Mining Person-Centric Datasets for Insight, Prediction, and Public Health Planning
In order to increase the accuracy and realism of agent-based simulation systems, it is necessary to take the full complexity of human behavior into account. Mobile phone records are capable of capturing this complexity, in the form of latent patterns. These patterns can be discovered via information processing, data mining, and visual analytics. Mobile phone records can be mined to improve our understanding of human societies, and those insights can be encapsulated in population models. Models of geographic mobility, travel, and migration are key components of both population models and the underlying datasets of simulation systems. For example, using such models enables both the analysis of existing traffic patterns and the creation of accurate simulations of real-time traffic flow. The case study presented here demonstrates how latent patterns and insights can be (1) extracted from mobile phone datasets, (2) turned into components of population models, and (3) utilized to improve health-related simulation software. It does so within the context of computational epidemiology, applying the Data Science process to answer nine specific research questions pertaining to factors influencing disease spread in a population. The answers can be used to inform a country’s strategy in case of an epidemic.
Unable to display preview. Download preview PDF.
- Barrett, C., Bisset, K., Eubank, S., Feng, X., & Marathe, M. (2008). EpiSimdemics: An efficient algorithm for simulating the spread of infectious disease over large realistic social networks. In Proceedings of the 2008 ACM/IEEE Conference on Supercomputing (SC ‘08) (pp 1–12). IEEE: Piscataway, NJ.Google Scholar
- Bertini, E., Hertzog, P., & Lalanne, D. (2007). SpiralView: Towards security policies assessment through visual correlation of network resources with evolution of alarms. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST) (pp 39–146) 2007.Google Scholar
- Bisset, K., Chen, J., Feng, X., Vullikanti, A., & Marathe, M. (2009). EpiFast: A fast algorithm for large scale realistic epidemic simulations on distributed memory systems. In Proceedings of the 23rd International Conference on Supercomputing (ICS ‘09) (pp 430–439). ACM: New York, NY.Google Scholar
- Ester, M., Kriegel, H.P., Sander,J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In 2nd International Conference on Knowledge Discovery and Data Mining (KDD), Portland, OR.Google Scholar
- Jiang, S., Ferreira, J., & González, M. (2015). Activity-based human mobility patterns inferred from mobile phone data: A case study of Singapore. In International Workshop on Urban Computing.Google Scholar
- Leidig, J.P., & Dharmapuri, S. (2015). Automated visualization workflow for simulation experiments. In IEEE Symposium on Information Visualization (InfoVis), Chicago, IL.Google Scholar
- MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. In 5th Berkeley Symposium on Mathematical Statistics and Probability (pp. 81–297). University of California Press.Google Scholar
- United States Department of Commerce: Bureau of the Census. (2017). American community survey (ACS): Public use microdata sample (PUMS).Google Scholar