Evidence map›Paper›PMID 41066479›Full record

ArticlePLoS neglected tropical diseases2025

Modelling the distribution of the tick Ixodes ricinus in England and Wales using passive surveillance data from citizen science reports.

Mark Gideon Burdon, Maximilian Ayling, Nyall Jamieson, Julie Day, Jolyon Medlock, Kayleigh Hansford, G R William Wint, Thomas Ward

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Babesiosis and Its Significance in Transfusion Medicine from a European Point of View.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Mark Gideon BurdonUK Health Security Agency, London, United Kingdom.ORCID 0009-0001-0354-4332
Maximilian AylingUK Health Security Agency, London, United Kingdom.
Nyall JamiesonDepartment of Mathematics, University of Manchester, Manchester, United Kingdom.
Julie DayUK Health Security Agency, London, United Kingdom.
Jolyon MedlockUK Health Security Agency, London, United Kingdom.
Kayleigh HansfordUK Health Security Agency, London, United Kingdom.
G R William WintDepartment of Biology, University of Oxford, Oxford, United Kingdom.
Thomas WardUK Health Security Agency, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTicks are a significant cause of illness globally. The tick Ixodes ricinus is commonly found across Europe and is a significant vector of Tick-Borne Encephalitis virus (TBEv), Borrelia burgdorferi s.l. (causative agent of Lyme borreliosis), Babesia divergens, Anaplasma phagocytophilum, and several Rickettsia bacteria, among others.

methodsThe Tick Surveillance Scheme (TSS) administered by the UK Health Security Agency (UKHSA) contains validated reports from the general public of tick encounters over the last twenty years. We modelled the probability of I. ricinus tick presence across England and Wales using the locations of TSS reports from 2013 to 2023 and a combination of biotic and abiotic factors. An ensemble of statistical and machine learning models was trained to classify points as presence (true tick report locations) or background (points generated randomly and by target-group sampling).

resultsThe ensemble model had a continuous Boyce index of 0.99 and area under the receiver-operator curve (ROC AUC) of 0.84 on out-of-sample 2024 data. Variables relating to roe deer (Capreolus capreolus) distribution and land cover type were most important. Most of southern England, as well as other areas with known tick populations such as the New Forest and the Lake District, are modelled as highly probable tick presence areas.

interpretationUnstructured citizen science data was suitable for creating a high-performing species distribution model for I. ricinus after addressing spatial and demographic biases. This model is now being used to inform local public health awareness showing the advantage of passive surveillance through to modelling and public health awareness.

Indexed as

Animal DistributionIxodesAnimalsCitizen ScienceDeerEnglandEpidemiological MonitoringHumansWales

Identifiers

PMID41066479
PMCPMC12533967

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.