Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing

Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Public Health and Epidemiology Informatics: Recent Research and Trends in the United States

Objectives To survey advances in public health and epidemiology informatics over the past three years. Methods We conducted a review of English-language research works conducted in the domain of public health informatics (PHI), and published in MEDLINE between January 2012 and December 2014, where information and communication technology (ICT) was a primary subject, or a main component of the study methodology. Selected articles were synthesized using a thematic analysis using the Essential Services of Public Health as a typology. Results Based on themes that emerged, we organized the advances into a model where applications that support the Essential Services are, in turn, supported by a socio-technical infrastructure that relies on government policies and ethical principles. That infrastructure, in turn, depends upon education and training of the public health workforce, development that creates novel or adapts existing infrastructure, and research that evaluates the success of the infrastructure. Finally, the persistence and growth of infrastructure depends on financial sustainability. Conclusions Public health informatics is a field that is growing in breadth, depth, and complexity. Several Essential Services have benefited from informatics, notably, “Monitor Health,” “Diagnose & Investigate,” and “Evaluate.” Yet many Essential Services still have not yet benefited from advances such as maturing electronic health record systems, interoperability amongst health information systems, analytics for population health management, use of social media among consumers, and educational certification in clinical informatics. There is much work to be done to further advance the science of PHI as well as its impact on public health practice

An Exploratory Study of COVID-19 Misinformation on Twitter

During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well as a better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights. We have collected all tweets mentioned in the verdicts of fact-checked claims related to COVID-19 by over 92 professional fact-checking organisations between January and mid-July 2020 and share this corpus with the community. This resulted in 1 500 tweets relating to 1 274 false and 276 partially false claims, respectively. Exploratory analysis of author accounts revealed that the verified twitter handle(including Organisation/celebrity) are also involved in either creating (new tweets) or spreading (retweet) the misinformation. Additionally, we found that false claims propagate faster than partially false claims. Compare to a background corpus of COVID-19 tweets, tweets with misinformation are more often concerned with discrediting other information on social media. Authors use less tentative language and appear to be more driven by concerns of potential harm to others. Our results enable us to suggest gaps in the current scientific coverage of the topic as well as propose actions for authorities and social media users to counter misinformation.Comment: 20 pages, nine figures, four tables. Submitted for peer review, revision

The potentials and challenges of big data in public health

The potential to use big data sources for public health increases with the broadening availability of data and improved methods of analysis. Whilst there are some well-known examples of the opportunistic use of big data, such as GoogleFlu, public health has not yet realised the full potential of such data sources. A literature review was undertaken to identify the potential of such data collections to impact public health, and to identify what challenges are currently limiting this potential. The potential include improved real-time analysis, research and development and genome studies. However, challenges listed are poor universal standardisation and classification, privacy and security, as well as current inadequate platforms and tools for analysis. Without such reviews, limited understanding will hinder the rate of advance in utilising such data to improve the health status of population in public health

DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Data Analytics for Crisis Management: A Case Study of Sharing Economy Services in the COVID-19 Pandemic

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data