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 

Background: The tick Ixodes ricinus is the most common tick species in the UK and a significant vector of Borrelia burgdorferi s.l. (causative agent of Lyme borreliosis) and Tick-Borne Encephalitis virus (TBEv) to humans and Anaplasma phagocutphilum, Babesia divergens and louping ill virus to animals.Methods: The Tick Surveillance Scheme (TSS) administered by the UK Health Security Agency (UKHSA) contains validated reports of tick encounters from the last twenty years sent in by human and animal health providers, as well as members of the public. We modelled the probability of tick presence across England and Wales using data sourced from the TSS and a combination of biotic and abiotic factors. TSS presence records between 2013 and 2023 are combined with background points generated through a combination of random sampling and target group sampling. An ensemble of statistical and machine learning models were then trained to classify points as presence or background. Results: The ensemble model had an out-of-sample continuous Boyce index of 0.99 and area under the receiver-operator curve (ROC AUC) of 0.84 on 2024 testing data. The greatest contributors to ROC AUC were variables relating to roe deer (Capreolus capreolus) distribution and land cover type. Normalised Difference Vegetation Index and other climatic variables made little contribution to the model’s performance. Most of southern England, as well as other areas with known tick populations such as the New Forest and the Lake District, are assigned some of the highest predicted probabilities of tick presence. Interpretation: Unstructured 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.