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Spread Of Contagions In The Presence Of Latent Spreaders Identifying Hidden Culprits And Learning The Probability Of Infection

Abstract: 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 thousand 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 the infection.

Bio: Maggie Makar is a graduate student at CSAIL, MIT. She works on developing models and inference tools to analyze interaction data (e.g., interactions on social networks and diffusion of contagions). She focuses on untangling causal mechanisms that govern diffusion dynamics and quantifying heterogeneous effects of interventions in connected communities. She received her BA in Mathematics and Economics from the University of Massachusetts in Amherst.