Mapping tick dynamics and tick bite risk using data-driven approaches and volunteered observations

Slides used during my presentation at the University of Idaho's TickBase Project 1st Annual Meeting, held 23-24 March 2021. This presentation contains a quick tour through my PhD research, focusing more on the scientific communication and the storytelling rather than in the technical details.

Programme here: https://tickbase.net/wp-content/uploads/2021/03/TickBase-Annual-Meeting_Schedule-Public.pdf

Transcript

1. Mapping tick dynamics and tick bite risk
   using data-driven approaches
   and volunteered observations
   Dr. Irene Garcia-Marti
   Prof. Dr. Raul Zurita-Milla
   In collaboration with:
   Dr. A Swart
   MG Harms, MSc
   Dr. AJH van Vliet
   Dr. CC van den Wijngaard
   

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2. Scope
   

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3. the Netherlands 101
   • BBOX: 350 x 300 km, 45km2 of land
   • Elevation: -7m – 322m
   • Population: ~ 17M
   • ~85% urbanization, little amount of forests
   • Population is mainly concentrated in Randstadt (8M)
   • Enthusiasm about outdoor leisure and nature
   • People are more exposed to ticks
   

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4. An increasing problem
   • Current situation:
   • 1 million tick bites per year
   • 2% contract disease
   • 27,000 cases per year
   • Cost of treatment:
   • 5700 euros per patient (~6800 USD)
   • Yearly lump sum: ~20M euros (~24M USD)
   • Disease burden: 10.55 DALYs
   • Source:
   • The cost of Lyme borreliosis
   (van den Wijngaard et al., 2017)
   • Socio-economic changes:
   • Urban demographics
   • Increased healthier lifestyles
   Source: https://www.rivm.nl/ziekte-van-lyme
   Source: Dutch Institute forPublicHealth
   and the Environment(RIVM)
   

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5. Conceptualizing
   the problem
   Risk
   Probability that an individual will become ill or die within a stated
   period of time [. . . ]
   Hazard
   Inherent capability of an agent [. . . ] to have an adversely
   health effect
   Exposure
   Proximity and/or contact with a source of a disease agent in such a
   manner that effective transmission of the agent or harmful effects of
   the agent may occur
   Vulnerability
   The conditions determined by physical, social, economic and
   environmental factors [...] increasing the susceptibility to
   the impacts of hazards
   Sources:
   A dictionary of Epidemiology (Last, 2001)
   Ecology and prevention of Lyme borreliosis (Braks et al., 2016)
   UN Disaster Risk Reduction
   An approach from Risk Assessment:
   R = H × E × V
   

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6. Grounding the
   problem
   Risk
   risk of tick bite
   Hazard
   tick dynamics
   Exposure
   human exposure
   Vulnerability
   unknown, thus constant
   An approach from Risk Assessment:
   R = H × E × V
   

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7. Data
   collections
   

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8. Citizen monitoring
   networks
   • Tick bites risk and tick dynamics
   are very local phenomena:
   • Unseen by: sensor networks,
   satellites, simulations
   • Certainly you can proxy!
   • Importance of grass-root
   campaigns: citizen science
   • Enabling citizens with digital devices
   and technologies to report conditions
   on Earth surface (Goodchild, 2007)
   2006 - 2012
   2012 - present
   

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9. Citizens collecting data about ticks
   Source: RIVM
   Roughly 27,000 cases of Lyme borreliosis per year in the Netherlands
   

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10. Tick dynamics
    • 15 volunteers, 9 years = ~3,000 samples
    • Counted: larvae, nymphs, adults
    • Sampling strategy: blanket dragging
    • Sites in different landscapes (forest, dunes)
    • Two transects/site, manually inspected
    • Gaps based in availability/meteorology
    • Read more on (Gassner, 2011)
    

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11. Tick dynamics
    

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12. Tick dynamics
    

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13. Tick bites
    • 11 years = ~ 47,000 samples
    • Content: tick bite report, containing
    location
    • Combination of two citizen science
    networks:
    • Natuurkalender (2006 – 2012)
    • Tekenradar (2012 – present)
    

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14. Tick bites
    

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15. Tick bites
    Why a machine learning algorithm
    should work, out-of-the-box, in such
    'difficult' conditions?
    

