There is concern that climate change will lead to expansion of vector-borne diseases as, of all disease types, they are the most sensitive to climate drivers1. Such expansion may threaten human health, and food security via effects on animal and crop health. Here we quantify the potential impact of climate change on a vector-borne disease of livestock, bluetongue, which has emerged in northern Europe in response to climate change2,3,4, affecting tens of thousands of farms at huge financial cost and causing the deaths of millions of animals5. We derive future disease risk trends for northern Europe, and use a detailed spatial transmission model6 to simulate outbreaks in England and Wales under future climatic conditions, using an ensemble of five downscaled general circulation models7. By 2100, bluetongue risk extends further north, the transmission season lengthens by up to three months and outbreaks are larger on average. A 1 in 20-year outbreak at present-day temperatures becomes typical by the 2070s under the highest greenhouse gas emission scenario. However, animal movement restrictions are sufficient to prevent truly devastating outbreaks. Disease transmission uncertainty dominates over climate uncertainty, even at the longest prediction timescales. Our results suggest that efficient detection and control measures to limit the spread of vector-borne diseases will be increasingly vital in future, warmer climates.
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The full model code in C++ is available upon request by contacting A.E.J., J.T. or M.B. Where possible within the limits of restrictions on confidential data (see Data availability), data analysis scripts and related input data are available on the open science framework platform at: https://osf.io/kn28j.
The farm and animal movement data are not publicly available due to information that could compromise farmer privacy/consent. Data are available only from the Animal and Plant Health Agency and subject to a confidentiality agreement. The UKCP09 daily gridded climate dataset from 1960 to 2016 is now publicly available, following registration, under Open Government Licence at http://catalogue.ceda.ac.uk/uuid/87f43af9d02e42f483351d79b3d6162a. The E-OBS daily gridded climate dataset for Europe is publicly available, following registration, at http://www.ecad.eu/download/ensembles/download.php. The NASA NEX-GDDP ensemble is publicly available at the Climate Model Services of the National Aeronautics and Space Agency at https://cds.nccs.nasa.gov/nex-gddp/. The Ordnance Survey boundary data is publicly available at https://www.ordnancesurvey.co.uk/opendatadownload/products.html#BDLINE.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was funded by BBSRC grant BB/J015806/1 awarded to M.B., A.P.M. and P.D. and by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emerging and Zoonotic Infections at the University of Liverpool in partnership with Public Health England and Liverpool School of Tropical Medicine. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England. The UK Climate Projections (UKCP09) have been made available by the Department for Environment, Food and Rural Affairs (Defra) and the Department of Energy and Climate Change (DECC) under licence from the Met Office, UKCIP, British Atmospheric Data Centre, Newcastle University, University of East Anglia, Environment Agency, Tyndall Centre and Proudman Oceanographic Laboratory. The authors would like to thank D. Hemming and M. Kendon at the Met Office for providing the UKCP09 data. We acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu). Climate scenarios used were from the NEX-GDDP dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange and distributed by the NASA Center for Climate Simulation. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and leads development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The authors would like to thank the Animal and Plant Health Agency (APHA) for providing the agricultural census and animal movement data. UK county boundaries were downloaded from OS OpenData (https://www.ordnancesurvey.co.uk).