Inferring Realistic Intra-hospital Contact Networks Using Link Prediction and Computer Logins

Disease spread in hospital settings is a common and important problem in health care. Knowing the network of contacts between health care workers and patients can be very helpful in mitigating disease spread. In this work, we address the problem of inferring the contact network of health care workers at the University of Iowa Hospital and Clinics facilities by integrating two sources of data: hospital-wide computer login data and proximity data obtained from direct measurement in the Medical Intensive Care Unit using a wireless sensor network. We treat this problem as a variant of the network completion problem, where one small portion of the network is well known while the rest is sparingly sampled, and we want to complete the network. In this case, we want to transform the login network, where an edge connects two people who logged into computers within some time and distance, of the hospital into a contact network. We solve this problem by borrowing techniques from link prediction. We train and evaluate these techniques on synthetic login networks and contact networks obtained from the sensor data. Our results are promising in that we can predict contact networks from login networks with accuracies mostly between 70% and 90%.