Vector-borne diseases and Lyme disease (2016)
1 like395 views
The document discusses vector-borne diseases, focusing on Lyme disease in the Netherlands and its evolution from 1994 to 2014. It highlights the impact of environmental changes on tick populations, the use of collaborative platforms for monitoring tick bites, and the role of citizen science in understanding tick ecology. Key findings indicate that increased awareness and environmental factors contribute to the prevalence of Lyme disease and tick densities.
Vector-borne diseases and Lyme disease (2016)
- 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 11
- 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 16
- 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 21
- 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 24
- 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