Conference Paper

An Interaction-Based Approach to Computational Epidemiology.

Conference: Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17, 2008
Source: DBLP
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    • "For instance, a model representing Chicago would involve 10 million customers, commercial locations, electrical infrastructure and associated market components. We have developed such models in the past for urban transport planning, public health epidemiology, telecommunication systems, e.g., the TRANSIMS and Simdemics modeling environments [13], [18], [20], [22], [23], [28]. Our initial work on energy systems can be found in [6]–[9], [26]. "
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    ABSTRACT: We present elements of a pervasive computing enabled modeling environment for integrated national energy systems (CI-MINES) to support policy and decision making as it pertains to co-evolving socio-energy systems. Decision support systems built using CI-MINES will provide public policy makers as well as private stakeholders entirely new ways to design and architect next-generation energy systems. When complete, CI-MINES can be used to evaluate the relative merits of competing conceptual architectures for interactive energy grids and markets before substantial investment is made in realizing them. It will also help evaluate new ways to invest in renewable energy sources and assess the reliability and security of the emerging grid architectures. CI-MINES is based on recent computational advances for modeling extremely large, complex, multi-scale socio-technical systems.
    Science and Technology, 2011 EPU-CRIS International Conference on; 01/2011
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    • "One mitigation means that can also be further explored is the role of vaccines and how they would shape the spread of the pandemic outbreak. Additionally, more complex social interaction patterns (Barrett et al. 2008) can be implemented to represent the spread of the pandemic in a more realistic manner. Data mining techniques (Baily-Kellogg et al. 2006) can then be used on the simulation results to better assist policy makers in assessing the implications and effectiveness of their policies both spatially and temporally. "
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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. DOI:10.1098/rsif.2009.0471 · 3.92 Impact Factor
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    • "Additionally, a person's health state change will affect his/her succeeding activities, leading to a new network. This co-evolution is important [8]. 2) The Disease Model: EpiSimdemics uses a disease model to specify the form of the local transition functions F explicitly . "
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    ABSTRACT: Preventing and controlling outbreaks of infectious diseases such as pandemic influenza is a top public health priority. We describe EpiSimdemics - a scalable parallel algorithm to simulate the spread of contagion in large, realistic social contact networks using individual-based models. EpiSimdemics is an interaction-based simulation of a certain class of stochastic reaction-diffusion processes. Straightforward simulations of such process do not scale well, limiting the use of individual-based models to very small populations. EpiSimdemics is specifically designed to scale to social networks with 100 million individuals. The scaling is obtained by exploiting the semantics of disease evolution and disease propagation in large networks. We evaluate an MPI-based parallel implementation of EpiSimdemics on a mid-sized HPC system, demonstrating that EpiSimdemics scales well. EpiSimdemics has been used in numerous sponsor defined case studies targeted at policy planning and course of action analysis, demonstrating the usefulness of EpiSimdemics in practical situations.
    Proceedings of the ACM/IEEE Conference on High Performance Computing, SC 2008, November 15-21, 2008, Austin, Texas, USA; 01/2008
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