A database supported modeling environment for pandemic planning and course of action analysis

Pandemics such as the 2009 H1N1 and the 2003 SARS events can significantly impact public health and society. In addition to analyzing historic epidemic data, computational simulation of epidemic propagation processes and disease control strategies can help us understand in the laboratory the spatio-temporal dynamics of epidemics. Consequently, the public can be better prepared and the government can control future epidemic outbreaks more effectively. Epidemic propagation simulation systems which use high performance computing technology have been proposed and developed to understand disease propagation processes. The proposed systems, however, do not strongly support two important steps in modeling disease propagation: run-time infection situation assessment and intervention adjustment. In addition, while these simulation systems are computationally efficient in their simulations, most of them have limited capabilities in terms of modeling interventions in realistic scenarios. In this dissertation, we focus on building a modeling and simulation environment for epidemic propagation and propagation control strategy. The objective of this work is to design such a modeling environment supporting the previously missing functions while still performing well in terms of the expected features such as modeling fidelity, computational efficiency, modeling capability, etc. Our proposed methodologies to build such a modeling environment are: 1) a loosely coupled and co-evolving model for disease propagation, situation assessment, and propagation control strategy, and 2) using relational databases to assess situations and simulate control strategies. Our motivations are: 1) a loosely coupled and co-evolving model allows us to design modules for each function separately and reduces design complexity for the modeling system, and 2) simulating propagation control strategies using relational databases improves the modeling capability and the human productivity of using this environment. To evaluate our proposed methodologies, we have designed and built a loosely coupled and database supported epidemic modeling and simulation environment. With detailed experimental results and realistic case studies, we demonstrate that our modeling environment provides the missing functions and greatly enhances many expected features, such as modeling capability, without significantly sacrificing computational efficiency and scalability.