Dr Chris Banks

I am a computer scientist and computational/mathematical modeller and currently a Core Scientist and Research Fellow at The Roslin Institute, University of Edinburgh. I work within the group of Prof Rowland Kao. Over my career, my research has focused on the computational modelling of systems to help improve the understanding of the natural and synthetic world.

My current research focuses on developing veterinary epidemiological models for UK agricultural disease. This includes computational modelling of disease transmission based on real-world scenarios, as well the analysis for understanding the associated risk factors and the mitigation of disease spread.

I am currently working on two major projects:
1) Machine learning models for improving disease diagnostics. Here we incorporate information on epidemiological and environmental risk factors into the interpretation of diagnostic tests using machine learning models. This has two benefits: improved herd-level accuracy of test and the ability to identify the risk factors that have greatest impact.

2) Combining land use statistics, economic models of land use change, and species distributions to estimate infectious disease risks due to land use and climate change. Here we are looking at the change in land use due to the provision of forestry subsidies, and how the creation of new woodland may be bringing wild deer populations closer to cattle herds.

I also work on a number of smaller projects within animal health and bioscience including: deep learning for diagnosing disease in small animal CT scans, using deep learning to identify and time developmental stages in chicken embryos, using models to quantify the effect of climate or land use changes on disease transmission and genetics, and various applications of a large models of bovine TB transmission in the UK and Avian Influenza in US cattle. Prior research on COVID-19 transmission modelling contributed to the response to the pandemic and provided advice to Public Health Scotland and Scottish Government during that time.