Modelling the current and future temperature suitability of the UK for the vector Hyalomma marginatum (Acari: Ixodidae)

Machine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe

BACKGROUND

Tick-borne pathogens pose a major threat to human health worldwide. Understanding the epidemiology of tick-borne diseases to reduce their impact on human health requires models covering large geographic areas and considering both the abiotic traits that affect tick presence, as well as the vertebrates used as hosts, vegetation, and land use. Herein, we integrated the public information available for Europe regarding the variables that may affect habitat suitability for ticks and hosts and tested five machine learning algorithms (MLA) for predicting the distribution of four prominent tick species across Europe.

MATERIALS AND METHODS

A grid of cells 20 km in diameter was prepared to cover the entire territory, containing data on vegetation, points of water, habitat fragmentation, forest density, grass extension, or imperviousness, with information on temperature and water deficit. The distribution of the hosts (162 species) was modelled and included in the dataset. We used five MLA, namely, Random Forest, Neural Networks, Naive Bayes, Gradient Boosting, and AdaBoost, trained with reliable coordinates for Ixodes ricinus, Dermacentor reticulatus, Dermacentor marginatus, and Hyalomma marginatum in Europe.

RESULTS

Both Random Forest and Gradient Boosting best predicted ticks and host environmental niches. Our results demonstrate that MLA can identify trait-matching combinations of environmental niches. The inclusion of land cover and land use variables has a superior capacity for predicting areas suitable for ticks, compared to classic methods based on the use of climate data alone.

CONCLUSIONS

Flexible MLA-driven models may offer several advantages over traditional models. We anticipate that these results may be extrapolated to other regions and combinations of tick-vertebrates. These results highlight the potential of MLA for inference in ecology and provide a background for the evolution of a completely automatized tool to calculate the seasonality of ticks for early warning systems aimed at preventing tick-borne diseases.

Vectorial dynamics underpinning current and future tick-borne virus emergence in Europe

Tick-borne diseases pose a growing threat to human and animal health in Europe, with tick-borne encephalitis virus (TBEV) and Crimean-Congo haemorrhagic fever virus (CCHFV), vectored by Ixodes ricinus and Hyalomma marginatum, respectively, emerging as primary public health concerns. The ability of ticks to transmit pathogens to multiple hosts and maintain infections across life stages makes them highly efficient vectors. However, many aspects of tick ecology and vectorial capacity remain understudied. This review examines key factors contributing to the vectorial competence of European ticks and their associated viruses. We first explore the influence of climate change on vector and disease ecology, using TBEV and CCHFV as case studies. We then analyse the role of the tick antiviral response in shaping vector competence. By integrating these elements, this review aims to enhance our understanding of tick-borne viral diseases and support the development of public health strategies, particularly through the One Health framework, to mitigate their impact in Europe.

Hyalomma marginatum - an emerging tickborne disease vector for the UK?

The tick Hyalomma marginatum is expanding its geographical range, reinforcing the need for surveillance and control to prevent the emergence of tickborne equine disease. Daniel Armstrong, policy adviser for exotic animal diseases at Defra, explains more.

Molecular identification and morphological variations of Amblyomma lepidum imported to Egypt, with notes about its potential distribution under climate change

The tick Amblyomma lepidum is an ectoparasite of veterinary importance due to its role in transmitting livestock diseases in Africa, including heartwater. This study was conducted in 2023 to monitor Amblyomma spp. infestation in dromedary camels imported from Somalia, Ethiopia, and Sudan to Egypt. This study inspected 200 camels at the Giza governorate's camel market that had been imported from Somalia, 200 from Ethiopia, and 200 from Sudan for tick infestation. Specimens were identified using morphological characteristics and phylogenetic analyses of the 12S and 16S rRNA genes. Clusters were calculated using an unweighted pair-group method with arithmetic averages (UPGMA) dendrogram to group the specimens according to their morphometric characteristics. The morphometric analysis compared the body shape of ticks collected from different countries by analyzing dorsal features. Principal component analysis (PCA) and canonical variate analysis (CVA) were performed to obtain body shape variation among specimens from different countries. Results indicated that camels were infested by 57 males Amblyomma lepidum, and no female specimens were observed; among these specimens, one may have a morphological abnormality. The results suggest that A. lepidum specimens collected from camels imported to Egypt from African countries exhibit locally adapted morphology with variations among specimens, particularly variations in body size. This adaptation suggests minimal potential for genetic divergence. Ecological niche modeling was used to predict the areas in Africa with suitable climates for A. lepidum. The study confirmed that East African countries might have the most favorable climatic conditions for A. lepidum to thrive. Interestingly, the amount of rain during the wettest quarter (Bio16) had the strongest influence on the tick's potential distribution, with suitability decreasing sharply as rainfall increased. Future predictions indicate that the climatic habitat suitability for A. lepidum will decrease under changing climate conditions. However, historical, current, and future predictions indicate no suitable climatic habitats for A. lepidum in Egypt. These findings demand continuous surveillance of A. lepidum in camel populations and the development of targeted strategies to manage tick infestations and prevent the spread of heartwater disease.

