A delay-tolerant network architecture for challenged internets
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Pocket switched networks and human mobility in conference environments
Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking
Resource and performance tradeoffs in delay-tolerant wireless networks
Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Simulating pandemic influenza risks of US cities
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Social sensing for epidemiological behavior change
Proceedings of the 12th ACM international conference on Ubiquitous computing
Effective utilization of place as a resource in pocket switched networks
LCN '11 Proceedings of the 2011 IEEE 36th Conference on Local Computer Networks
Proceedings of the 2nd ACM international workshop on Pervasive Wireless Healthcare
The impact of spatial resolution and representation on human mobility predictability
W2GIS'13 Proceedings of the 12th international conference on Web and Wireless Geographical Information Systems
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Contagions - either pathogens spread through contact networks or societal memes spread through social networks - impact the occurrence and character of both epidemic and endemic diseases. While computational models explore disease parameters in the context of a given contact network, these models are always subject to the caveat that reality may not be consistent with the simplified assumptions regarding contact, contagion or network structure. More - and more accurate - data on the contact dynamics between people and places could alleviate some uncertainties, and make models more robust tools for policy-makers and researchers. Properly applied, consumer electronics can serve as a valuable source of this data. Using smartphones as sensor platforms rather than personal communications devices, it is possible to record high fidelity information on a participant's location, activity level, and contacts between both people and places. This paper describes the design, architecture and a preliminary deployment of a general smartphone-based epidemiological data collection system. The dataset, gathered over one month, contains over 45 million records related to the behavioral patterns of 39 participants. We provide an initial analysis of aggregate level statistics to demonstrate the power and scope of the technique for capturing relevant data. Demonstrating the potential for such data to inform decision-making, we further perform an agent-based simulation of a flu-like illness that uses the dataset to capture aspects of both person-person and environmental (place-person) transmission. We demonstrate that the data collection is possible, valuable, and scalable and that the data can be leveraged to inform detailed models capturing more complex physical interactions than were previously feasible.