Measuring human behavioral patterns has broad application across different sciences. An individual’s social, proximal and geographical contact patterns can have significant importance in Delay Tolerant Networking (DTN) and epidemiological modeling. Recent advances in computer science have not only provided the opportunity to record these behaviors with considerably higher temporal resolution and phenomenological accuracy, but also made it possible to record specific aspects of the behaviors which have been previously difficult to measure.
This thesis presents a data collection system using tiny sensors which is capable of recording humans’ proximal contacts and their visiting pattern to a set of geographical locations. The system also collects information on participants’ health status using weekly surveys. The system is tested on a population of 36 participants and 11 high-traffic public places. The resulting dataset offers rich information on human proximal and geographic contact patterns cross-linked with their health information.
In addition to the basic analysis of the dataset, the collected data is applied to two different applications. In DTNs the dataset is used to study the importance of public places as relay nodes, and described an algorithm that takes advantage of stationary nodes to improve routing performance and load balancing in the network. In epidemiological modeling, the collected dataset is combined with data on H1N1 infection spread over the same time period and designed a model on H1N1 pathogen transmission based on these data. Using the collected high-resolution contact data as the model’s contact patterns, this work represents the importance of contact density in addition to contact diversity in infection transmission rate. It also shows that the network measurements which are tied to contact duration are more representative of the relation between centrality of a person and their chance of contracting the infection
