Abstract

Human interactions sensed ubiquitously by mobile phones can improve a significant number of public health problems, particularly helping to track the spread of disease. In this talk, we evaluate multiple avenues for the integration of high-resolution face to face Bluetooth-sensed interaction networks into epidemic models. Our goal is to evaluate the capacity of the different avenues of integration to track the spread of seasonal influenza on a real-world community of 72 individuals over a period of 17 weeks. The dataset considered contains real-time tracking of individual flu symptoms over the whole observation period, providing a concrete individualized source for evaluation. We present two different studies on this dataset. The first considers the standard SIR model simulated over real network dynamics with the overall goal of predicting the real infections over time. We obtain an error of less than 2 infected people on average when predicting the total number of individuals affected by the flu and a precision of approximately 30% when predicting exactly which individual will become infected at a given time. Our results indicate that high-resolution mobile phone data can increase the predictive power of even the simplest of epidemic models. The second study proposes a dual model for contact tracing, where an infection is spreading in the physical interpersonal network, which we assume can never be fully recovered, and contact tracing is occurring in a communication network which acts as a proxy for the first. Our results suggest that contact tracing via mobile phone communication may be a viable option for controlling contagious outbreaks.