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

4bf1842cee25dfa07e8afe80830387d6?s=47 Irene
September 27, 2019

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

Presentation slides used during my PhD Defence, held on 27th of September 2019 at the University of Twente (the Netherlands). You can find the PhD thesis here: https://library.itc.utwente.nl/papers_2019/phd/garciamarti.pdf and a summary of the research in plain terms here: https://irenegarciamarti.com/post/rs00-explaining-my-phd-research/. Feel free to contact me at research [at] irenegarciamarti [dot] com if you have any questions.

4bf1842cee25dfa07e8afe80830387d6?s=128

Irene

September 27, 2019
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  1. 1.

    1 Mapping tick dynamics and tick bite risk using data-driven

    approaches and volunteered observations Irene Garcia-Marti 27-09-2019
  2. 2.

    CONTEXT • Re-emergence of vector-borne diseases: • Global & socio-economic

    changes • VDB: 23 diseases (including Zika) • Changes + ticks • Longer tick season • Increase in tick populations • Northward + elevation expansion • New suitable habitats • Concurrent increase of tick-borne diseases Source: WHO
  3. 3.

    CONTEXT Source: RIVM Source: RIVM 25K – 27K LD cases

    per year ~2K develop persisting symptoms
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    CITIZEN SCIENCE TICK ACTIVITY • Monitoring tick activity • Project

    started and coordinated by WUR researchers • Monitoring 15-25 forested locations in the NL • Sampling: • Once a month, 2006-2016 • Using dragging blanket • Counting ticks every 25m • ~4,000 samples • One of the longest time-series on tick activity: unique!
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  6. 7.

    R = H x E RESEARCH OBJECTIVES Modelling tick bite

    risk and tick activity using citizen science data
  7. 8.

    DATA AND METHODS • General ML operations: • Classification •

    Regression • Pattern mining • ML methods: • Random Forest • Apriori • + • Statistics: • Poisson, neg. bin. • Zero-inflation, skewness
  8. 9.

    TICK ACTIVITY (H) Daily tick activity 15/4/2014 to 15/7/2014 Time-aware

    random forest 100+ covariates in 11 time windows
  9. 10.

    EXPLORING TICK BITE RISK What is the tick bites collection

    monitoring? Is there any information? EDA with Apriori to understand patterns of tick bites Tick bites are a realization of R!
  10. 11.

    HUMAN EXPOSURE (E) Pre-step before developing tick bite risk model

    Bridges the need of ICT data E clustered at the edges of forests and natural areas Locating E means we can characterize it R = H x E E = R ÷ H
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    12 TICK BITE RISK (R) Modification of random forest to

    handle skewed and zero-inflated distributions Risk is higher along the coast Combination of H and 19 new covariates of E
  12. 13.

    CONTRIBUTIONS • Illustrating intrinsic relationship between R, H, E •

    Atmospheric water levels drive tick dynamics • Tick bite risk is strongly 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 simple method to estimate human exposure to ticks without ICT • Guidelines to explore patterns of R 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
  13. 14.

    FUTURE WORK Improving prediction of tick dynamics Creating dynamic tick

    bite risk model Linking machine learning and statistics Creating apps for professionals and citizens
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