The Douglas-fir tussock moth and the spongy moth are insect pests that defoliate forests in North America, causing millions of dollars of damage every year, but damage would be far worse if not for the mortality caused by insect-killing viruses. Models that could predict how and when insect viruses will protect forests from defoliating insects would be invaluable for protecting forests. The creation of accurate models is hampered by the computational difficulties of using data to create realistic models, and by the logistic difficulties of collecting sufficient data to determine the best models. The investigators have recently developed a new class of interpretable machine learning algorithms that can discover the best mathematical models directly from data, even if the data are sparse and noisy, as ecological data usually are. In this project, the investigators will advance these methods to work with insect host-pathogen data. The ultimate goal is to rapidly provide robust, evidence-based models for guiding the management of pests of American forests. This project will foster a variety of inter-disciplinary mathematical biology and quantitative ecology research experiences for graduate and undergraduate students. Students in high school and university communities will be trained through the project outreach activities. The goal of this work is to advance Weak form Scientific Machine Learning (WSciML) theory and methodology, expanding its capabilities in model discovery a