Predicting unknown mammalian viral hosts with Dynamic Positive-Unlabeled learning.

Most emerging infectious diseases in humans are zoonotic in origin. Yet, knowledge of host-pathogen associations remains limited. Machine learning models have been increasingly used to infer missing zoonotic host-pathogen associations, but face challenges due to biased data and the absence of confirmed negative cases. Here, we introduce Dynamic Positive-Unlabeled (DPU) learning, a semi-supervised framework for predicting missing links in incomplete, biased networks. DPU learning integrates a propensity score model that estimates the probability of observing existing links with a classifier that predicts true link existence. This approach corrects predictions for uneven sampling bias and explicitly accounts for missing links arising from either true absences of associations or gaps in data collection. We applied DPU learning to predict missing associations between 5,330 mammalian species and 33 viral families worldwide, integrating phylogeographic relationships between mammals, observed mammal-virus association patterns, mammalian traits, and viral genetic characteristics. Our model predicted newly confirmed associations absent from the training data with a median probability of 0.82 and estimated a 8.6-fold increase in the total number of mammalian species-viral family associations. These results highlight the potential of DPU learning to support strategic, data-driven surveillance activities for proactive zoonotic risk mitigation.