Browsing by Author "Barrett, Christopher L."

Now showing 1 - 18 of 18
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    Analysis system using brokers that access information sources
    (United States Patent and Trademark Office, 2018-01-16)
    Systems, methods, and computer-readable media for generating a data set are provided. One method includes generating a data set based on input data using a plurality of brokers. The method further includes receiving a request from a user and determining whether the request can be fulfilled using data currently in the data set. When the request can be fulfilled using data currently in the data set, the data is accessed using broker(s) configured to provide access to data within the data set. When the request cannot be fulfilled using data currently in the data set, at least one new broker is spawned using existing broker(s) and additional data needed to fulfill the request is added to the data set using the new broker. The method further includes generating a response to the request using one or more of the plurality of brokers.
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    Capacity Characterization of Multi-Hop Wireless Networks- A Cross Layer Approach
    Chafekar, Deepti Ramesh (Virginia Tech, 2009-03-11)
    A fundamental problem in multi-hop wireless networks is to estimate their throughout capacity. The problem can be informally stated as follows: given a multi-hop wireless network and a set of source destination pairs, determine the maximum rate r at which data can be transmitted between each source destination pair. Estimating the capacity of a multi-hop wireless network is practically useful --- it yields insights into the fundamental performance limits of the wireless network and at the same time aids the development of protocols that can utilize the network close to this limit. A goal of this dissertation is to develop rigorous mathematical foundations to compute the capacity of any given multi-hop wireless network with known source-destination pairs. An important factor that affects the capacity of multi-hop wireless networks is radio interference. As a result, researchers have proposed increasingly realistic interference models that aim to capture the physical characteristics of radio signals. Some of the commonly used simple models that capture radio interference are based on geometric disk-graphs. The simplicity of these models facilitate the development of provable and often conceptually simple methods for estimating the capacity of wireless networks. A potential weakness of this class of models is that they oversimplify the physical process by assuming that the signal ends abruptly at the boundary of a geometric region (a disk for omni-directional antennas). A more sophisticated interference model is the physical interference model, also known as the Signal to Interference Plus Noise Ratio (SINR) model. This model is more realistic than disk-graph models as it captures the effects of signal fading and ambient noise. This work considers both disk-graph and SINR interference models. In addition to radio interference, the throughput capacity of a multi-hop wireless network also depends on other factors, including the specific paths selected to route the packets between the source destination pairs (routing), the time at which packets are transmitted (scheduling), the power with which nodes transmit (power control) and the rate at which packets are injected (rate control). In this dissertation, we consider three different problems related to estimating network capacity. We propose an algorithmic approach for solving these problems. We first consider the problem of maximizing throughput with the SINR interference model by jointly considering the effects of routing and scheduling constraints. Second, we consider the problem of maximizing throughput by performing adaptive power control, scheduling and routing for disk-graph interference models. Finally, we examine the problem of minimizing end-to-end latency by performing joint routing, scheduling and power control using the SINR interference model. Recent results have shown that traditional layered networking principles lead to inefficient utilization of resources in multi-hop wireless networks. Motivated by these observations, recent papers have begun investigating cross-layer design approaches. Although our work does not develop new cross-layered protocols, it yields new insights that could contribute to the development of such protocols in the future. Our approach for solving these multi-objective optimization problems is based on combining mathematical programming with randomized rounding to obtain polynomial time approximation algorithms with provable worst case performance ratios. For the problems considered in this work, our results provide the best analytical performance guarantees currently known in the literature. We complement our rigorous theoretical and algorithmic analysis with simulation-based experimental analysis. Our experimental results help us understand the limitations of our approach and assist in identifying certain parameters for improving the performance of our techniques.
