2,413 research outputs found
Dynamics of Information Diffusion and Social Sensing
Statistical inference using social sensors is an area that has witnessed
remarkable progress and is relevant in applications including localizing events
for targeted advertising, marketing, localization of natural disasters and
predicting sentiment of investors in financial markets. This chapter presents a
tutorial description of four important aspects of sensing-based information
diffusion in social networks from a communications/signal processing
perspective. First, diffusion models for information exchange in large scale
social networks together with social sensing via social media networks such as
Twitter is considered. Second, Bayesian social learning models and risk averse
social learning is considered with applications in finance and online
reputation systems. Third, the principle of revealed preferences arising in
micro-economics theory is used to parse datasets to determine if social sensors
are utility maximizers and then determine their utility functions. Finally, the
interaction of social sensors with YouTube channel owners is studied using time
series analysis methods. All four topics are explained in the context of actual
experimental datasets from health networks, social media and psychological
experiments. Also, algorithms are given that exploit the above models to infer
underlying events based on social sensing. The overview, insights, models and
algorithms presented in this chapter stem from recent developments in network
science, economics and signal processing. At a deeper level, this chapter
considers mean field dynamics of networks, risk averse Bayesian social learning
filtering and quickest change detection, data incest in decision making over a
directed acyclic graph of social sensors, inverse optimization problems for
utility function estimation (revealed preferences) and statistical modeling of
interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112
2013 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics
URC Schedule and Abstract Book for the Fifth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics
Date: November 16-17, 2013Plenary Speaker: Mariel Vazquez, Associate Professor of Mathematics at San Francisco State UniversityFeatured Speaker: Andrew Liebhold, Research Entomologist for the USDA Forest Servic
Report on DIMACS Working Group Meeting: Mathematical Sciences Methods for the Study of Deliberate Releases of Biological Agents and their Consequences
55 pages, 1 article*Report on DIMACS Working Group Meeting: Mathematical Sciences Methods for the Study of Deliberate Releases of Biological Agents and their Consequences* (Castillo-Chavez, Carlos; Roberts, Fred S.) 55 page
Literature review: The vaccine supply chain
Vaccination is one of the most effective ways to prevent and/or control the outbreak of infectious diseases. This medical intervention also brings about many logistical questions. In the past years, the Operations Research/Operations Management community has shown a growing interest in the logistical aspects of vaccination. However, publications on vaccine logistics often focus on one specific logistical aspect. A broader framework is needed so that open research questions can be identified more easily and contributions are not overlooked.In this literature review, we combine the priorities of the World Health Organization for creating a flexible and robust vaccine supply chain with an Operations Research/Operations Management supply chain perspective. We propose a classification for the literature on vaccine logistics to structure this relatively new field, and identify promising research directions. We classify the literature into the following four components: (1) product, (2) production, (3) allocation, and (4) distribution. Within the supply chain classification, we analyze the decision problems for existing outbreaks versus sudden outbreaks and developing countries versus developed countries. We identify unique characteristics of the vaccine supply chain: high uncertainty in both supply and demand; misalignment of objectives and decentralized decision making between supplier, public health organization and end customer; complex political decisions concerning allocation and the crucial
Mathematical models for chemotaxis and their applications in self-organisation phenomena
Chemotaxis is a fundamental guidance mechanism of cells and organisms,
responsible for attracting microbes to food, embryonic cells into developing
tissues, immune cells to infection sites, animals towards potential mates, and
mathematicians into biology. The Patlak-Keller-Segel (PKS) system forms part of
the bedrock of mathematical biology, a go-to-choice for modellers and analysts
alike. For the former it is simple yet recapitulates numerous phenomena; the
latter are attracted to these rich dynamics. Here I review the adoption of PKS
systems when explaining self-organisation processes. I consider their
foundation, returning to the initial efforts of Patlak and Keller and Segel,
and briefly describe their patterning properties. Applications of PKS systems
are considered in their diverse areas, including microbiology, development,
immunology, cancer, ecology and crime. In each case a historical perspective is
provided on the evidence for chemotactic behaviour, followed by a review of
modelling efforts; a compendium of the models is included as an Appendix.
