Towards Agent-based Disease Spread Modeling Combining Knowledge-driven Simulation and Machine Learning

Thanks to the progress in precision agriculture and remote sensing, automatic visual disease severity scoring of largescale fields is getting more efficient. This potentially allows the investigation and observation of disease spread dynamics and infection incidence in real-world field trials. Furthermore, phytopathological research has been obtaining comprehensive knowledge about pathogens and epidemiology for decades. In this work, we want to leverage modern data-driven Machine Learning (ML) methods and connect them with available knowledge incorporated by simulation techniques. As a result, we show how to create infection datasets from disease severity annotations and environmental data that can serve as training data for a transformer-based ML model. First preliminary results show coherent, interpretable results that motivate for further optimization and improvements of our modeling strategy.