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    A Cognitively Inspired Architecture for Wireless Sensor Networks: A Web Service Oriented Middleware for a Traffic Monitoring System
    Tupe, Sameer Vijay (Virginia Tech, 2006-06-08)
    We describe CoSMo, a Cognitively Inspired Service and Model Architecture for situational awareness and monitoring of vehicular traffic in urban transportation systems using a network of wireless sensors. The system architecture combines (i) a cognitively inspired internal representation for analyzing and answering queries concerning the observed system and (ii) a service oriented architecture that facilitates interaction among individual modules, of the internal representation, the observed system and the user. The cognitively inspired model architecture allows one to effectively respond to deductive as well as inductive queries by combining simulation based dynamic models with traditional relational databases. On the other hand the service oriented design of interaction allows one to build flexible, extensible and scalable systems that can be deployed in practical settings. To illustrate our concepts and the novel features of our architecture, we have recently completed a prototype implementation of CoSMo. The prototype illustrates advantages of our approach over other traditional approaches for designing scalable software for situational awareness in large complex systems. The basic architecture and its prototype implementation are generic and can be applied for monitoring other complex systems. CoSMo's architecture has a number of features that distinguish cognitive systems. This includes: dynamic internal models of the observed system, inductive and deductive learning and reasoning, perception, memory and adaptation. This thesis describes the service oriented model and the associated prototype implementation. Two important contributions of this thesis include the following: The Generic Service Architecture - CoSMo's service architecture is generic and can be applied to many other application domains without much change in underlying infrastructure. Integration of emerging web technologies - Use of Web Services, UPnP, UDDI and many other emerging technologies have taken CoSMo beyond a prototype implementation and towards a real production system.
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    Cognitively-inspired Architecture for Wireless Sensor Networks: A Model Driven Approach for Data Integration in a Traffic Monitoring System
    Phalak, Kashmira (Virginia Tech, 2006-06-08)
    We describe CoSMo, a Cognitively Inspired Service and Model Architecture for situational awareness and monitoring of vehicular traffic in urban transportation systems using a network of wireless sensors. The system architecture combines (i) a cognitively inspired internal representation for analyzing and answering queries concerning the observed system and (ii) a service oriented architecture that facilitates interaction among individual modules, of the internal representation, the observed system and the user. The cognitively inspired model architecture allows effective deductive as well as inductive reasoning by combining simulation based dynamic models for planning with traditional relational databases for knowledge and data representation. On the other hand the service oriented design of interaction allows one to build flexible, extensible and scalable systems that can be deployed in practical settings. To illustrate our concepts and the novel features of our architecture, we have recently completed a prototype implementation of CoSMo. The prototype illustrates advantages of our approach over other traditional approaches for designing scalable software for situational awareness in large complex systems. The basic architecture and its prototype implementation are generic and can be applied for monitoring other complex systems. This thesis describes the design of cognitively-inspired model architecture and its corresponding prototype. Two important contributions include the following: • The cognitively-inspired architecture: In contrast to earlier work in model driven architecture, CoSMo contains a number of cognitively inspired features, including perception, memory and learning. Apart from illustrating interesting trade-offs between computational cost (e.g. access time, memory), and correctness available to a user, it also allows users specified deductive and inductive queries. • Distributed Data Integration and Fusion: In keeping with the cognitively-inspired model-driven approach, the system allows for an efficient data fusion from heterogeneous sensors, simulation based dynamic models and databases that are continually updated with real world and simulated data. It is capable of supporting a rich class of queries.
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    Comparing Effectiveness of Top-Down and Bottom-Up Strategies in Containing Influenza
    Marathe, Achla; Lewis, Bryan L.; Barrett, Christopher L.; Chen, Jiangzhuo; Marathe, Madhav V.; Eubank, Stephen; Ma, Yifei (Public Library of Science, 2011-09-22)
    This research compares the performance of bottom-up, self-motivated behavioral interventions with top-down interventions targeted at controlling an “Influenza-like-illness”. Both types of interventions use a variant of the ring strategy. In the first case, when the fraction of a person's direct contacts who are diagnosed exceeds a threshold, that person decides to seek prophylaxis, e.g. vaccine or antivirals; in the second case, we consider two intervention protocols, denoted Block and School: when a fraction of people who are diagnosed in a Census Block (resp., School) exceeds the threshold, prophylax the entire Block (resp., School). Results show that the bottom-up strategy outperforms the top-down strategies under our parameter settings. Even in situations where the Block strategy reduces the overall attack rate well, it incurs a much higher cost. These findings lend credence to the notion that if people used antivirals effectively, making them available quickly on demand to private citizens could be a very effective way to control an outbreak.
