Epidemiological models are a simplification of complex ecological systems, involving the interactions of host, agent, environment, and mankind.

At the James Hutton Institute we construct models to help us better understand and predict the dynamics of important pests and pathogens in agriculture, and to identify optimal strategies for control. This work integrates and synthesises data from key areas of research within the Institute, and is funded through a range of sources: UK research councils; the Scottish Government’s Strategic Research Programme; government centres of expertise, such as ClimateXChange; and levy boards, such as the Agriculture and Horticulture Development Board.

Theoretical epidemiology

We develop new epidemic theory using simple analytical and numerical models for the spread of pests and diseases in heterogeneous landscapes. For example, the ‘dispersal scaling hypothesis’ is a new general relationship between scale of host patterning and magnitude of epidemic spread that can be exploited by available management options. This new theory was developed using an array of models that encompassed the biotic / abiotic dispersal and spread of a diverse range of economically important species: a major insect pest of coniferous forests, the mountain pine beetle (Dendroctonus ponderosae); the bacterium Pseudomonas syringae, one of the most-widespread and best-studied bacterial plant pathogens; the mosquito Culex erraticus, an important vector for many human and animal pathogens; and the oomycete Phytophthora infestans, the causal agent of potato late blight.

The dispersal scaling hypothesis

Figure 1: Conceptual diagram of the dispersal scaling hypothesis.

Pathosystem simulation

We also develop more complex simulation models of major disease pathosystems. We simulate the life cycle of the host crop (at the scale of individual leaves / plants), the life cycle of the disease agent (for different pest / pathogen strains), spread of infectious agents to distant crops, and the influence of weather and management on final epidemic extent. We parameterise and validate each component using a mixture of laboratory and field experiments. We then use these models as virtual experimental platforms, to answer specific questions regarding the nature and management of disease spread at temporal and spatial scales that preclude experimentation. We are currently investigating the influence of within-crop heterogeneity on Rhynchosporium in barley, and landscape-scale crop and pathogen heterogeneity on the spread of Fusarium species in cereals, and Phytophthora infestans in potato.

Figure 2: A simulation model of the potato late blight pathosystem, comprised of a field-scale model and a model of the aerobiological component of the disease cycle, applied to real crop distributions and driven by historical weather data. Here we are investigating how subtle differences between P. infestans genotypes measured in the laboratory translate to differences in epidemic severity at the landscape-scale. The simulated epidemic pictured above is for illustrative purposes only and does not represent any actual instances of disease occurrence.

Decision support systems

Decision support systems integrate and organise information on pest / pathogen life cycles, the weather (historical and forecast), plant growth, fungicides, cultivar resistance, and disease pressure in order to facilitate day-to-day decisions regarding the need for plant protection products. At the James Hutton Institute we believe that a number of opportunities exist to improve current decision support systems.

For example, we recently developed the new national warning system for potato late blight in Great Britain – the Hutton Criteria. This has replaced 60 years of using the Smith Period to forecast the risk of late blight. The Hutton Criteria were rolled out to the entire potato industry in 2017 via the AHDB Potatoes Blightwatch service.

Figure 3: Screenshot from the Blightwatch service showing Hutton Criteria risk predictions for a selection of postcodes.

Climate change risk assessment

We combine our linked crop disease models with data on the spatial coverage of crops and climate change scenarios to deliver geospatial representations of future food security issues. We aim to provide a quantitative analysis of future climatic and geographic risks for numerous crop pests and pathogens, in order to identify adaptation priorities for Scottish agriculture. Our main initial focus is on Fusarium species, Escherichia coli, Dickeya and Pectobacterium species, Phytophthora infestans, and potato cyst nematodes, as the pathogenesis and ecology of these species are main areas of research within the Institute.

ICCRI modular risk assessment framework

Figure 4: We have constructed an overarching modular simulation framework that can easily incorporate different crop distribution datasets, future climate scenarios, crop growth, pest/disease risk, and dispersal models to determine the future environmental and geographic risks of key crop pests and pathogens.

We recently developed a desktop app for performing state-of-the-art climate change risk assessments in real crop locations. The app is packed with features, including real crop distributions, probabilistic climate change data, a module for eaily building your own risk model, pest and pathogen dispersal, and much more. The app is freely available for download using the link on the project page


Figure 5: Screenshot from the 4C-model desktop app, showing the risk of disease spread among potato crops in the English Midlands for May to September under a low CO2 emissions scenario in the 2040s. 

Contact: Peter Skelsey, James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK.