems-disease-control-programs-gis/

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
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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?