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.

Abstract

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.