Finally, a half-serious/half-tongue-in-cheek model is developed to explain how
cliques form in academia. Assumptions in which scholars alter their research
line according to available problems leads to clustering of academics and the
formation of "hot" research topics.Comment: 35 pages, 8 figures, Submitted to Journal of Theoretical Biolog
Early Warning Analysis for Social Diffusion Events
There is considerable interest in developing predictive capabilities for
social diffusion processes, for instance to permit early identification of
emerging contentious situations, rapid detection of disease outbreaks, or
accurate forecasting of the ultimate reach of potentially viral ideas or
behaviors. This paper proposes a new approach to this predictive analytics
problem, in which analysis of meso-scale network dynamics is leveraged to
generate useful predictions for complex social phenomena. We begin by deriving
a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes
taking place over social networks with realistic topologies; this modeling
approach is inspired by recent work in biology demonstrating that S-HDS offer a
useful mathematical formalism with which to represent complex, multi-scale
biological network dynamics. We then perform formal stochastic reachability
analysis with this S-HDS model and conclude that the outcomes of social
diffusion processes may depend crucially upon the way the early dynamics of the
process interacts with the underlying network's community structure and
core-periphery structure. This theoretical finding provides the foundations for
developing a machine learning algorithm that enables accurate early warning
analysis for social diffusion events. The utility of the warning algorithm, and
the power of network-based predictive metrics, are demonstrated through an
empirical investigation of the propagation of political memes over social media
networks. Additionally, we illustrate the potential of the approach for
security informatics applications through case studies involving early warning
analysis of large-scale protests events and politically-motivated cyber
attacks
Construction and evaluation of epidemiologic simulation models for the within- and among-unit spread and control of infectious diseases of livestock and poultry
2012 Fall.Includes bibliographical references.Epidemiologic modeling is an increasingly common method of estimating the potential impact of outbreaks of highly contagious diseases, such as foot-and-mouth disease (FMD) and highly pathogenic avian influenza (HPAI), in populations of domesticated animals. Disease models are also used to inform policy decisions regarding disease control methods and outbreak response plans, to estimate the possible magnitude of an outbreak, and to estimate the resources needed for outbreak response. Although disease models are computationally sophisticated, the quality of the results of modeling studies depends on the quality and accuracy of the data on which they are based, and on the conceptual soundness and validity of the models themselves. For such models to be credibly applied, they should realistically represent the systems they are intended to reflect, should be based to as great an extent as possible on valid data, and should be subjected to careful and ongoing scrutiny. Two key steps in the evaluation of epidemiologic models are model verification and model validation. Verification is the demonstration that a computer-driven model is operating correctly, and conforms to its intended design. Validation refers to the process of determining how well a model corresponds to the system that it intended to represent. For a veterinary epidemiologic model, validation would address issues such as how well the model represents the dynamics of the disease in question in a population to which the model is applied, and how well the model represents the application of different measures for disease control. Among the steps that can be taken by epidemiologic modelers to facilitate the processes of model verification and validation are to clearly state the purpose, assumptions, and limitations of a model; to provide a detailed description of the conceptual model for use by everyone who might be tasked with evaluation of a model; document steps already taken to test the model; and thoroughly describe the data sources and the process used to produce model input parameters from data. The realistic representation of the dynamics of spread of disease within individual herds or flocks can have important implications for disease detection and surveillance, as well as for disease transmission between herds or flocks. We have developed a simulation model of within-unit (within-herd or within-flock) disease spread that operates at the level of the individual animal, and fully incorporates sources of individual-level variation such as variability in the durations of incubating and infectious periods, the stochastic nature of disease spread among individuals, and the effects of vaccination. We describe this stochastic model, along with the processes employed for verification and validation. The incorporation of this approach to modeling of within-unit disease dynamics into models of between-unit disease spread should improve the utility of these models for emergency preparedness and response planning by making it possible to assess the value of different approaches to disease detection and surveillance, in populations with or without some existing level of vaccine immunity. Models rely not only on realistic representations of the systems of interest, but also on valid and realistic information. For spatially explicit models of the spread and control of disease in populations of livestock and poultry, this means a heavy reliance upon valid spatial representations of the populations of interest, including such characteristics as the geographic locations of farms and their proximity to others in the population. In the United States, limited information regarding the locations of actual farm premises is available, and modeling work often makes use of artificially generated population datasets. In order to evaluate the accuracy and validity of the use of such artificially generated datasets, we compared the outcomes of mechanistic epidemiologic simulation models that were run using an empirical population dataset to those of models that made use of several different synthetic population datasets. Although we found generally good qualitative agreement among models run using various population datasets, the quantitative differences in model outcomes could be substantial. When quantitative outcomes from epidemiologic models are desired or required, care should be taken to adequately capture or describe the uncertainty in model-based outcomes due to the use of synthetic population datasets
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
Scalable visual analytics over voluminous spatiotemporal data
2018 Fall.Includes bibliographical references.Visualization is a critical part of modern data analytics. This is especially true of interactive and exploratory visual analytics, which encourages speedy discovery of trends, patterns, and connections in data by allowing analysts to rapidly change what data is displayed and how it is displayed. Unfortunately, the explosion of data production in recent years has led to problems of scale as storage, processing, querying, and visualization have struggled to keep pace with data volumes. Visualization of spatiotemporal data pose unique challenges, thanks in part to high-dimensionality in the input feature space, interactions between features, and the production of voluminous, high-resolution outputs. In this dissertation, we address challenges associated with supporting interactive, exploratory visualization of voluminous spatiotemporal datasets and underlying phenomena. This requires the visualization of millions of entities and changes to these entities as the spatiotemporal phenomena unfolds. The rendering and propagation of spatiotemporal phenomena must be both accurate and timely. Key contributions of this dissertation include: 1) the temporal and spatial coupling of spatially localized models to enable the visualization of phenomena at far greater geospatial scales; 2) the ability to directly compare and contrast diverging spatiotemporal outcomes that arise from multiple exploratory "what-if" queries; and 3) the computational framework required to support an interactive user experience in a heavily resource-constrained environment. We additionally provide support for collaborative and competitive exploration with multiple synchronized clients
2016 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics
Schedule and abstract book for the Eighth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics
Date: October 8-9, 2016Location: UT Conference Center, KnoxvillePlenary Speaker: Jorge X. Velasco Hernández, Universidad Nacional Autónoma de MéxicoFeatured Speaker: Judy Day, University of Tennessee, Knoxvill
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