Publications

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Results show that a time-series based classification may be possible. For the test cases considered, the correct model can be selected and the number of index case can be captured within {+-} {sigma} with 5-10 days of data. The low signal-to-noise ratio makes the classification difficult for small epidemics. The problem statement is: (1) Create Bayesian techniques to classify and characterize epidemics from a time-series of ICD-9 codes (will call this time-series a 'morbidity stream'); and (2) It is assumed the morbidity stream has already set off an alarm (through a Kalman filter anomaly detector) Starting with a set of putative diseases: Identify which disease or set of diseases 'fit the data best' and, Infer associated information about it, i.e. number of index cases, start time of the epidemic, spread rate, etc.

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We present a Bayesian approach for estimating transmission chains and rates in the Abakaliki smallpox epidemic of 1967. The epidemic affected 30 individuals in a community of 74; only the dates of appearance of symptoms were recorded. Our model assumes stochastic transmission of the infections over a social network. Distinct binomial random graphs model intra- and inter-compound social connections, while disease transmission over each link is treated as a Poisson process. Link probabilities and rate parameters are objects of inference. Dates of infection and recovery comprise the remaining unknowns. Distributions for smallpox incubation and recovery periods are obtained from historical data. Using Markov chain Monte Carlo, we explore the joint posterior distribution of the scalar parameters and provide an expected connectivity pattern for the social graph and infection pathway.

This report presents the results of our efforts to apply high-performance computing to entity-based simulations with a multi-use plugin for parallel computing. We use the term 'Entity-based simulation' to describe a class of simulation which includes both discrete event simulation and agent based simulation. What simulations of this class share, and what differs from more traditional models, is that the result sought is emergent from a large number of contributing entities. Logistic, economic and social simulations are members of this class where things or people are organized or self-organize to produce a solution. Entity-based problems never have an a priori ergodic principle that will greatly simplify calculations. Because the results of entity-based simulations can only be realized at scale, scalable computing is de rigueur for large problems. Having said that, the absence of a spatial organizing principal makes the decomposition of the problem onto processors problematic. In addition, practitioners in this domain commonly use the Java programming language which presents its own problems in a high-performance setting. The plugin we have developed, called the Parallel Particle Data Model, overcomes both of these obstacles and is now being used by two Sandia frameworks: the Decision Analysis Center, and the Seldon social simulation facility. While the ability to engage U.S.-sized problems is now available to the Decision Analysis Center, this plugin is central to the success of Seldon. Because Seldon relies on computationally intensive cognitive sub-models, this work is necessary to achieve the scale necessary for realistic results. With the recent upheavals in the financial markets, and the inscrutability of terrorist activity, this simulation domain will likely need a capability with ever greater fidelity. High-performance computing will play an important part in enabling that greater fidelity.

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Structured adaptive mesh refinement methods are being widely used for computer simulations of various physical phenomena. Parallel implementations potentially offer realistic simulations of complex three-dimensional applications. But achieving good scalability for large-scale applications is non-trivial. Performance is limited by the partitioner's ability to efficiently use the underlying parallel computer's resources. Designed on sound SAMR principles, Nature+Fable is a hybrid, dedicated SAMR partitioning tool that brings together the advantages of both domain-based and patch-based techniques while avoiding their drawbacks. But the original bi-level partitioning approach in Nature+Fable is insufficient as it for realistic applications regards frequently occurring bi-levels as ''impossible'' and fails. This document describes an improved bi-level partitioning algorithm that successfully copes with all possible bi-levels. The improved algorithm uses the original approach side-by-side with a new, complementing approach. By using a new, customized classification method, the improved algorithm switches automatically between the two approaches. This document describes the algorithms, discusses implementation issues, and presents experimental results. The improved version of Nature+Fable was found to be able to handle realistic applications and also to generate less imbalances, similar box count, but more communication as compared to the native, domain-based partitioner in the SAMR framework AMROC.

Structured adaptive mesh refinement methods are being widely used for computer simulations of various physical phenomena. Parallel implementations potentially offer realistic simulations of complex three-dimensional applications. But achieving good scalability for large-scale applications is non-trivial. Performance is limited by the partitioner's ability to efficiently use the underlying parallel computer's resources. Designed on sound SAMR principles, Nature+Fable is a hybrid, dedicated SAMR partitioning tool that brings together the advantages of both domain-based and patch-based techniques while avoiding their drawbacks. But the original bi-level partitioning approach in Nature+Fable is insufficient as it for realistic applications regards frequently occurring bi-levels as 'impossible' and fails. This document describes an improved bi-level partitioning algorithm that successfully copes with all possible hi-levels. The improved algorithm uses the original approach side-by-side with a new, complementing approach. By using a new, customized classification method, the improved algorithm switches automatically between the two approaches. This document describes the algorithms, discusses implementation issues, and presents experimental results. The improved version of Nature+Fable was found to be able to handle realistic applications and also to generate less imbalances, similar box count, but more communication as compared to the native, domain-based partitioner in the SAMR framework AMROC.

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