Learning the probability of activation in the presence of latent spreaders
Name
1712.00643.pdf
Description
Submitted version
Size
554.27 KB
Format
Adobe PDF
Checksum (MD5)
e262767130aeebe0fdb3799be58faa42
Author(s)
Makar, Maggie
Guttag, John V
Wiens, Jenna
Date Issued
2018
Journal
Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Citation
Makar, Maggie et al. "Learning the probability of activation in the presence of latent spreaders." Thirty-Second AAAI Conference on Artificial Intelligence, February 2018, New Orleans, Louisiana, Association for the Advancement of Artificial Intelligence, 2018. © 2018 Association for the Advancement of Artificial Intelligence
Version
Original manuscript
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 20,000 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
