Flunet: Automated tracking of contacts during flu season

By analyzing people's contact patterns over time, it is possible to build efficient delay tolerant networking (DTN) algorithms and derive important data for parameterizing and calibrating epidemiological models. Significant research has been performed in the automated acquisition of contact patterns using mobile devices such as Zigbee motes or Bluetooth-enabled cellular phones. However, the limited number of studies described to date do not capture the breadth of human experience or specifically include the acquisition of health related information. In this paper we present Flunet, a mobile contact-tracking network deployed in a Canadian university environment during flu season. Flunet tracked contact patterns of 36 participants and their proximity to 11 stationary nodes using MicaZ motes over a period of three months. Participants filled out weekly surveys on their state of health. This study is distinct from others because we incorporate health information and the impact of sub-zero temperatures on mobility patterns. This paper presents a preliminary analysis of the data set, primarily from a DTN perspective. We present fundamental attributes of the dataset, the efficiency of routing for single pass and flooding-based algorithms and a preliminary look at the relationship between network characteristics and health status.