James Holland Jones: Stanford University Department of Anthropology

Interaction networks shaped by social processes constitute the substrate on which various phenomena of interest to evolutionary anthropologists and human biologists occur. Examples of such phenomena include epidemics, diffusion of ideas or information, and the exertion of social and political influence. Understanding the structure of network formation is thus crucial to our understanding of how relational human interactions mediate key biosocial outcomes. Research in this area has been hampered by an acute lack of (1) high-quality data on the formation and structure of contact networks and (2) realistic models for the social processes that shape networks. In this talk, I present results from two recent papers that address these deficits. First, I will show the consequences for epidemic behavior of networks that exhibit strong community structure. Such hierarchical social interaction is ubiquitous in human populations, making it important that we understand the behavior of epidemics in community-structured networks. We show that the degree of community structure has a pronounced impact on the final size of simulated epidemics, even in the absence of any changes in the basic reproduction number of the epidemic. We use our results to design network-informed vaccination strategies that generally outperform comparable existing strategies.

Our finding that community structure can affect epidemic behavior qualitatively highlighted the acute lack of high-quality data on network structure for relations relevant to disease transmission and motivated our collection of such data. Using Wireless Sensor Network (WSN) technology, we measured the temporal dynamics of close-proximity interaction networks during a typical school day in a high school in the San Francisco Bay Area. Participants wore small wireless sensors which send and receive radio signals to and from other sensors nearby. This technology allowed us to collect dynamic contact network data with unparalleled precision. At a 94% coverage, we collected 762,868 CPIs at a maximal distance of 3 meters among 788 individuals. The data revealed a high density network with typical small world properties and a relatively homogenous distribution of both interaction time and interaction partners among subjects. Computer simulations of the spread of an influenza-like disease on the weighted contact graph are in good agreement with absentee data during the most recent influenza season. Analysis of targeted immunization strategies suggested that contact network data are required to design strategies that are significantly more effective than random immunization. Immunization strategies based on contact strength were generally most effective and the effects were seen most acutely at high vaccination coverage.