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16. Modelling
    

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17. What are we
    modelling?
    Risk
    risk of tick bite
    Hazard
    tick dynamics
    Exposure
    human exposure
    Vulnerability
    unknown, thus constant
    An approach from Risk Assessment:
    R = H × E × V
    

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18. Modelling tick data
    Obtaining indicators of tick dynamics and tick bite riskto assist
    at creating tick mitigation and awareness campaigns
    

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19. Tick dynamics
    

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20. Tick
    dynamics
    • Unclear from literature:
    • Broad view on the most important variables driving tick dynamics
    • Most suitable temporal units to model tick dynamics
    • Our approach:
    • Building an array of explanatory variables
    • Tick habitat: composition, mast years
    • Weather: T, P, EV, RH, SD, VP (at 11 time-scales)
    • Vegetation: NDVI, EVI, NDWI
    • Land use: land cover type
    • Applying a data-driven method: Random Forest
    • With a tweak: time-awareness
    • Tick counts transformed into monthly Z-scores
    • Constrained and normalize range of tick counts
    Can we build
    a data-driven
    model predicting
    daily tick activity
    over the country?
    

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21. Tick
    dynamics
    Model selection:
    

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23. R2 ≥ 0.7
    R2 < 0.7
    

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24. Tick
    dynamics
    

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25. Human
    exposure
    tick dynamics ≠ tick bite risk
    TICK BITES PER 1KM GRID CELL (2006-2016)
    R = H × E E = R ÷ H
    

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26. Human
    exposure
    Recreational patterns along forest edges
    Not all forests are heavilyvisited
    

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27. Tick bite
    risk
    • From the reported tick bites, we had:
    • R = number of tick bites per pixel
    (descriptive model)
    • Could we make a ... ?
    • R = F(covariates)
    (predictive model)
    • Covariates: H × E
    • What are measures of human exposure?
    • Based on (Zeimes, 2014):
    • Framework based in measures of attractiveness
    and accessibility as proxies of human exposure
    • Including forests, wildlife, and waterbody
    characteristics as proxies of tick hazard
    R = H × E
    

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28. Tick bite
    risk
    1 – 19 : human exposure
    20 – 21: tick hazard
    

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29. Tick bite
    risk
    • Modelling challenges:
    • Canonical ML algorithms might not
    handle well disproportioned data
    (Krawczyk, 2016)
    • Implies that a data-driven method
    produces results biased towards
    the majority class
    • How to learn from a dataset with
    heavy disproportions?
    • Zero-inflation
    • Overdispersion
    

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30. Tick bite
    risk
    A Random Forest capable of learning from zero-inflated
    and overdispersed data
    

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31. Tick bite risk
    

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32. Tick bite risk
    

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33. Wrapping up
    

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34. Tick Hazard Human exposure Tick bite risk
    

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35. Publications
    

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36. Conclusions
    •Illustrating relationship between R, H, E
    •Atmospheric water levels drive tick dynamics
    •Tick bite risk is influenced by human exposure
    •Principles can be applicable to other tick-borne diseases
    Scientific
    •Modify method to handle time-aware observations
    •Modify method to handle skewed and zero-inflated distributions
    •Devised method to estimate human exposure to ticks without ICT data
    •Guidelines to explore patterns of risk
    Methodological
    •Monitoring elusive phenomena at unprecedented levels of detail
    •Observations can have sufficient quality to support scientific research
    •Sharing creative approaches at validating citizen science observations
    Citizen Science
    

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37. Additional ideas
    ACQUIRING ICT DATA FOR
    BETTER HUMAN EXPOSURE
    METRICS
    ADDING DYNAMIC TIME-
    SERIES OF WILDLIFE
    SUSTAINED ENGAGEMENT
    WITH THE COMMUNITY
    INVESTIGATING OTHER
    WATER-BASED FEATURES
    

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38. Thanks!
    Questions?
    email: garciamarti@knmi.nl
    web: irenegarciamarti.com
    PhD data deposit:
    https://doi.org/10.17026/dans-zre-tggd
    

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