Conference Proceeding

An Interaction-Based Approach to Computational Epidemiology.

01/2008; In proceeding of: Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17, 2008
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    ABSTRACT: It is widely feared that a novel, highly pathogenic, human transmissible influenza virus may evolve that could cause the next global pandemic. Mitigating the spread of such an influenza pandemic would require not only the timely administration of antiviral drugs to those infected, but also the implementation of suitable intervention policies for stunting the spread of the virus. Towards this end, mathematical modelling and simulation studies are crucial as they allow us to evaluate the predicted effectiveness of the various intervention policies before enforcing them. Diagnosis plays a vital role in the overall pandemic management framework by detecting and distinguishing the pathogenic strain from the less threatening seasonal strains and other influenza-like illnesses. This allows treatment and intervention to be deployed effectively, given limited antiviral supplies and other resources. However, the time required to design a fast and accurate testkit for novel strains may limit the role of diagnosis. Herein, we aim to investigate the cost and effectiveness of different diagnostic methods using a stochastic agent-based city-scale model, and then address the issue of whether conventional testing approaches, when used with appropriate intervention policies, can be as effective as fast testkits in containing a pandemic outbreak. We found that for mitigation purposes, fast and accurate testkits are not necessary as long as sufficient medication is given, and are generally recommended only when used with extensive contact tracing and prophylaxis. Additionally, in the event of insufficient medication and fast testkits, the use of slower, conventional testkits together with proper isolation policies while waiting for the diagnostic results can be an equally effective substitute.
    Journal of The Royal Society Interface 12/2009; 7(48):1033-47. · 4.91 Impact Factor
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    ABSTRACT: 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.
    Proceedings of the 2009 Winter Simulation Conference, WSC 2009, Hilton Austin Hotel, Austin, TX, USA, December 13-16, 2009; 01/2009
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    ABSTRACT: Large scale realistic epidemic simulations have recently become an increasingly important application of high-performance computing. We propose a parallel algorithm, EpiFast, based on a novel interpretation of the stochastic disease propagation in a contact network. We implement it using a master-slave computation model which allows scalability on distributed memory systems. EpiFast runs extremely fast for realistic simulations that involve: (i) large populations consisting of millions of individuals and their heterogeneous details, (ii) dynamic interactions between the disease propagation, the individual behaviors, and the exogenous interventions, as well as (iii) large number of replicated runs necessary for statistically sound estimates about the stochastic epidemic evolution. We find that EpiFast runs several magnitude faster than another comparable simulation tool while delivering similar results. EpiFast has been tested on commodity clusters as well as SGI shared memory machines. For a fixed experiment, if given more computing resources, it scales automatically and runs faster. Finally, EpiFast has been used as the major simulation engine in real studies with rather sophisticated settings to evaluate various dynamic interventions and to provide decision support for public health policy makers.
    Proceedings of the 23rd international conference on Supercomputing, 2009, Yorktown Heights, NY, USA, June 8-12, 2009; 01/2009

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