"Agent based modeling of urban-level, contact-based infectious disease spread"

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The worldwide H1N1 influenza pandemic in 2009/2010 renewed research attention to the many aspects of infection spread and control, ranging from the epidemiology of the illness, development and deployment of vaccines and other pharmaceutical measures, and public health and emergency management measures. It is critical to understand and predict how pandemic influenza spreads within a population, due to the devastating potential consequences to public health and to the overall economy of a region. This research program's objective is to develop a precise, accurate, and scalable Agent-Based Modeling (ABM) framework by which to simulate the complex social dynamics of infection spread and to simulate public health interventions at the scale of a city or region. The work goes beyond previous efforts by focusing on the integration of real data to model characteristic profiles of people's (agents') movements, behaviours, and interaction with one another to a high level of specificity and fidelity. In this program, real data includes the incorporation of existing real data to define model topographies and agent profiles, the development of new data tools to model fine- and medium-grained social networks, and the integration of real-time data feeds. Additionally, the work goes beyond previous efforts by integrating models of machine learning, or agent policy that will be automatically generated by the model to minimize infection spread. The model will be used to simulate the impacts of infection control measures for to influenza-like illness in cities and larger regions. The work has immediate applications as a decision support tool to public health and policy analysts and to emergency management. The work is unique in that the ABM approach to modeling and simulation incorporates the real-world complexity of social systems to a degree of sensitivity and fidelity that other approaches such as mathematical modeling cannot match in terms of understanding a complex phenomenon. The advantages and novelty of ABM are further amplified by integrating the potential of high performance computing, the increasing amounts of real social data becoming available to researchers, and the inclusion of machine learning.