Strategy to Develop a Digital Public Health Observatory Integrating Business Intelligence and Visual Analytics

Author(s):  
Leidy Alexandra Lozano ◽  
Maria del Pilar Villamil
2021 ◽  
Author(s):  
Joseph Bullock ◽  
Carolina Cuesta-Lazaro ◽  
Arnau Quera-Bofarull ◽  
Anjali Katta ◽  
Katherine Hoffmann Pham ◽  
...  

AbstractThe spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. Non-pharmaceutical public health interventions can be used to mitigate transmission, and modeling efforts can provide crucial insights on the potential effectiveness of such interventions to help inform decision making processes. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements. The model, based on the JUNE open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. Furthermore, we present a visual analytics tool which allows decision makers to distill insights by comparing the results of different simulations and scenarios. Through simulating their effects on the epidemiological development of COVID-19, we evaluate several public health interventions ranging from increasing mask wearing compliance to the reopening of learning institutions. The development and testing of this approach focuses on the Cox’s Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings.


Prospectiva ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Germán Sánchez Torres ◽  
Maria del pilar Villamil ◽  
Andres Moreno ◽  
Eduard Avendaño

Resumen En este artículo, se presenta una estrategia para el desarrollo de observatorios de riesgo de desastres, integrando inteligencia empresarial y análisis visual. El análisis y la contextualización a nivel global se llevan a cabo desde la perspectiva de políticas, herramientas y literatura científica en el contexto de la reducción del riesgo de desastres. La estrategia integra la metodología Kimball para la construcción de proyectos de inteligencia empresarial (BI) y el marco de visualización de Tamara Munzner para fortalecer el proceso de toma de decisiones y hacerlo flexible y más rápido, satisfaciendo las necesidades de los usuarios empresariales. Considerar la gestión del riesgo de desastres como un proceso permite la identificación del punto de partida y los procesos que siguen, lo que resulta en un inicio rápido del ciclo de vida del proyecto. Además, incluir el marco de visualización, junto con la metodología de BI, facilita el desarrollo de herramientas de análisis para resolver los problemas del usuario final.


Author(s):  
Anton Ninkov ◽  
Kamran Sedig

This paper reports and describes VINCENT, a visual analytics system that is designed to help public health stakeholders (i.e., users) make sense of data from websites involved in the online debate about vaccines. VINCENT allows users to explore visualizations of data from a group of 37 vaccine-focused websites. These websites differ in their position on vaccines, topics of focus about vaccines, geographic location, and sentiment towards the efficacy and morality of vaccines, specific and general ones. By integrating webometrics, natural language processing of website text, data visualization, and human-data interaction, VINCENT helps users explore complex data that would be difficult to understand, and, if at all possible, to analyze without the aid of computational tools.The objectives of this paper are to explore A) the feasibility of developing a visual analytics system that integrates webometrics, natural language processing of website text, data visualization, and human-data interaction in a seamless manner; B) how a visual analytics system can help with the investigation of the online vaccine debate; and C) what needs to be taken into consideration when developing such a system. This paper demonstrates that visual analytics systems can integrate different computational techniques; that such systems can help with the exploration of public health online debates that are distributed across a set of websites; and that care should go into the design of the different components of such systems. 


2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Judy Shlay ◽  
Emily McCormick Kraus ◽  
Nicole Steffens ◽  
Noam H. Atzt ◽  
Arthur Davidson

