Interactions among human behavior, social networks, and societal infrastructures: A Case Study in Computational Epidemiology
- 762 Downloads
Human behavior, social networks, and the civil infrastructures are closely intertwined. Understanding their co-evolution is critical for designing public policies and decision support for disaster planning. For example, human behaviors and day to day activities of individuals create dense social interactions that are characteristic of modern urban societies. These dense social networks provide a perfect fabric for fast, uncontrolled disease propagation. Conversely, people’s behavior in response to public policies and their perception of how the crisis is unfolding as a result of disease outbreak can dramatically alter the normally stable social interactions. Effective planning and response strategies must take these complicated interactions into account. In this chapter, we describe a computer simulation based approach to study these issues using public health and computational epidemiology as an illustrative example. We also formulate game-theoretic and stochastic optimization problems that capture many of the problems that we study empirically.
Keywordsinteraction-based computing theory of simulations agent-based models biological socio-technical and information systems urban infrastructures discrete dynamical systems computational complexity combinatorial algorithms
- C. L. Barrett, R. J. Beckman, K. P. Berkbigler, K. R. Bisset, B. W. Bush, K. Campbell, S. Eubank, K. M. Henson, J. M. Hurford, D. A. Kubicek, M. V. Marathe, P. R. Romero, J. P. Smith, L. L. Smith, P. L. Speckman, P. E. Stretz, G. L. Thayer, E. V. Eeckhout, and M. D. Williams. Transims: Transportation analysis simulation system. Technical Report LA-UR-00-1725, Los Alamos National Laboratory Unclassified Report, 2001. Google Scholar
- C. L. Barrett, K. Bisset, S. Eubank, V. S. A. Kumar, M. V. Marathe, and H. S. Mortveit. Modeling and simulation of large biological and information and socio-technical systems: An interaction-based approach. In Proc. Short Course on Modeling and Simulation of Biological Networks, AMS Lecture Notes, Series: PSAPM, 2007. Google Scholar
- C. Barrett, K. Bisset, J. Chen, B. Lewis, S. Eubank, V. S. A. Kumar, M. Marathe, and H. Mortveit. Effect of public policies and individual behavior on the co-evolution of social networks and infectious disease dynamics. In Proc. DIMACS DyDAn Workshop on Computational Methods for Dynamic Interaction Networks, 2007. Google Scholar
- R. Breban, R. Vardavas, and S. Blower. Inductive reasoning games as influenza vaccination models: Mean field analysis. In arXriv: q-bio.PE/0608016, 2006.
- J. Epstein, J. Parker, and D. Cummings. Coupled contagion dynamics of fear and disease: A behavioral basis for the 1918 epidemic waves: Mathematical and computational explorations. Technical Report, Brookings Institute, 2006. Presentation made at the MIDAS meeting. Google Scholar
- S. Eubank, V. S. A. Kumar, M. Marathe, A. Srinivasan, and N. Wang. Structural and algorithmic aspects of large social networks. In Proc. 15th ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 711–720, 2004. Google Scholar
- S. Eubank, V. S. A. Kumar, M. Marathe, A. Srinivasan, and N. Wang. Structure of social contact networks and their impact on epidemics. In AMS-DIMACS Special Volume on Epidemiology, 2005. Google Scholar
- N. Fredkin. A Structural Theory of Social Influence. Cambridge University Press, Cambridge, 1998. Google Scholar
- N. Immorlica, D. Karger, M. Minkoff, and V. S. Mirrokni. On the costs and benefits of procrastination: Approximation algorithms for stochastic combinatorial optimization problems. In Proceedings of the Fifteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 684–693, 2004. Google Scholar
- D. Kempe, J. Kleinberg, and E. Tardos. Influential nodes in a diffusion model for social networks. In Proc. International Colloquium on Automata Programming and Languages (ICALP), pages 1127–1138, 2005. Google Scholar
- P. Lazarsfeld and R. Merton. Friendship as social process. In T. Abel and C. Page, editors, Freedom and Control in Modern Society, Van Nostrand, New York, 1957. Google Scholar
- R. Leenders. Structure and influence, statistical models for the dynamics of actor attributes, network structure and their independence. PhD Thesis, Amsterdam, 1995. Google Scholar
- M. Mavronicolas, V. Papadopoulou, A. Philippou, and P. Spirakis. A network game with attacker and protector entities. In Proceedings of the 16th Annual International Symposium on Algorithms and Computation (ISAAC 2005), volume 3827, pages 288–297, 2005. Google Scholar
- T. Moscibroda and R. Wattenhofer. When selfish meets evil: Byzantine players in a virus inoculation game. In 25th Annual Symposium on Principles of Distributed Computing (PODC), pages 35–44, 2006. Google Scholar
- T. Snijders, C. Steglich, and M. Schweinberger. Modeling the co-evolution of networks and behavior. In K. van Montfort, H. Oud and A. Satorra, editors, Longitudinal Models in the Behavioral and Related Sciences. Routledge/Taylor & Francis, New York, 2006. Google Scholar
- C. Steglich, T. Snijders, and M. Pearson. Dynamic networks and behavior: Separating selection from influence. Technical Report, University of Groningen, The Netherlands, 2007. Available at http://stat.gamma.rug.nl/snijders/.
- P. Young. Individual Strategy and Social Structure: An Evolutionary Theory of Institutions. Princeton University Press, Princeton, 1998. Google Scholar