The control of epidemics, broadly defined to range from human diseases such as influenza and smallpox to malware in communication networks, relies crucially on interventions such as vaccinations and anti-virals (in human diseases) or software patches (for malware). These interventions are almost always voluntary directives from public agencies; however, people do not always adhere to such recommendations, and make individual decisions based on their specific "self interest". Additionally, people alter their contacts dynamically, and these behavioral changes have a huge impact on the dynamics and the effectiveness of these interventions, so that "good" intervention strategies might, in fact, be ineffective, depending upon the individual response.
The goal of this project is to study the foundations of policy design for controlling epidemics, using a broad class of epidemic games on complex networks involving uncertainty in network information, temporal evolution and learning. Models will be proposed to capture the complexity of static and temporal interactions and patterns of information exchange, including the possibility of failed interventions and the potential for moral hazard. The project will also study specific policies posed by public agencies and network security providers for controlling the spread of epidemics and malware, and will develop resource constrained mechanisms to implement them in this framework.
This project will integrate approaches from Computer Science, Economics, Mathematics, and Epidemiology to give intellectual unity to the study and design of public health policies and has the potential for strong dissertation work in all these areas. Education and outreach is an important aspect of the project, and includes curriculum development at both the graduate and under-graduate levels. A multi-disciplinary workshop is also planned as part of the project.
