Abstract

We focus on the development and application of a modelling framework that aims at realistic predictions of risk of airborne spread of infectious plant diseases. In collaboration with the UK Meteorological Office we build a spatio-temporally explicit model that is based on large-scale meteorological datasets to predict atmospheric dispersal of fungal pathogens over heterogeneous host-environment landscapes on regional and continental scales. In particular, we link NAME – a Lagrangian atmospheric dispersion model (LPDM) – to environmental and epidemiological submodels. We study likely routes of airborne spread of novel strains, specifically the current Ug99 wheat stem rust epidemic, which has been considered a threat to global food security. The modelling framework was adapted for use in real-time to support decision-making in the face of current emergencies in East Africa. Overall, we hope to (i) enhance fundamental understanding of the role of meteorological dynamics for plant epidemiology and (ii) provide a generic numerical framework that can be integrated with epidemiological models to serve as a tool for optimized control-strategies.