Using imperfect data in predictive mapping of vectors: a regional example of Ixodes ricinus distribution

Ribeiro, R. , Eze, J. I., Gilbert, L. , Wint, G. R. W., Gunn, G., Macrae, A., Medlock, J. M. and Auty, H. (2019) Using imperfect data in predictive mapping of vectors: a regional example of Ixodes ricinus distribution. Parasites and Vectors, 12, 536. (doi: 10.1186/s13071-019-3784-1) (PMID:31727162) (PMCID:PMC6857280)

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Background: Knowledge of Ixodes ricinus tick distribution is critical for surveillance and risk management of transmissible tick-borne diseases such as Lyme borreliosis. However, as the ecology of I. ricinus is complex, and robust long-term geographically extensive distribution tick data are limited, mapping often relies on datasets collected for other purposes. We compared the modelled distributions derived from three datasets with information on I. ricinus distribution (quantitative I. ricinus count data from scientific surveys; I. ricinus presence-only data from public submissions; and a combined I. ricinus dataset from multiple sources) to assess which could be reliably used to inform Public Health strategy. The outputs also illustrate the strengths and limitations of these three types of data, which are commonly used in mapping tick distributions. Methods: Using the Integrated Nested Laplace algorithm we predicted I. ricinus abundance and presence–absence in Scotland and tested the robustness of the predictions, accounting for errors and uncertainty. Results: All models fitted the data well and the covariate predictors for I. ricinus distribution, i.e. deer presence, temperature, habitat, index of vegetation, were as expected. Differences in the spatial trend of I. ricinus distribution were evident between the three predictive maps. Uncertainties in the spatial models resulted from inherent characteristics of the datasets, particularly the number of data points, and coverage over the covariate range used in making the predictions. Conclusions: Quantitative I. ricinus data from scientific surveys are usually considered to be gold standard data and we recommend their use where high data coverage can be achieved. However in this study their value was limited by poor data coverage. Combined datasets with I. ricinus distribution data from multiple sources are valuable in addressing issues of low coverage and this dataset produced the most appropriate map for national scale decision-making in Scotland. When mapping vector distributions for public-health decision making, model uncertainties and limitations of extrapolation need to be considered; these are often not included in published vector distribution maps. Further development of tools to better assess uncertainties in the models and predictions are necessary to allow more informed interpretation of distribution maps.

Item Type:Articles
Additional Information:RR was supported by Scotland’s Rural College (SRUC) internal studentship. LG was supported by the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS) and as part of the Scottish Government’s Centre of Expertise on Animal Disease Outbreaks (EPIC).
Glasgow Author(s) Enlighten ID:Cardoso Ribeiro, Dr Rita Claudia and Gilbert, Dr Lucy and Auty, Harriet and Gunn, Prof George
Authors: Ribeiro, R., Eze, J. I., Gilbert, L., Wint, G. R. W., Gunn, G., Macrae, A., Medlock, J. M., and Auty, H.
College/School:College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
Journal Name:Parasites and Vectors
Publisher:BioMed Central
ISSN (Online):1756-3305
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Parasites and Vectors 12: 536
Publisher Policy:Reproduced under a Creative Commons License
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