Privacy-preserving controls for sharing mHealth data

Mobile devices allow people to collect and share health and health-related information with recipients such as health providers, family and friends, employers and insurance companies, to obtain health, emotional or financial benefits. People may consider certain health information sensitive and prefer to disclose only what is necessary. In this dissertation, we present our findings about factors that affect people’s sharing behavior, describe scenarios in which people may wish to collect and share their personal health-related information with others, but may be hesitant to disclose the information if necessary controls are not available to protect their privacy, and propose frameworks to provide the desired privacy controls. We introduce the concept of close encounters that allow users to share data with other people who may have been in spatio-temporal proximity. We developed two smartphone-based systems that leverage stationary sensors and beacons to determine whether users are in spatio-temporal proximity. The first system, ENACT, allows patients diagnosed with a contagious airborne disease to alert others retrospectively about their possible exposure to airborne virus. The second system, SPICE, allows users to collect sensor information, retrospectively, from others with whom they shared a close encounter. We present design and implementation of the two systems, analyse their security and privacy guarantees, and evaluate the systems on various performance metrics. Finally, we evaluate how Bluetooth beacons and Wi-Fi access points can be used in support of these systems for close encounters, and present our experiences and findings from a deployment study on Dartmouth campus

Statistical physics of vaccination

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination–one of the most important preventive measures of modern times–is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research

Targeted vaccination based on a wireless sensor system

Vaccination is one of the most effective ways to protect people from being infected by infectious disease. However, it is often impractical to vaccinate all people in a community due to various resource constraints. Therefore, targeted vaccination, which vaccinates a small group of people, is an alternative approach to contain infectious disease spread. To achieve better performance in targeted vaccination, we collect student contact traces in a high school based on wireless sensors carried by students. With our wireless sensor system, we can record student contacts within the disease propagation distance, and then construct a disease propagation graph to model the infectious disease propagation. Based on this graph, we propose a metric called connectivity centrality to measure a node's importance during disease propagation and design centrality based algorithms for targeted vaccination. The proposed algorithms are evaluated and compared with other schemes based on our collected traces. Trace driven simulation results show that our algorithms can help to effectively contain infectious diseas