Crimean-Congo haemorrhagic fever virus uses LDLR to bind and enter host cells

Climate change and population densities accelerated transmission of highly pathogenic viruses to humans, including the Crimean-Congo haemorrhagic fever virus (CCHFV). Here we report that the Low Density Lipoprotein Receptor (LDLR) is a critical receptor for CCHFV cell entry, playing a vital role in CCHFV infection in cell culture and blood vessel organoids. The interaction between CCHFV and LDLR is highly specific, with other members of the LDLR protein family failing to bind to or neutralize the virus. Biosensor experiments demonstrate that LDLR specifically binds the surface glycoproteins of CCHFV. Importantly, mice lacking LDLR exhibit a delay in CCHFV-induced disease. Furthermore, we identified the presence of Apolipoprotein E (ApoE) on CCHFV particles. Our findings highlight the essential role of LDLR in CCHFV infection, irrespective of ApoE presence, when the virus is produced in tick cells. This discovery holds profound implications for the development of future therapies against CCHFV.

Predicting climate-driven distribution shifts in Hyalomma marginatum (Ixodidae)

Hyalomma marginatum is an important tick species which is the main vector of Crimean–Congo haemorrhagic fever and spotted fever. The species is predominantly distributed in parts of southern Europe, North Africa and West Asia. However, due to ongoing climate change and increasing reports of H. marginatum in central and northern Europe, the expansion of this range poses a potential future risk. In this study, an ecological niche modelling approach to model the current and future climatic suitability of H. marginatum was followed. Using high-resolution climatic variables from the Chelsa dataset and an updated list of locations for H. marginatum, ecological niche models were constructed under current environmental conditions using MaxEnt for both current conditions and future projections under the ssp370 and ssp585 scenarios. Models show that the climatically suitable region for H. marginatum matches the current distributional area in the Mediterranean basin and West Asia. When applied to future projections, the models suggest a considerable expansion of H. marginatum's range in the north in Europe as a result of rising temperatures. However, a decline in central Anatolia is also predicted, potentially due to the exacerbation of drought conditions in that region.

Ecological Niche Modelling Approaches: Challenges and Applications in Vector-Borne Diseases

Vector-borne diseases (VBDs) pose a major threat to human and animal health, with more than 80% of the global population being at risk of acquiring at least one major VBD. Being profoundly affected by the ongoing climate change and anthropogenic disturbances, modelling approaches become an essential tool to assess and compare multiple scenarios (past, present and future), and further the geographic risk of transmission of VBDs. Ecological niche modelling (ENM) is rapidly becoming the gold-standard method for this task. The purpose of this overview is to provide an insight of the use of ENM to assess the geographic risk of transmission of VBDs. We have summarised some fundamental concepts and common approaches to ENM of VBDS, and then focused with a critical view on a number of crucial issues which are often disregarded when modelling the niches of VBDs. Furthermore, we have briefly presented what we consider the most relevant uses of ENM when dealing with VBDs. Niche modelling of VBDs is far from being simple, and there is still a long way to improve. Therefore, this overview is expected to be a useful benchmark for niche modelling of VBDs in future research.

Tick and Tick-Borne Diseases: New Problems Providing New Possible Solutions

Ticks and tick-borne diseases are responsible for enormous losses in animal and human life, which do not seem to become better as new data show surprising connections [...].