Mathematical Modelling of Human Behaviour in Disease Management: A Case Study on Precision-Bred Cereal Adoption in the UK and Policy Implications

According to the AHDB a major fungal disease, Septoria triticii, is estimated to cause annual yield losses in the UK wheat sector ranging from £110 million to £220 million, despite the use of fungicides. With disease incidences increasing, integrating dynamic human behaviour into epidemiological models promises enhanced effectiveness of disease management practices in crops, crucial in the context of sustainable food and agricultural systems amid climate change.
This study uses a mixed-methods approach to adapt an existing epidemiological model for Septoria triticii in wheat, to incorporate human behaviour dynamics. A structured literature review revealed that there are a limited number of studies on agricultural pest and disease models incorporating dynamic human behaviour, most of which are spatial and primarily consider decision-making rather than factors such as willingness (publication forthcoming).
In collaboration with modelling and social science experts, the study aims to conduct interviews with 20 farmers in lowland England, and a survey with 100 farmers across the UK to develop the model schematic and inform model parameters, considering existing management methods for treating Septoria triticii in wheat and farmers' perceptions of future methods such as precision-bred crop adoption, considering social, economic and environmental aspects.
The model will be applied to adoption scenarios of precision-bred wheat and intends to improve the predictive ability of Septoria triticii prevalence. This can lead to greater insights for policymakers on which levers are most effective in supporting farmers to reducing chemical application and transitioning to adoption of precision-bred varieties with improved traits, to enhance agriculture and food sufficiency and sustainability in the UK.
In the future, the resulting model can be enhanced by a systems-thinking approach of incorporating modelling scenarios that include other value chain actors, such as consumers.