Investigating Animal Infectious Diseases with Visual Analytics

Animal infectious diseases interfere with the sustainability of livestock farming. Developing comprehensive strategies for disease prevention and control requires professionals to study livestock farms from a variety of data sources, such as veterinary medical tests, financial reports, and animal movements between farms. However, investigating animal health surveillance is challenging as the collected data is often heterogeneous, high-dimensional, and spatio-temporal. Furthermore, data missingness, one common challenge in disease surveillance, can limit the effectiveness of the analysis and induce the misinterpretation of the result due to the lack of uncertainty representation. In this paper, we present a visual analytics interface of coordinated views that supports investigating disease outbreaks by connecting the relationships of livestock farms from different aspects — geospatial, transactional, and financial. Coupled with unsupervised machine learning methods, we infer the health status of a farm, despite the absence of its diagnostic history, with uncertainty and provide interpretability to such inferences. With these functionalities, we further quantify the influence of a disease outbreak, severity and scale, guiding the user toward investigating important outbreaks. We demonstrate the analysis capability of our visual analytics interface with multiple use cases on a real-world swine production dataset.