Using citrus disease as a model for scaling from disease control for individual growers to strategies for regional control

Theme: Agriculture and Food Security

Citrus production in the United States, Brazil and worldwide is threatened by a number of exotic pathogens, most notably citrus canker and citrus greening. Due to intensive research interest and particularly the availability of good epidemiological data, most notably detailed disease surveys at both local and regional scales, citrus is an excellent model system for food security threats to agricultural and horticultural crops more generally. A number of recent models of citrus diseases have targeted spread at scales relevant to individual producers, often tracking the disease status of individual plants within a planting. However, these models can be extended in a number of ways, such as more faithfully representing the effects of control strategies, accounting for within-host pathogen dynamics, including environmental drivers, accounting for selection pressures caused by preferential removal of symptomatic hosts, and, for citrus greening, vector dynamics.

In practice, success of control in protecting threatened regions will depend on matching the temporal and spatial scales of control with the inherent scales of pathogen and vector populations. However, models of large-scale spread dynamics have not yet been developed. Scaling-up smaller-scale models to track spread at spatial scales would allow landscape-scale control to be understood and assessed.

The PhD project would use mathematical modelling to simulate the spread of citrus canker and to identify the best control methods to reduce the spread of disease. There is the opportunity to look at other diseases and to examine how the models produced can be used across a variety of different diseases.

The project will involve incorporating biological knowledge into a mathematical model, simulating disease spread and analysing the impact of factors such as different control methods and environmental and spatial changes. This will involve complex modelling in both R Studio and Python, as well as a need to graphically represent results in a clear and concise way.