Assessing tick attachments to humans with citizen science data: spatio-temporal mapping in Switzerland from 2015 to 2021 using spatialMaxent
Abstract Background Ticks are the primary vectors of numerous zoonotic pathogens, transmitting more pathogens than any other blood-feeding arthropod. In the northern hemisphere, tick-borne disease cases in humans, such as Lyme borreliosis and tick-borne encephalitis, have risen in recent years, and...
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BMC
2025-01-01
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| Series: | Parasites & Vectors |
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| Online Access: | https://doi.org/10.1186/s13071-024-06636-4 |
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| author | Lisa Bald Nils Ratnaweera Tomislav Hengl Patrick Laube Jürg Grunder Werner Tischhauser Netra Bhandari Dirk Zeuss |
| author_facet | Lisa Bald Nils Ratnaweera Tomislav Hengl Patrick Laube Jürg Grunder Werner Tischhauser Netra Bhandari Dirk Zeuss |
| author_sort | Lisa Bald |
| collection | DOAJ |
| description | Abstract Background Ticks are the primary vectors of numerous zoonotic pathogens, transmitting more pathogens than any other blood-feeding arthropod. In the northern hemisphere, tick-borne disease cases in humans, such as Lyme borreliosis and tick-borne encephalitis, have risen in recent years, and are a significant burden on public healthcare systems. The spread of these diseases is further reinforced by climate change, which leads to expanding tick habitats. Switzerland is among the countries in which tick-borne diseases are a major public health concern, with increasing incidence rates reported in recent years. Methods In response to these challenges, the “Tick Prevention” app was developed by the Zurich University of Applied Sciences and operated by A&K Strategy Ltd. in Switzerland. The app allows for the collection of large amounts of data on tick attachment to humans through a citizen science approach. In this study, citizen science data were utilized to map tick attachment to humans in Switzerland at a 100 m spatial resolution, on a monthly basis, for the years 2015 to 2021. The maps were created using a state-of-the-art modeling approach with the software extension spatialMaxent, which accounts for spatial autocorrelation when creating Maxent models. Results Our results consist of 84 maps displaying the risk of tick attachments to humans in Switzerland, with the model showing good overall performance, with median $$\hbox {AUC}_{\textrm{ROC}}$$ AUC ROC values ranging from 0.82 in 2018 to 0.92 in 2017 and 2021 and convincing spatial distribution, verified by tick experts for Switzerland. Our study reveals that tick attachment to humans is particularly high at the edges of settlement areas, especially in sparsely built-up suburban regions with green spaces, while it is lower in densely urbanized areas. Additionally, forested areas near cities also show increased risk levels. Conclusions This mapping aims to guide public health interventions to reduce human exposure to ticks and to inform the resource planning of healthcare facilities. Our findings suggest that citizen science data can be valuable for modeling and mapping tick attachment risk, indicating the potential of citizen science data for use in epidemiological surveillance and public healthcare planning. Graphical Abstract |
| format | Article |
| id | doaj-art-bfd40f53b0e14a43a52b1b484962965b |
| institution | Kabale University |
| issn | 1756-3305 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Parasites & Vectors |
| spelling | doaj-art-bfd40f53b0e14a43a52b1b484962965b2025-01-26T12:17:43ZengBMCParasites & Vectors1756-33052025-01-0118111710.1186/s13071-024-06636-4Assessing tick attachments to humans with citizen science data: spatio-temporal mapping in Switzerland from 2015 to 2021 using spatialMaxentLisa Bald0Nils Ratnaweera1Tomislav Hengl2Patrick Laube3Jürg Grunder4Werner Tischhauser5Netra Bhandari6Dirk Zeuss7Faculty of Geography, Environmental Informatics, University of MarburgInstitute of Natural Resource Sciences, Zurich University of Applied Sciences ZHAWOpenGeoHub FoundationInstitute of Natural Resource Sciences, Zurich University of Applied Sciences ZHAWA&K Strategy Ltd., Smartphone application “Tick Prevention”A&K Strategy Ltd., Smartphone application “Tick Prevention”Faculty of Geography, Environmental Informatics, University of MarburgFaculty of Geography, Environmental Informatics, University of MarburgAbstract Background Ticks are the primary vectors of numerous zoonotic pathogens, transmitting more pathogens than any other blood-feeding arthropod. In the northern hemisphere, tick-borne disease cases in humans, such as Lyme borreliosis and tick-borne encephalitis, have risen in recent years, and are a significant burden on public healthcare systems. The spread of these diseases is further reinforced by climate change, which leads to expanding tick habitats. Switzerland is among the countries in which tick-borne diseases are a major public health concern, with increasing incidence rates reported in recent years. Methods In response to these challenges, the “Tick Prevention” app was developed by the Zurich University of Applied Sciences and operated by A&K Strategy Ltd. in Switzerland. The app allows for the collection of large amounts of data on tick attachment to humans through a citizen science approach. In this study, citizen science data were utilized to map tick attachment to humans in Switzerland at a 100 m spatial resolution, on a monthly basis, for the years 2015 to 2021. The maps were created using a state-of-the-art modeling approach with the software extension spatialMaxent, which accounts for spatial autocorrelation when creating Maxent models. Results Our results consist of 84 maps displaying the risk of tick attachments to humans in Switzerland, with the model showing good overall performance, with median $$\hbox {AUC}_{\textrm{ROC}}$$ AUC ROC values ranging from 0.82 in 2018 to 0.92 in 2017 and 2021 and convincing spatial distribution, verified by tick experts for Switzerland. Our study reveals that tick attachment to humans is particularly high at the edges of settlement areas, especially in sparsely built-up suburban regions with green spaces, while it is lower in densely urbanized areas. Additionally, forested areas near cities also show increased risk levels. Conclusions This mapping aims to guide public health interventions to reduce human exposure to ticks and to inform the resource planning of healthcare facilities. Our findings suggest that citizen science data can be valuable for modeling and mapping tick attachment risk, indicating the potential of citizen science data for use in epidemiological surveillance and public healthcare planning. Graphical Abstracthttps://doi.org/10.1186/s13071-024-06636-4Citizen scienceLyme diseaseSpatio-temporal mappingspatialMaxentSpecies distribution modelingSwitzerland |
| spellingShingle | Lisa Bald Nils Ratnaweera Tomislav Hengl Patrick Laube Jürg Grunder Werner Tischhauser Netra Bhandari Dirk Zeuss Assessing tick attachments to humans with citizen science data: spatio-temporal mapping in Switzerland from 2015 to 2021 using spatialMaxent Parasites & Vectors Citizen science Lyme disease Spatio-temporal mapping spatialMaxent Species distribution modeling Switzerland |
| title | Assessing tick attachments to humans with citizen science data: spatio-temporal mapping in Switzerland from 2015 to 2021 using spatialMaxent |
| title_full | Assessing tick attachments to humans with citizen science data: spatio-temporal mapping in Switzerland from 2015 to 2021 using spatialMaxent |
| title_fullStr | Assessing tick attachments to humans with citizen science data: spatio-temporal mapping in Switzerland from 2015 to 2021 using spatialMaxent |
| title_full_unstemmed | Assessing tick attachments to humans with citizen science data: spatio-temporal mapping in Switzerland from 2015 to 2021 using spatialMaxent |
| title_short | Assessing tick attachments to humans with citizen science data: spatio-temporal mapping in Switzerland from 2015 to 2021 using spatialMaxent |
| title_sort | assessing tick attachments to humans with citizen science data spatio temporal mapping in switzerland from 2015 to 2021 using spatialmaxent |
| topic | Citizen science Lyme disease Spatio-temporal mapping spatialMaxent Species distribution modeling Switzerland |
| url | https://doi.org/10.1186/s13071-024-06636-4 |
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