Mist Data: Leveraging Mist Computing for Secure and Scalable Architecture for Smart and Connected Health

The smart health paradigms employ Internet-connected wearables for tele-monitoring, diagnosis providing inexpensive healthcare solutions. Mist computing reduces latency and increases throughput by processing data near the edge of the network. In the present paper, we proposed a secure mist Computing architecture that is validated on recently released public geospatial health dataset. Results and discussion support the efficacy of proposed architecture for smart geospatial health applications. The present research paper proposed SoA-Mist i.e. a three-tier secure framework for efficient management of geospatial health data with the use of mist devices. It proposed the security aspects in client layer, mist layer, fog layer and cloud layer. It has defined the prototype development by using win-win spiral model with use case and sequence diagram. Overlay analysis has been performed with the developed framework on malaria vector borne disease positive maps of Maharastra state in India from 2011 to 2014 in mobile clients as test case. Finally, It concludes with the comparison analysis of cloud based framework and proposed SoA-Mist framework

Measuring antibiotic availability and use in 20 low- and middle-income countries

Objective To assess antibiotic availability and use in health facilities in low- and middle-income countries, using the service provision assessment and service availability and readiness assessment surveys. Methods We obtained data on antibiotic availability at 13 561 health facilities in 13 service provision assessment and 8 service availability and readiness assessment surveys. In 10 service provision assessment surveys, child consultations with health-care providers were observed, giving data on antibiotic use in 22 699 children. Antibiotics were classified as access, watch or reserve, according to the World Health Organization’s AWaRe categories. The percentage of health-care facilities across countries with specific antibiotics available and the proportion of children receiving antibiotics for key clinical syndromes were estimated. Findings The surveys assessed the availability of 27 antibiotics (19 access, 7 watch, 1 unclassified). Co-trimoxazole and metronidazole were most widely available, being in stock at 89.5% (interquartile range, IQR: 11.6%) and 87.1% (IQR: 15.9%) of health facilities, respectively. In contrast, 17 other access and watch antibiotics were stocked, by fewer than a median of 50% of facilities. Of the 22 699 children observed, 60.1% (13 638) were prescribed antibiotics (mostly co-trimoxazole or amoxicillin). Children with respiratory conditions were most often prescribed antibiotics (76.1%; 8972/11 796) followed by undifferentiated fever (50.1%; 760/1518), diarrhoea (45.7%; 1293/2832) and malaria (30.3%; 352/1160). Conclusion Routine health facility surveys provided a valuable data source on the availability and use of antibiotics in low- and middle-income countries. Many access antibiotics were unavailable in a majority of most health-care facilities

Project sanitarium:playing tuberculosis to its end game

Interdisciplinary and collaborative projects between industry and academia provide exceptional opportunities for learning. Project Sanitarium is a serious game for Windows PC and Tablet which aims to embed learning about tuberculosis (TB) through the player taking on the role of a doctor and solving cases across the globe. The project developed as a collaboration between staff and undergraduate students at the School of Arts, Media and Computer Games at Abertay University working with academics and researchers from the Infection Group at the University of St Andrews. The project also engaged industry partners Microsoft and DeltaDNA. The project aimed to educate students through a workplace simulation pedagogical model, encourage public engagement at events and through news coverage and lastly to prototype whether games could be used to simulate a virtual clinical trial. The project was embedded in the Abertay undergraduate programme where students are presented with real world problems to solve through design and technology. The result was a serious game prototype that utilized game design techniques and technology to demystify and educate players about the diagnosis and treatment of one of the world’s oldest and deadliest diseases, TB. Project Sanitarium aims to not only educate the player, but allows the player to become a part of a simulated drug trial that could potentially help create new treatments in the fight against TB. The game incorporates a mathematical model that is based on data from real-world drug trials. The interdisciplinary pedagogical model provides undergraduates with workplace simulation, wider industry collaboration and access to academic expertise to solve challenging and complex problems

Situating the Next Generation of Impact Measurement and Evaluation for Impact Investing

