Guiding Health Care Policy through Applied Public Health Modeling and Simulation

The risk of a widespread epidemic is a primary public health concern with implications for healthcare providers and organizations. Modeling and simulation techniques have been successfully applied at the national level to set governmental polices and mitigation strategies through simulation-based predictions. Existing research in this field has been non-uniform in its coverage of local systems and region-specific findings. New collaborations between on the ground providers and modeling groups are required for successful simulation-based experimentation of region-specific health systems. These proposed collaborations are expected to contribute high-quality sub-population datasets to be used in experiments at the national level and allow for the reuse of existing disease models and simulation infrastructure in support of regional predictive experimentation

Extending the 5S Framework of Digital Libraries to support Complex Objects, Superimposed Information, and Content-Based Image Retrieval Services

Advanced services in digital libraries (DLs) have been developed and widely used to address the required capabilities of an assortment of systems as DLs expand into diverse application domains. These systems may require support for images (e.g., Content-Based Image Retrieval), Complex (information) Objects, and use of content at fine grain (e.g., Superimposed Information). Due to the lack of consensus on precise theoretical definitions for those services, implementation efforts often involve ad hoc development, leading to duplication and interoperability problems. This article presents a methodology to address those problems by extending a precisely specified minimal digital library (in the 5S framework) with formal definitions of aforementioned services. The theoretical extensions of digital library functionality presented here are reinforced with practical case studies as well as scenarios for the individual and integrative use of services to balance theory and practice. This methodology has implications that other advanced services can be continuously integrated into our current extended framework whenever they are identified. The theoretical definitions and case study we present may impact future development efforts and a wide range of digital library researchers, designers, and developers

Mining Mobile Datasets to Enable the Fine-Grained Stochastic Simulation of Ebola Diffusion

The emergence of Ebola in West Africa is of worldwide public health concern. Successful miti- gation of epidemics requires coordinated, well-planned intervention strategies that are specific to the pathogen, transmission modality, population, and available resources. Modeling and sim- ulation in the field of computational epidemiology provides predictions of expected outcomes that are used by public policy planners in setting response strategies. Developing up to date models of population structures, daily activities, and movement has proven challenging for developing countries due to limited governmental resources. Recent collaborations (in 2012 and 2014) with telecom providers have given public health researchers access to Big Data needed to build high-fidelity models. Researchers now have access to billions of anonymized, detailed call data records (CDR) of mobile devices for several West African countries. In addition to official census records, these CDR datasets provide insights into the actual population locations, densities, movement, travel patterns, and migration in hard to reach areas. These datasets allow for the construction of population, activity, and movement models. For the first time, these models provide computational support of health related decision making in these developing areas (via simulation-based studies). New models, datasets, and simulation software were produced to assist in mitigating the continuing outbreak of Ebola. Existing models of disease characteristics, propagation, and progression were updated for the current circulating strain of Ebola. The simulation process required the interactions of multi-scale models, including viral loads (at the cellular level), disease progression (at the individual person level), disease propagation (at the workplace and family level), societal changes in migration and travel movements (at the population level), and mitigating interventions (at the abstract governmental policy level). The predictive results from this system were validated against results from the CDC\u27s high-level predictions

The utilization of appropriate osteoporosis medications improves following a multifaceted educational intervention: the Canadian quality circle project (CQC)

<p>Abstract</p> <p>Background</p> <p>Osteoporosis is a serious but treatable condition. However, appropriate therapy utilization of the disease remains suboptimal. Thus, the objective of the study was to change physicians' therapy administration behavior in accordance with the Osteoporosis Canada 2002 guidelines.</p> <p>Methods</p> <p>The Project was a two year cohort study that consisted of five Quality Circle (QC) phases that included: 1) Training & Baseline Data Collection, 2) First Educational Intervention & First Follow-Up Data Collection 3) First Strategy Implementation Session, 4) Final Educational Intervention & Final Follow-up Data Collection, and 5) Final Strategy Implementation Session. A total of 340 family physicians formed 34 QCs and participated in the study. Physicians evaluated a total of 8376, 7354 and 3673 randomly selected patient charts at baseline, follow-up #1 and the final follow-up, respectively. Patients were divided into three groups; the high-risk, low-risk, and low-risk without fracture groups. The generalized estimating equations technique was utilized to model the change over time of whether physicians</p> <p>Results</p> <p>The odds of appropriate therapy was 1.29 (95% CI: 1.13, 1.46), and 1.41 (95% CI: 1.20, 1.66) in the high risk group, 1.15 (95% CI: 0.97, 1.36), and 1.16 (95% CI: 0.93, 1.44) in the low risk group, and 1.20 (95% CI: 1.01, 1.43), and 1.23 (95% CI: 0.97, 1.55) in the low risk group without fractures at follow-up #1 and the final follow-up, respectively.</p> <p>Conclusion</p> <p>QCs methodology was successful in increasing physicians' appropriate use of osteoporosis medications in accordance with Osteoporosis Canada guidelines.</p

