3,248 research outputs found
Prediction and predictability of global epidemics: the role of the airline transportation network
The systematic study of large-scale networks has unveiled the ubiquitous
presence of connectivity patterns characterized by large scale heterogeneities
and unbounded statistical fluctuations. These features affect dramatically the
behavior of the diffusion processes occurring on networks, determining the
ensuing statistical properties of their evolution pattern and dynamics. In this
paper, we investigate the role of the large scale properties of the airline
transportation network in determining the global evolution of emerging disease.
We present a stochastic computational framework for the forecast of global
epidemics that considers the complete world-wide air travel infrastructure
complemented with census population data. We address two basic issues in global
epidemic modeling: i) We study the role of the large scale properties of the
airline transportation network in determining the global diffusion pattern of
emerging diseases; ii) We evaluate the reliability of forecasts and outbreak
scenarios with respect to the intrinsic stochasticity of disease transmission
and traffic flows. In order to address these issues we define a set of novel
quantitative measures able to characterize the level of heterogeneity and
predictability of the epidemic pattern. These measures may be used for the
analysis of containment policies and epidemic risk assessment.Comment: 20 pages, 5 figure
On Understanding Catastrophe — The Case of Highly Severe Influenza-Like Illness
Computational epidemiology is a form of spatiotemporal
reasoning in which social link structures
are employed, and spatially explicit models
are specified and executed. We point to issues thus
far addressed neither by engineers, nor scientists, in
the light of a use case focusing on catastrophic scenarios
that assume the emergence of a highly unlikely
but lethal and contagious strain of influenza.
Our conclusion is that important perspectives are
missing when dealing with policy issues resulting
from scenario execution and analyses in computational
epidemiology
The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale
<p>Abstract</p> <p>Background</p> <p>Computational models play an increasingly important role in the assessment and control of public health crises, as demonstrated during the 2009 H1N1 influenza pandemic. Much research has been done in recent years in the development of sophisticated data-driven models for realistic computer-based simulations of infectious disease spreading. However, only a few computational tools are presently available for assessing scenarios, predicting epidemic evolutions, and managing health emergencies that can benefit a broad audience of users including policy makers and health institutions.</p> <p>Results</p> <p>We present "GLEaMviz", a publicly available software system that simulates the spread of emerging human-to-human infectious diseases across the world. The GLEaMviz tool comprises three components: the client application, the proxy middleware, and the simulation engine. The latter two components constitute the GLEaMviz server. The simulation engine leverages on the Global Epidemic and Mobility (GLEaM) framework, a stochastic computational scheme that integrates worldwide high-resolution demographic and mobility data to simulate disease spread on the global scale. The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. The output is a dynamic map and a corresponding set of charts that quantitatively describe the geo-temporal evolution of the disease. The software is designed as a client-server system. The multi-platform client, which can be installed on the user's local machine, is used to set up simulations that will be executed on the server, thus avoiding specific requirements for large computational capabilities on the user side.</p> <p>Conclusions</p> <p>The user-friendly graphical interface of the GLEaMviz tool, along with its high level of detail and the realism of its embedded modeling approach, opens up the platform to simulate realistic epidemic scenarios. These features make the GLEaMviz computational tool a convenient teaching/training tool as well as a first step toward the development of a computational tool aimed at facilitating the use and exploitation of computational models for the policy making and scenario analysis of infectious disease outbreaks.</p
An Epidemiology of Information: Data Mining the 1918 Influenza Pandemic
An Epidemiology of Information: Data Mining the 1918 Influenza Pandemic seeks to harness the power of data mining techniques with the interpretive analytics of the humanities and social sciences to understand how newspapers shaped public opinion and represented authoritative knowledge during this deadly pandemic. This project makes use of the more than 100 newspaper titles for 1918 available from Chronicling America at the United States Library of Congress and the Peel’s Prairie Provinces collection at the University of Alberta Library. The application of algorithmic techniques enables the domain expert to systematically explore a broad repository of data and identify qualitative features of the pandemic in the small scale as well as the genealogy of information flow in the large scale. This research can provide methods for understanding the spread of information and the flow of disease in other societies facing the threat of pandemics
Text-based Spatial and Temporal Visualizations and their Applications in Visual Analytics
Textual labels are an essential part of most visualizations used in practice. However, these textual labels are mainly used to annotate other visualizations rather than being a central part of the visualization. Visualization researchers in areas like cartography and geovisualization have studied the combination of graphical features and textual labels to generate map based visualizations, but textual labels alone are not the primary focus in these representations. The idea of using symbols in visual representations and their interpretation as a quantity is gaining more traction. These types of representations are not only aesthetically appealing but also present new possibilities of encoding data. Such scenarios regularly arise while designing visual representations, where designers have to investigate feasibility of encoding information using symbols alone especially textual labels but the lack of readily available automated tools, and design guidelines makes it prohibitively expensive to experiment with such visualization designs. In order to address such challenges, this thesis presents the design and development of visual representations consisting entirely of text. These visual representations open up the possibility of encoding different types of spatial and temporal datasets. We report our results through two novel visualizations: typographic maps and text-based TextRiver visualization. Typographic maps merge text and spatial data into a visual representation where text alone forms the graphical features, mimicking the practices of human map makers. We also introduce methods to combine our automatic typographic maps technique with spatial datasets to generate thema-typographic maps where the properties of individual characters in the map are modified based on the underlying spatial data. Our TextRiver visualization is composed of collection of stream-like shapes consisting entirely of text where each stream represents thematic strength variations over time within a corpus. Such visualization enables additional ways to encode information contained in temporal datasets by modifying text attributes. We also conducted a usability evaluation to assess the potential value of our text-based TextRiver design
Estimation of COVID-19 spread curves integrating global data and borrowing information
Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to
global health. The rapid spread of the virus has created pandemic, and
countries all over the world are struggling with a surge in COVID-19 infected
cases. There are no drugs or other therapeutics approved by the US Food and
Drug Administration to prevent or treat COVID-19: information on the disease is
very limited and scattered even if it exists. This motivates the use of data
integration, combining data from diverse sources and eliciting useful
information with a unified view of them. In this paper, we propose a Bayesian
hierarchical model that integrates global data for real-time prediction of
infection trajectory for multiple countries. Because the proposed model takes
advantage of borrowing information across multiple countries, it outperforms an
existing individual country-based model. As fully Bayesian way has been
adopted, the model provides a powerful predictive tool endowed with uncertainty
quantification. Additionally, a joint variable selection technique has been
integrated into the proposed modeling scheme, which aimed to identify possible
country-level risk factors for severe disease due to COVID-19
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