ObjectiveTo describe a business intelligence system designed to reprocess and utilize an immunization information system’s (IIS) data to visualize, and track population trends in immunization coverage in an urban population.IntroductionIIS have effectively increased vaccination rates through targeted engagement and outreach, often with clinicians and patients. Little has been published around IIS use for generating meaningful population health measures. To leverage IIS data for sub-county population health measures, new tools are required to make IIS data easily accessed for this distinct use case.Human papillomavirus (HPV), the most common sexually transmitted infection in the United States, has a highly effective (97%) vaccine to prevent infection when administered to individuals 9-26 years old. According to the National Immunization Survey, only 47% of Colorado females 13-17 years had completed the HPV vaccine series in 2011. In 2012, Denver metropolitan health departments were awarded a three year grant to support the Alliance for HPV Free Colorado, where media and clinic coaching were used to improve HPV vaccination coverage among adolescents (11-17 years) in Adams, Arapahoe, Denver, Douglas, and Jefferson counties. Recent HPV vaccination schedule changes from three to two required doses highlighted further challenges in monitoring vaccination UTD rates.MethodsWe describe a Denver metropolitan area HPV case study where IIS data were used to inform and evaluate the impact of Alliance for HPV Free Colorado activities. IIS data were processed through the Immunization Calculation Engine (ICE)TM, a state-of-the-art open-source web application that provides immunization evaluation and forecasting, typically for patients and providers at the point of care. With the IIS data, the goal of ICE processing was to identify communities of low adolescent HPV coverage (11-17 years) for targeted media placement and track HPV trends over time at the clinic and population level. The Immunization Business Intelligence System (IBIS), processed IIS data from the Colorado Department of Public Health and Environment; using ICE, the validity of each vaccine was evaluated. Each HPV vaccine was evaluated for validity and an assessment made for each individual regarding HPV series initiation and completion (i.e., percent of individuals receiving 1, 2 or 3 valid HPV doses) depending on interval between vaccine and age at first dose. IBIS components and functionality were developed through collaborative design with a goal of developing lessons relevant for future surveillance efforts. Tableau dashboards were constructed to calculate rates of HPV initiation and completion for each participating county and healthcare practice.IBIS contained data on 33 million vaccines administered to 2.5 million adults and children residing in metro counties. In 2017, IBIS received approximately 2 million vaccines administered to 959,000 adults and children, representing roughly 35% of the 2.7 million metro residents estimated by the American Community Survey (2016). Specific to HPV vaccines, IBIS received over 900,000 HPV vaccines administered to roughly 400,000 individuals by over 1100 clinics; 2017 data included 91,951 HPV vaccines administered to 81,795 patients.Between 2015 and 2017, 186,489 HPV vaccines were administered to 116,901 adolescents 11 to 17 years residing in the Denver metro area. Using ICE, 85% of HPV vaccines were valid, 10% were accepted as extra doses not needed to complete the HPV series, 4% were invalid because the dose was given too soon after the previous dose, and less than 1% as invalid because the dose was administered too early (under nine years).As of 12/31/2017, 65,447 or 56% of adolescents 11 to 17 years had completed the HPV vaccine series, among those receiving any HPV vaccines. County specific completion rates varied from 53% to 60%, among adolescents receiving any HPV vaccines. Completion increased with age from 7% at 11 years, 34% at 12 years, 70% at 14 years, 76% at 15 years and then declined to 68% at 17 years of age. Among adolescents receiving any vaccines in the past decade, HPV completion rates were lower but increased with age from 2% at 11 years to 39% at 14 years and down to 22% at 17 years.Tableau reports displayed monthly age and county specific HPV completion rates, tracking trends over time. As ICE implemented modifications aligned with 2016 HPV schedule changes (from 3 doses to 2), IBIS was updated and trend data were reprocessed to accurately reflect current ACIP rules. IBIS was indexed to optimize direct query using Tableau for stratified dashboard reporting by demographic and/or geographic populations.IIS-based vaccination surveillance and reporting provided important guidance for public health program direction. IBIS repurposed a knowledge management system for a population-focused HPV surveillance use case applies across the metro area of Colorado. IBIS was built on a scalable platform, allowing for expansion of data capture and reporting across broader geographies and demographic groups, as well as different vaccines, vaccine groups and vaccine schedules. Collaboration across public health entities was important to construct appropriate infrastructure to build and maintain IBIS for broader public health use. Future development of IBIS includes expanding reporting to 10 additional Colorado counties and vaccines in 2018.How the Moderator Intends to Engage the Audience in Discussions on the TopicThe moderator will engage audience members in a discussion about the lessons learned from developing the IBIS tool at an LPHA including challenges to understand and interpret up to date rates and opportunities for translation in other jurisdictions. 


2019 ◽  
Vol 39 (1) ◽  
pp. 543-580 ◽  
Author(s):  
Bernhard Preim ◽  
Kai Lawonn

First Monday ◽  
2017 ◽  
Vol 22 (4) ◽  
Author(s):  
Anna Wilson ◽  
Terrie Lynn Thompson ◽  
Cate Watson ◽  
Valerie Drew ◽  
Sarah Doyle

Recent critiques of both the uses of and discourse surrounding big data have raised important questions as to the extent to which big data and big data techniques should be embraced. However, while the context-dependence of data has been recognized, there remains a tendency among social theorists and other commentators to treat certain aspects of the big data phenomenon, including not only the data but also the methods and tools used to move from data as database to data that can be interpreted and assigned meaning, in a homogenizing way. In this paper, we seek to challenge this tendency, and to explore the ways in which explicit consideration of the plurality of big data might inform particular instances of its exploitation. We compare one currently popular big data-inspired innovation — learning analytics — with three other big data contexts — the physical sciences, business intelligence and public health. Through these comparisons, we highlight some dangers of learning analytics implemented without substantial theoretical, ethical and design effort. In so doing, we also highlight just how plural data, analytical approaches and intentions are, and suggest that each new big data context needs to be recognized in its own singularity.


Author(s):  
Simon Andrews

As a preface to this Special 'CUBIST' Edition of the International Journal of Intelligent Information Technologies (IJIIT), this article describes the European Framework Seven Combining and Unifying Business Intelligence with Semantic Technologies (CUBIST) project, which ran from October 2010 to September 2013. The project aimed to combine the best elements of traditional BI with the newer, semantic, technologies of the Sematic Web, in the form of the Resource Description Framework (RDF), and Formal Concept Analysis (FCA). CUBIST's purpose was to provide end-users with “conceptually relevant and user friendly visual analytics” to allow them to explore their data in new ways, discovering hidden meaning and solving hitherto difficult problems. To this end, three of the partners in CUBIST were use-cases: recruitment consultancy, computational biology and the space industry. Each use-case provided their own requirements and problems that were finally addressed by the prototype CUBIST visual-analytics developed in the project.


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