Utilization and Refinement of Standard Curation Models

The OAIS and Curation Lifecycle Model provide widely accepted models for curation workflows. However, primary and scientific research often produces content in a manner incompatible with the lack of emphasis these models place on integrating curation-supporting activities in early stages within a scientific workflow. Pre-ingest modules are needed in both models to enable curation of complex, domainspecific content during generation processes

Very Large Digital Libraries

Digital libraries are systems that store large quantities of diverse content. They are well suited to store, index, manage, and preserve the information produced and consumed by big data efforts

An Interactive Visualization Tool Built Using Google Maps

PURPOSE: Geographic visualizations can be a powerful tool to help health researchers and decision-makers to take measures during catastrophic and epidemic events like Ebola. The project is a web-based geo-visualization tool to visualize and compare the spread of Ebola in the West African countries Ivory Coast and Senegal. PROCEDURES: The tool is built using Google Maps JavaScript API. This is an interactive tool that helps in visualizing time series geospatial information. In this project the D4D data sets are used to depict the spread of Ebola. The project uses the data from Orange telecom; which gave access to its data, containing 300,000 records of anonymous, random users. The tool is built using the technologies Java, JavaScript, Java Servlets, JSP, HTML, and CSS. Heat maps are built for the time series data for a period of 150 days to discover the places where Ebola has most spread in number. OUTCOME: The tool was built to successfully visualize and compare the spread of the Ebola using heat maps. We are also using other ways to better visualize the data. Furthermore, the tool can also be used to visualize any data set containing geospatial information. IMPACT: The application could be a powerful tool that can help healthcare researchers and decision-makers in building mitigation strategies

Curricular Initiative Regarding a Minor in Data Science

Over the past several months faculty in statistics and computing & information systems have met to develop the framework for a new minor in Data Science. We will discuss our progress on this curricular initiative and elicit feedback

New forms of cultural nationalism? American and British Indians in the Trump and Brexit Twittersphere

Diaspora networks are one of the key, but often invisible, drivers in reinforcing long-distance nationalism towards the ‘homeland’ but simultaneously construct nationalist myths within their countries of residence. This article examines Indian diaspora supporters of Brexit and Trump in the United Kingdom and the United States who promote exclusionary nationalist imaginaries. Combining quantitative and qualitative approaches, it analyses British Indian and Indian American users that circulate radical right narratives within the Brexit and Trump Twittersphere. This article finds that these users express issues of concern pertinent to the radical right—for example, Islam and Muslims and the left-oriented political and media establishment—by employing civic nationalist discourse that promotes cultural nationalism. It sheds light on digital practices among diaspora actors who participate in the reinvigoration of exclusionary nationalist imaginaries of the Anglo-Western radical right

An Extensible Digital Library Service to Support Network Science

Network science research aims to understand the underlying properties of complex networks. Large-scale modeling and simulation is the core of network science research. Existing systems take a long time to run large network science experiments with high performance computing resources. Scientific data management systems currently lack the performance efficiency needed to support this type of computation and data-intensive research. Memoization provides the ability to index, archive, and reuse frequently requested and expensive to re-compute datasets. In this paper, we describe a domain independent memoization service to increase the computational execution process performance within cyberinfrastructure-based systems. We propose an extensible memoization framework for the computational and simulation network science domains that is built on top of well-defined metadata objects. We present extensible concepts, discuss the proposed algorithm and framework architecture, and examine the flexible nature of the framework. The framework was utilized as a part of the cyberinfrastructure-based digital library (DL). Our experimental results indicate an increase in the efficiency of the system and recommendation of the service inclusion in scientific DL