Biology subjects

Budgey, R.

Publications and source records attributed to Budgey, R..

3 recordsLinked to original sources

bioRxiv2025

Predicting West Nile Virus risk across Europe for the current and future conditions

Vector-borne diseases have significant impacts on animal and human health globally, and these impacts are likely to increase in the future due to environmental and climate change. Understanding where to target surveillance and control measures to mitigate the impacts of emerging vector-borne diseases can be challenging when pathogens or disease is absent. In this study, we utilise a species distribution modelling approach previously applied to the UK to predict areas at higher risk of mosquito-borne disease across Europe, using West Nile Virus (WNV) as a case study. WNV is an Orthoflavivirus that is naturally transmitted between Culex mosquitoes and a range of avian species. However, it can spread to hosts such as humans and horses where it has the potential to lead to severe illness and mortality. Suitability predictions for Culex (Cx. pipiens and Cx. modestus) and avian hosts (mainly Passerine species) are made across Europe to determine potential risk of WNV circulation and establishment. These maps are then combined with information on human and horse density to determine risk to human and equine health. The resulting risk maps reveal that across Europe, there are areas of higher and lower risk that are predominantly driven by vector suitability as avian hosts are widespread. These predictions are projected into the future in 2100 using best- and worst-case Shared Socioeconomic Pathways (SSP1 and SSP5 respectively) to determine how risk may change over time, revealing that some areas see an increase in suitability for both vectors and hosts leading to higher risk (e.g., central England and northern Belgium) whilst other areas see a decline in suitability and consequently lower WNV risk (e.g., northern Italy and western Germany). Overall, this work will improve understanding of mosquito-borne disease risk in changing environments and demonstrates how species distribution modelling can be used to aid contingency preparedness by highlighting areas at higher risk of emerging disease.

pathology↗

bioRxiv2024

Using correlative and mechanistic species distribution models to predict vector-borne disease risk for the current and future environmental and climatic change: a case study of West Nile Virus in the UK.

Globally, vector-borne diseases have significant impacts on both animal and human health, and these are predicted to increase with the effects of climate change. Understanding the drivers of such diseases can help inform surveillance and control measures to minimise risks both now and in the future. In this study, we illustrate a generalised approach for assessing disease risk combining species distribution models of vector and wildlife hosts with data on livestock and human populations using the potential emergence of West Nile Virus (WNV) in the UK as a case study. Currently absent in the UK, WNV is an orthoflavivirus with a natural transmission cycle between Culex mosquitos (Cx. pipiens and Cx. modestus) and birds. It can spread into non-target hosts (e.g., equids, humans) via mosquito bites where it can cause febrile disease with encephalitis and mortality in severe cases. We compared six correlative species distribution models and selected the most appropriate for each vector based on a selection of performance measures and compared this to mechanistic species distribution models and known distributions. We then combined these with correlative species distribution models of representative avian hosts, equines, and human population data to predict risk of WNV occurrence. Our findings highlighted areas at greater risk of WNV due to higher habitat suitability for both avian hosts and vectors, and considered how this risk could change by 2100 under a best-case Shared Socioeconomic Pathway (SSP1) and worst-case (SSP5) future climate scenario. Generally, WNV risk in the future was found to increase in south-eastern UK and decrease further north. Overall, this paper presents how current and future vector distributions can be modelled and combined with projected host distributions to predict areas at greater risk of novel diseases. This is important for policy decision making and contingency preparedness to enable adaptation to changing environments and the resulting shifts in vector-borne diseases that are predicted to occur.

ecology↗

bioRxiv2024

Selection of movement rules to simulate species dispersal in a mosaic landscape model

Dispersal is an ecological process central to population dynamics, representing an important driver of movement between populations and across landscapes. In spatial population models for terrestrial vertebrates, capturing plausible dispersal behaviour is of particular importance when considering the spread of disease or invasive species. The distribution of distances travelled by dispersers, or the dispersal kernel, is typically highly skewed, with most individuals remaining close to their origin but some travelling substantially further. Using mechanistic models to simulate individual dispersal behaviour, the dispersal kernel can be generated as an emergent property. Through stepwise simulation of the entire movement path, models can also account for the influence of the local environment, and contacts during the dispersal event which may spread disease. In this study, we explore a range of simple rules to emulate individual dispersal behaviour within a mosaic model generated using irregular geometry. Movement rules illustrate a limited range of behavioural assumptions and when applied across these simple synthetic landscapes generated a wide range of emergent kernels. Given the variability in dispersal distances observed within species, our results highlight the importance of considering landscape heterogeneity and individual-level variation in movement, with simpler rules approximating random walks providing less plausible emergent kernels. As a case study, we demonstrate how rule sets can be selected by comparison to an empirical kernel for a study species (red fox; Vulpes vulpes). These results provide a foundation for the selection of movement rules to represent dispersal in spatial agent-based models, however, we also emphasise the need to corroborate rules against the behaviour of specific species and within chosen landscapes to avoid the potential for these rules to bias predictions.

ecology↗