Slides used in a session during the MOOC course on "Geohealth: Improving Public Health through Geographic Information" organized by the University of Twente. Link: https://www.futurelearn.com/courses/geohealth

1. KIT
Vector-borne diseases
and Lyme disease

2. Vector-borne diseases
A vector is “an insect or any living carrier that transports an
infectious agent from an infected individual or its wastes to a
susceptible individual or its food or most immediate surroundings”
A Dictionary for Epidemiology
Malaria | Dengue | Chikungunya | Yellow-fever
Japanese Encephalitis | Lymphatic filariasis | Leishmaniasis River
blindness | Congo haemorragic fever |Schistosomiasis | Chagas
Lyme disease | Tick-Borne Encephalitis
Source: A global brief on VBD, WHO (2014)

3. Distribution of Lyme disease
http://en.wikipedia.org/wiki/Lyme_disease
Lyme, CT
3

4. Ticks
4

5. Evolution of Lyme disease in the Netherlands
5

6. Evolution of Lyme disease in the Netherlands
6
 Highlights:
 Decrease in tick bite consultations
 Increase (gentler) in the cases of
Lyme disease
 Different causes:
 Increased awareness
 Increased carelessness
 No increase on infection rate
0
10
20
30
40
50
60
70
80
90
100
1994 2001 2005 2009 2014
#ofcases
Thousands
Tick bites and Lyme cases
1994 – 2014
Tick Bites Lyme Disease
Source: RIVM

7. Serious matter
Actions in the Netherlands

8. Serious matter
 Lyme disease has a high cost
 For citizens:
 Long-lasting sequels: muscles and joint pain, brain deterioration
 For public health agents:
 Treatment for a potentially chronical disease
 Population at risk: children and elder
 Public health entities and universities monitoring this phenomenon

9. Why is this happening?
Causes
 Global warming
 Changes in weather
dynamics
 Landscape fragmentation
 Alteration of wildlife
dynamics
Consequences
 Longer tick season
 Higher tick densities
 Geographic distribution
of ticks is pushing
northwards
 Development of new
habitats suitable for ticks
Challenges
 How to monitor ticks?
 What variables influence
the number of ticks?
 Can we predict the
densities of ticks for each
point in the NL?

10. KIT
Monitoring tick bites
and Lyme disease

11. Tekenradar
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12. Tekenradar
 Collaborative platform created in
2012 by RIVM and WUR
 Conceived for the crowdsourced
monitoring of tick bites
 More than 40.000 tick bites reports
collected in 4 years
 Each tick bite report:
 Location, date of the tick bite
 Type of vegetation around
 Type of activity carried out

13. Tekenradar
 What are the situations to report a tick bite?
Mary is 65 years old and likes going to the forest to
pick mushrooms. She has been doing this activity
for 40 years and she knows the forest. Thus, she
says she is not scared of ticks and goes through
high grasses and bushes. But one day she
discovers a tick attached to her skin.
John and his two kids went to play to the
park next to their house. The kids had a
lot of fun rolling in the grass and running
to a nearby forest patch with high vegetation.
A week later, John discovers that one of
them has a big red rash in his shoulder.
Can you think in other common situations to report a tick bite?

14. Tekenradar

15. Volunteered tick sampling
 Since 2006:
 Group of volunteers sample 17
locations in NL on a monthly
basis
 Count ticks in its different life
stages (i.e. larvae, nymph,
adults)
 First citizen science project of
its kind!
Source: WUR
15

16. Volunteered tick sampling
 Volunteered tick sampling:
 Three types of habitat:
 Deciduous forest (oak, beech)
 Coniferous forest (pine)
 Grasses/bushes
 Monthly tick counts:
 Period 2006 – 2014
 Currently, around 3000
samples
Source: WUR
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17. Volunteered tick sampling
Now we know the evolution of tick counts in the time series
and we can link it to environmental variables
to train models that predict tick densities…
…and understand main drivers of the phenomenon
17

18. Motivation
 What can we do with these volunteer data collections?
 Answer scientific questions!
1. Identify the enviromental conditions in which tick bites are produced
2. Predict the abundance of ticks in forests
 Next section

19. KIT
Methods

20. Methodology
 Basic scientific method:
1. Identify important factors on the
phenomenon under study:
2. Apply algorithms:
1. Frequent pattern mining
2. Regression
3. Visualize and interpret information
 Two use cases:
1. UC1: Identifying factors associated
to Tekenradar tick bites
2. UC2: Predicting tick abundances in
nature

21. UC1: Important factors on tick bites
• Warm days are suitable to go to the forest
• High temperatures means less risk for tick bitesTemperature
• Rainy days are not suitable to go the forest
• Precipitation prevents tick desiccationPrecipitation
• People tend to go to green spaces for leisure activities
• Dense forest canopy prevents tick dessicationVegetation
• Provides a measurement of where risky areas could beDistances
• Helps determining suitable tick habitatsSoil
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22. UC1: Frequent pattern Mining
 Efficient form of extracting information from crowdsourced data
 Conceptually similar to Amazon or Netflix recommendations:
 Watch “Game of Thrones” + “Vikings”  suggest “Lord of the Rings”
 Buy “PS4” + “Uncharted”  suggest “FIFA 2016”
 Automatic exploration of data to find hidden patterns:
 Suitable for big datasets, where visual exploration is not possible
 Count the number of co-occurrences of the variables
 Check references for the complete experiment description

23. UC1: Visualization and interpretation

24. UC2: Important factors on tick ecology
• 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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25. UC2: Regression
 Conceptually similar to linear regression in mathematics
 Multiple variables involved  Multivariate regression
 Non-linear phenomenon  Non-linear algorithms
 Steps:
1. Volunteer flagging dataset is enriched with environmental data
2. Model is trained with the enriched dataset
3. Model learns the main traits of the non-linear phenomenon of tick densities
4. Model can predict unseen places  create tick abundance maps for the Netherlands

26. UC2: Visualization and interpretation

27. Summary
 Identified the main factors associated to tick bites
 Predicted the tick abundance in the Netherlands
 Volunteer data can be used to feed a scientific workflow