In taking stock of the landscape, this paper promotes a convergence of methods, building from both the impact investment and evaluation fields.The commitment of impact investors to strengthen the process of generating evidence for their social returns alongside the evidence for financial returns is a veritable game changer. But social change is a complex business and good intentions do not necessarily translate into verifiable impact.As the public sector, bilaterals, and multilaterals increasingly partner with impact investors in achieving collective impact goals, the need for strong evidence about impact becomes even more compelling. The time has come to develop new mindsets and approaches that can be widely shared and employed in ways that will advance the frontier for impact measurement and evaluation of impact investing. Each of the menu options presented in this paper can contribute to building evidence about impact. The next generation of measurement will be stronger if the full range of options comes into play and the more evaluative approaches become commonplace as means for developing evidence and testing assumptions about the processes of change from a stakeholder perspective– with a view toward context and systems.Creating and sharing evidence about impact is a key lever for contributing to greater impact, demonstrating additionality, and for building confidence among potential investors, partners and observers in this emergent industry on its path to maturation. Further, the range of measurement options offers opportunities to choose appropriate approaches that will allow data to contribute to impact management– to improve on the business model of ventures and to improve services and systems that improve conditions for people and households living in poverty.

LEVERAGING PRIVATE DATA FOR PUBLIC GOOD: A Descriptive Analysis and Typology of Existing Practices

To address the challenges of our times, we need both new solutions and new ways to develop those solutions. Data will play a central role in this process. Yet, much of the most useful, timely and comprehensive data that could help transform the way we make decisions or solve public problems resides with the private sector in the form of call detail records, online purchases, sensor data, social media data, and other assets. If we truly want to harness the potential of data to improve people's lives, we need to understand and find ways to unlock and re-use this private data for public good.In what follows, we analyze the current practice of "data collaboratives," an emerging form of collaboration in which a private-sector entity's data is leveraged in partnership with other entities from the public sector, civil society or academia for public good. The GovLab coined the term "data collaborative" in 2015.The potential and realized contributions of data collaboratives stem from how the supply of and demand for data are widely dispersed—spread across government, the private sector, and civil society—and often poorly matched. While most commentary on the data era's shortcomings focuses on the potential misuse of data, one of the key challenges of our data age actually lies in a persistent failure to re-use data responsibly for public good. This failure results in tremendous inefficiencies and lost potential.Data collaboratives, when designed responsibly, are key to addressing this shortcoming. They draw together otherwise siloed data and a dispersed range of expertise, matching supply and demand and ensuring that relevant institutions and individuals are using and analyzing data in ways that maximize the possibility of new, innovative social solutions.While we have seen an uptake in normative discussions on how data should be shared, little analysis exists of the actual practice. Over the last few years, we have identified, curated and documented more than 150 data collaboratives deployed around the world to address societal challenges as varied as urban mobility, public health, and corruption. These cases are stored on our Data Collaboratives Explorer, the largest such repository on the topic.This paper seeks to answer the central question: What institutional arrangements and operational dynamics enable private-sector data holders to collaborate with external parties to create new public value

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Spatial Data Infrastructure (SDI) is an important framework for sharing geospatial big data using the web. Integration of SDI with cloud computing led to emergence of Cloud-SDI as a tool for transmission, processing and analysis of geospatial data. Fog computing is a paradigm where embedded computers are employed to increase the throughput and reduce latency at the edge of the network. In this study, we developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data. We built prototypes using Intel Edison and Raspberry Pi for studying the comparative performance. We conducted a case study on Malaria vector-borne disease positive maps of Maharastra state in India. The proposed framework had provision of lossless data compression for reduced data transfer. Also, overlay analysis of geospatial data was implemented. In addition, we discussed energy savings, cost analysis and scalability of the proposed framework with respect to efficient data processing. We compared the performance of the proposed framework with the state-of-the-art Cloud-SDI in terms of analysis time. Results and discussions showed the efficacy of the proposed system for enhanced analysis of geo-health big data generated from a variety of sensing frameworks

Topical Mining of malaria Using Social Media. A Text Mining Approach

Malaria is a life-threatening parasitic disease, common in subtropical and tropical climates caused by mosquitoes. Each year, several hundred thousand of people die from malaria infections. However, with the rapid growth, popularity and global reach of social media usage, a myriad of opportunities arises for extracting opinions and discourses on various topics and issues. This research examines the public discourse, trends and emergent themes surrounding malaria discussion. We query Twitter corpus leveraging text mining algorithms to extract and analyze topical themes. Further, to investigate these dynamics, we use Crimson social media analytics software to analyze topical emergent themes and monitor malaria trends. The findings reveal the discovery of pertinent topics and themes regarding malaria discourses. The implications include shedding insights to public health officials on sentiments and opinions shaping public discourse on malaria epidemic. The multi-dimensional analysis of data provides directions for future research and informs public policy decisions