When an infection spreads in a community, an individual's probability of
becoming infected depends on both her susceptibility and exposure to the
contagion through contact with others. While one often has knowledge regarding
an individual's susceptibility, in many cases, whether or not an individual's
contacts are contagious is unknown. We study the problem of predicting if an
individual will adopt a contagion in the presence of multiple modes of
infection (exposure/susceptibility) and latent neighbor influence. We present a
generative probabilistic model and a variational inference method to learn the
parameters of our model. Through a series of experiments on synthetic data, we
measure the ability of the proposed model to identify latent spreaders, and
predict the risk of infection. Applied to a real dataset of 20,000 hospital
patients, we demonstrate the utility of our model in predicting the onset of a
healthcare associated infection using patient room-sharing and nurse-sharing
networks. Our model outperforms existing benchmarks and provides actionable
insights for the design and implementation of targeted interventions to curb
the spread of infection.Comment: To appear in AAA1-1