Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
Scalable, efficient epidemiological simulation
Proceedings of the 2002 ACM symposium on Applied computing
A flexible, large-scale, distributed agent based epidemic model
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
EpiNet: a simulation framework to study the spread of malware in wireless networks
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
Proceedings of the 23rd international conference on Supercomputing
An interaction-based approach to computational epidemiology
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Generation and analysis of large synthetic social contact networks
Winter Simulation Conference
BioWar: scalable agent-based model of bioattacks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Optimizing epidemic protection for socially essential workers
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Poster: large-scale computational epidemiology modeling using charm++
Proceedings of the 2011 companion on High Performance Computing Networking, Storage and Analysis Companion
A general-purpose graph dynamical system modeling framework
Proceedings of the Winter Simulation Conference
Interaction-based HPC modeling of social, biological, and economic contagions over large networks
Proceedings of the Winter Simulation Conference
Indemics: An interactive high-performance computing framework for data-intensive epidemic modeling
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on simulation in complex service systems
| Hi-index | 0.00 |
Human behavior, social networks, and civil infrastructure are closely intertwined. Understanding their co-evolution is critical for designing public policies. Human behaviors and day-to-day activities of individuals create dense social interactions that provide a perfect fabric for fast disease propagation. Conversely, people's behavior in response to public policies and their perception of the crisis can dramatically alter normally stable social interactions. Effective planning and response strategies must take these complicated interactions into account. The basic problem can be modeled as a coupled co-evolving graph dynamical system and can also be viewed as partially observable Markov decision process. As a way to overcome the computational hurdles, we describe an High Performance Computing oriented computer simulation to study this class of problems. Our method provides a novel way to study the co-evolution of human behavior and disease dynamics in very large, realistic social networks with over 100 Million nodes and 6 Billion edges.