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    Complex situation analysis system that generates a social contact network, uses edge brokers and service brokers, and dynamically adds brokers
    (United States Patent and Trademark Office, 2013-04-16)
    A system for generating a representation of a situation is disclosed. The system comprises one or more computer-readable media including computer-executable instructions that are executable by one or more processors to implement a method of generating a representation of a situation. The method comprises receiving input data regarding a target population. The method further comprises constructing a synthetic data set including a synthetic population based on the input data. The synthetic population includes a plurality of synthetic entities. Each synthetic entity has a one-to-one correspondence with an entity in the target population. Each synthetic entity is assigned one or more attributes based on information included in the input data. The method further comprises receiving activity data for a plurality of entities in the target population.
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    Complex situation analysis system that spawns/creates new brokers using existing brokers as needed to respond to requests for data
    (United States Patent and Trademark Office, 2014-03-25)
    Systems, methods, and computer-readable media for generating a data set are provided. One method includes generating a data set based on input data using a plurality of brokers. The method further includes receiving a request from a user and determining whether the request can be fulfilled using data currently in the data set. When the request can be fulfilled using data currently in the data set, the data is accessed using broker(s) configured to provide access to data within the data set. When the request cannot be fulfilled using data currently in the data set, at least one new broker is spawned using existing broker(s) and additional data needed to fulfill the request is added to the data set using the new broker. The method further includes generating a response to the request using one or more of the plurality of brokers.
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    Complex situation analysis system using a plurality of brokers that control access to information sources
    (United States Patent and Trademark Office, 2016-06-14)
    Systems, methods, and computer-readable media for generating a data set are provided. One method includes generating a data set based on input data using a plurality of brokers. The method further includes receiving a request from a user and determining whether the request can be fulfilled using data currently in the data set. When the request can be fulfilled using data currently in the data set, the data is accessed using broker(s) configured to provide access to data within the data set. When the request cannot be fulfilled using data currently in the data set, at least one new broker is spawned using existing broker(s) and additional data needed to fulfill the request is added to the data set using the new broker. The method further includes generating a response to the request using one or more of the plurality of brokers.
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    Data Integration Methodologies and Services for Evaluation and Forecasting of Epidemics
    Deodhar, Suruchi (Virginia Tech, 2016-05-31)
    Most epidemiological systems described in the literature are built for evaluation and analysis of specific diseases, such as Influenza-like-illness. The modeling environments that support these systems are implemented for specific diseases and epidemiological models. Hence they are not reusable or extendable. This thesis focuses on the design and development of an integrated analytical environment with flexible data integration methodologies and multi-level web services for evaluation and forecasting of various epidemics in different regions of the world. The environment supports analysis of epidemics based on any combination of disease, surveillance sources, epidemiological models, geographic regions and demographic factors. The environment also supports evaluation and forecasting of epidemics when various policy-level and behavioral interventions are applied, that may inhibit the spread of an epidemic. First, we describe data integration methodologies and schema design, for flexible experiment design, storage and query retrieval mechanisms related to large scale epidemic data. We describe novel techniques for data transformation, optimization, pre-computation and automation that enable flexibility, extendibility and efficiency required in different categories of query processing. Second, we describe the design and engineering of adaptable middleware platforms based on service-oriented paradigms for interactive workflow, communication, and decoupled integration. This supports large-scale multi-user applications with provision for online analysis of interventions as well as analytical processing of forecast computations. Using a service-oriented architecture, we have provided a platform-as-a-service representation for evaluation and forecasting of epidemics. We demonstrate the applicability of our integrated environment through development of the applications, DISIMS and EpiCaster. DISIMS is an interactive web-based system for evaluating the effects of dynamic intervention strategies on epidemic propagation. EpiCaster is a situation assessment and forecasting tool for projecting the state of evolving epidemics such as flu and Ebola in different regions of the world. We discuss how our platform uses existing technologies to solve a novel problem in epidemiology, and provides a unique solution on which different applications can be built for analyzing epidemic containment strategies.
