Preventing pandemics via emergent behavior

Abstract

Attempts to avoid 'massive noise' in large data sets associated with social networks have moved away from technical approaches that attempt to filter or classify social network data into meaningful elements through key words or other classification schemes. Rather, new approaches have an increased emphasis on communication flows between people in order to determine situational awareness. This paper summarizes recent innovative projects that stress agents (individuals) interacting with each other to generate an emergent and evolving social network. The projects build on Norbert Wiener's concept of 'emergent behavior' and show how it is applied to communications between individuals reporting on emerging biological diseases. Socio-technical analyses have concluded that the lack of feedback explains why Severe Acute Respiratory Syndrome (SARS) was widely transmitted. A proactive approach to disease detection using feedback loops is introduced to help the Influenza-Like Illness (ILI) detection community communicate in a social network dedicated to the prevention of pandemics. This network does not depend on predetermined categories of information. Rather, it tracks a pandemic as it evolves in such a way that 'digital pheromones' help to prevent the risk of wide transmission in a changing socio-ecological world.