Understanding how a disease spreads in a population is a first step to preparing for future epidemics, and machine learning models are a useful tool to analyze the spreading process of infectious diseases. For effective predictions of these spreading processes, node embeddings are used to encode networks based on the similarity between nodes into feature vectors, i.e., higher dimensional representations of human contacts. In this work, we evaluated the impact of homophily and structural equivalence on embedding for disease spread prediction by testing them on real world temporal human contact networks. Our results show that structural equivalence is a useful indicator for the infection status of a person. Embeddings that are balanced towards the preservation of structural equivalence performed better than those that focus on the preservation of homophily, with an average improvement of 0.1042 in the f1-score (95% CI 0.051 to 0.157). This indicates that structurally equivalent nodes behave similarly during an epidemic (e.g., expected time of a disease onset). This observation could greatly improve predictions of future epidemics where only partial information about contacts is known, thereby helping determine the risk of infection for different groups in the population.