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    Detail in network models of epidemiology: are we there yet?
    Eubank, Stephen; Barrett, Christopher L.; Beckman, Richard J.; Bisset, Keith R.; Durbeck, L.; Kuhlman, Christopher J.; Lewis, Bryan L.; Marathe, Achla; Marathe, Madhav V.; Stretz, P. (Taylor & Francis, 2010)
    Network models of infectious disease epidemiology can potentially provide insight into how to tailor control strategies for specific regions, but only if the network adequately reflects the structure of the region’s contact network. Typically, the network is produced by models that incorporate details about human interactions. Each detail added renders the models more complicated and more difficult to calibrate, but also more faithful to the actual contact network structure. We propose a statistical test to determine when sufficient detail has been added to the models and demonstrate its application to the models used to create a synthetic population and contact network for the USA.
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    Economic and Social Impact of Influenza Mitigation Strategies by Demographic Class
    Barrett, Christopher L.; Bisset, Keith R.; Leidig, Jonathan; Marathe, Achla; Marathe, Madhav V. (Elsevier, 2011-03-01)
    Background—We aim to determine the economic and social impact of typical interventions proposed by the public health officials and preventive behavioral changes adopted by the private citizens in the event of a “flu-like” epidemic. Method—We apply an individual-based simulation model to the New River Valley area of Virginia for addressing this critical problem. The economic costs include not only the loss in productivity due to sickness but also the indirect cost incurred through disease avoidance and caring for dependents. Results—The results show that the most important factor responsible for preventing income loss is the modification of individual behavior; it drops the total income loss by 62% compared to the base case. The next most important factor is the closure of schools which reduces the total income loss by another 40%. Conclusions—The preventive behavior of the private citizens is the most important factor in controlling the epidemic.
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    Forecasting influenza activity using machine-learned mobility map
    Venkatramanan, Srinivasan; Sadilek, Adam; Fadikar, Arindam; Barrett, Christopher L.; Biggerstaff, Matthew; Chen, Jiangzhuo; Dotiwalla, Xerxes; Eastham, Paul; Gipson, Bryant; Higdon, Dave; Kucuktunc, Onur; Lieber, Allison; Lewis, Bryan L.; Reynolds, Zane; Vullikanti, Anil Kumar S.; Wang, Lijing; Marathe, Madhav V. (2021-02-09)
    Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model's performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model's ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology. Human mobility plays a central role in the spread of infectious diseases and can help in forecasting incidence. Here the authors show a comparison of multiple mobility benchmarks in forecasting influenza, and demonstrate the value of a machine-learned mobility map with global coverage at multiple spatial scales.
