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

Abstract

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.