Infectious disease transmission relies on the contact of infected individuals with susceptible individuals. For many species (including humans), behavior is the strongest force governing the probability of contacts among individuals. Traditional models overlook individual- and small-group-level behavior in favor of approximating global average "mixing rates". More recent work has begun to examine the effect of explicit social contact networks, or else the more tailored uniform mixing of distinct subpopulations, on disease spread. Large-scale agent based simulations have also begun to incorporate incredible amounts of behavioral detail into the framework of the experiments in hopes of finding certain sets of critical behaviors which could drive population-level disease dynamics.
Recent worries about pandemic flu and bioterrorism threats have increased the scope of questions to be addressed by behavioral epidemiology: how would individuals react to widespread disease exposure risks, how can we expect individuals to act if offered protective vaccines that themselves carry the risk of adverse events, and, most importantly, how could these individual behaviors themselves influence the scope of an outbreak. One of the most interesting and primarily unaddressed questions in this area is whether or not local behavioral interventions can scale up to affect nation-wide disease risks. Answering these questions, for endemic, epidemic and pandemic disease threats, whether natural or introduced, will necessitate the creation of new types of investigative models. To explore such questions, this workshop will bring together applied mathematicians in the areas of game theory, graph theory and operations research, mathematical epidemiologists (both human and wildlife), economists, animal behaviorists, conservation ecologists, medical sociologists, and public health officials.