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    Human Initiated Cascading Failures in Societal Infrastructures
    Barrett, Christopher L.; Channakeshava, Karthik; Huang, Fei; Marathe, Achla; Marathe, Madhav V.; Pei, Guanhong; Saha, Sudip; Vullikanti, Anil Kumar S.; Kim, Junwhan; Subbiah, Balaaji S. P. (Public Library of Science, 2012-10-31)
    In this paper, we conduct a systematic study of human-initiated cascading failures in three critical inter-dependent societal infrastructures due to behavioral adaptations in response to a crisis. We focus on three closely coupled socio-technical networks here: (i) cellular and mesh networks, (ii) transportation networks and (iii) mobile call networks. In crises, changes in individual behaviors lead to altered travel, activity and calling patterns, which influence the transport network and the loads on wireless networks. The interaction between these systems and their co-evolution poses significant technical challenges for representing and reasoning about these systems. In contrast to system dynamics models for studying these interacting infrastructures, we develop interaction-based models in which individuals and infrastructure elements are represented in detail and are placed in a common geographic coordinate system. Using the detailed representation, we study the impact of a chemical plume that has been released in a densely populated urban region. Authorities order evacuation of the affected area, and this leads to individual behavioral adaptation wherein individuals drop their scheduled activities and drive to home or pre-specified evacuation shelters as appropriate. They also revise their calling behavior to communicate and coordinate among family members. These two behavioral adaptations cause flash-congestion in the urban transport network and the wireless network. The problem is exacerbated with a few, already occurring, road closures. We analyze how extended periods of unanticipated road congestion can result in failure of infrastructures, starting with the servicing base stations in the congested area. A sensitivity analysis on the compliance rate of evacuees shows non-intuitive effect on the spatial distribution of people and on the loading of the base stations. For example, an evacuation compliance rate of 70% results in higher number of overloaded base stations than the evacuation compliance rate of 90%.
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    in silico Public Health: The Essential Role of Highly Detailed Simulations in Support of Public Health Decision-Making
    Lewis, Bryan L. (Virginia Tech, 2011-01-19)
    Public Health requires a trans-disciplinary approach to tackle the breadth and depth of the issues it faces. Public health decisions are reached through the compilation of multiple data sources and their thoughtful synthesis. The complexity and importance of these decisions necessitates a variety of approaches, with simulations increasingly being relied upon. This dissertation describes several research efforts that demonstrate the utility of highly detailed simulations in public health decision-making. Simulations are frequently used to represent dynamic processes and to synthesize data to predict future outcomes, which can be used in cost-benefit and course of action analyses. The threat of pandemic influenza and its subsequent arrival prompted many simulation-based studies. This dissertation details several such studies conducted at the federal policy level. Their use for planning and the rapid response to the unfolding crisis demonstrates the integration of highly detailed simulations into the public health decision-making process. Most analytic methods developed by public health practitioners rely on historical data sources, but are intended to be broadly applicable. Oftentimes this data is limited or incomplete. This dissertation describes the use of highly detailed simulations to evaluate the performance of outbreak detection algorithms. By creating methods that generate realistic and configurable synthetic data, the reliance on these historical samples can be reduced, thus facilitating the development and improvement of methods for public health practice. The process of decision-making itself can significantly influence the decisions reached. Many fields use simulations to train and evaluate, however, public health has yet to fully adopt these approaches. This dissertation details the construction of highly detailed synthetic data that was used to build an interactive environment designed to evaluate the decision-making processes for pertussis control. The realistic data sets provide sufficient face validity to experienced public health practitioners, creating a natural and effective medium for training and evaluation purposes. Advances in high-performance computing, information sciences, computer science, and epidemiology are enabling increasing innovation in the application of simulations. This dissertation illustrates several applications of simulations to relevant public health practices and strongly argues that highly detailed simulations have an essential role to play in Public Health decision-making.
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    Modeling targeted layered containment of an influenza pandemic in the United States
    Halloran, Elizabeth M.; Ferguson, Neil M.; Eubank, Stephen; Longini, Ira M. Jr.; Cummings, Derek A. T.; Lewis, Bryan L.; Xu, Shufu; Fraser, Christophe; Vullikanti, Anil; Germann, Timothy C.; Wagener, Diane; Beckman, Richard J.; Kadau, Kai; Barrett, Christopher L.; Macken, Catherine A.; Burke, Donald S.; Cooley, Philip (NAS, 2008-03-25)
    Planning a response to an outbreak of a pandemic strain of influenza is a high public health priority. Three research groups using different individual-based, stochastic simulation models have examined the consequences of intervention strategies chosen in consultation with U.S. public health workers. The first goal is to simulate the effectiveness of a set of potentially feasible intervention strategies. Combinations called targeted layered containment (TLC) of influenza antiviral treatment and prophylaxis and nonpharmaceutical interventions of quarantine, isolation, school closure, community social distancing, and workplace social distancing are considered. The second goal is to examine the robustness of the results to model assumptions. The comparisons focus on a pandemic outbreak in a population similar to that of Chicago, with 8.6 million people. The simulations suggest that at the expected transmissibility of a pandemic strain, timely implementation of a combination of targeted household antiviral prophylaxis, and social distancing measures could substantially lower the illness attack rate before a highly efficacious vaccine could become available. Timely initiation of measures and school closure play important roles. Because of the current lack of data on which to base such models, further field research is recommended to learn more about the sources of transmission and the effectiveness of social distancing measures in reducing influenza transmission.
