Algorithms for identifying the infection states of nodes in a network are crucial for understanding and containing epidemics and cascades. Often, however, only the infection states of a small set of nodes are known. Moreover, the length of time that each node has been infected is also unknown. This missing data – infection states of most nodes and the infection times of the observed infected nodes – poses a challenge to the study of real-world epidemics/cascades.In this work, we develop techniques to identify the latent infected nodes in the presence of missing infection time-and-state data. Based on likely epidemic paths predicted by a simple susceptible-infected epidemic model, we propose a measure (infection betweenness centrality) for uncovering unknown infection states. We evaluate infection state classifiers based on Naive Bayes, Naive Bayes with kernel density estimation, and decision trees, in combination with features including infection betweenness centrality and different centrality measures. Our experimental results show that among the set of features that include degree and different centrality measures, infection betweenness centrality is the most effective feature for identifying latent infected nodes.