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    Modeling the Ebola Outbreak in West Africa, August 4th 2014 update
    Lewis, Bryan L.; Rivers, Caitlin; Eubank, Stephen; Marathe, Marathe; Barrett, Christopher L. (2014)
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    Modeling, Analysis and Comparison of Large Scale Social Contact Networks on Epidemic Studies
    Xia, Huadong (Virginia Tech, 2015-04-07)
    Social contact networks represent proximity relationships between individual agents. Such networks are useful in diverse applications, including epidemiology, wireless networking and urban resilience. The vertices of a social contact network represent individual agents (e.g. people). Time varying edges represent time varying proximity relationship. The networks are relational -- node and edge labels represent important demographic, spatial and temporal attributes. Synthesizing social contact networks that span large urban regions is challenging for several reasons including: spatial, temporal and relational variety of data sources, noisy and incomplete data, and privacy and confidentiality requirements. Moreover, the synthesized networks differ due to the data and methods used to synthesize them. This dissertation undertakes a systematic study of synthesizing urban scale social contact networks within the specific application context of computational epidemiology. It is motivated by three important questions: (i) How does one construct a realistic social contact network that is adaptable to different levels of data availability? (ii) How does one compare different versions of the network for a given region, and what are appropriate metrics when comparing the relational networks? (iii) When does a network have adequate structural details for the specific application we have. We study these questions by synthesizing three social contact networks for Delhi, India. Our case study suggests that we can iteratively improve the quality of a network by adapting to the best data sources available within a framework. The networks differ by the data and the models used. We carry out detailed comparative analyses of the networks. The analysis has three components: (i) structure analysis that compares the structural properties of the networks, (ii) dynamics analysis that compares the epidemic dynamics on these networks and (iii) policy analysis that compares the efficacy of various interventions. We have proposed a framework to systematically analyze how details in networks impact epidemic dynamics over these networks. The results suggest that a combination of multi-level metrics instead of any individual one should be used to compare two networks. We further investigate the sensitivity of these models. The study reveals the details necessary for particular class of control policies. Our methods are entirely general and can be applied to other areas of network science.
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    Simulating the Spread of Malaria: A Cellular Automaton Based Mathematical Model & A Prototype Software Implementation
    Merchant, Farid (Virginia Tech, 2007-02-05)
    Every year three million deaths are attributed to malaria, of which one-third are of children. Malaria is a vector-borne disease, where a mosquito acts as the vector that transmits the disease. In the last few years, computer simulation based models have been used effectively to study the vector population dynamics and control strategies of vector-borne diseases. Typically, these models use ordinary differential equations to simulate the spread of malaria. Although these models provide a powerful mechanism to study the spread of malaria, they have several shortcomings. The research in this thesis focuses on creating a simulation model based on the framework of cellular automata, which addresses many shortcomings of previous models. Cellular automata are dynamical systems, which are discrete in time and space. The implementation of the model proposed can easily be integrated with EpiSims/TRANSIMS. EpiSims is an epidemiological modeling tool for studying the spread of infectious diseases; it uses social contact network from TRANSIMS (A Transport Analysis and Simulation System). Simulation results from the prototype implementation showed qualitatively correct results for vector densities, diffusion and epidemiological curves.