Search results

Traditional public health methods for tracking contagious diseases are increasingly complemented with digital tools, which use data mining, analytics and crowdsourcing to predict disease outbreaks. In recent years, alongside these public health tools, commercial mobile apps such as Sickweather have also been released. Sickweather collects information from across the web, as well as self-reports from users, so that people can see who is sick in their neighborhood. The purpose of this paper is to examine the privacy and surveillance implications of digital disease tracking tools.

The author performed a content and platform analysis of two apps, Sickweather and HealthMap, by using them for three months, taking regular screenshots and keeping a detailed user journal. This analysis was guided by the walkthrough method and a cultural-historical activity theory framework, taking note of imagery and other content, but also the app functionalities, including characteristics of membership, “rules” and parameters of community mobilization and engagement, monetization and moderation. This allowed me to study HealthMap and Sickweather as modes of governance that allow for (and depend upon) certain actions and particular activity systems.

Draw on concepts of network power, the surveillance assemblage, and Deleuze’s control societies, as well as the data gathered from the content and platform analysis, the author argues that disease tracking apps construct disease threat as omnipresent and urgent, compelling users to submit personal information – including sensitive health data – with little oversight or regulation.

Disease tracking mobile apps are growing in popularity yet have received little attention, particularly regarding privacy concerns or the construction of disease risk.

This case study paper aims to explore the complexities and challenges of epidemic response and public health surveillance in Native American and Indigenous American communities in the United States and find viable solutions. This paper explores these topics through the emergence and impact of the hantavirus pulmonary syndrome (HPS) within the Navajo Nation in the United States using critical incident analysis and best practices.

This project is a case study paper based on a topical review of the literature. A topical review of the literature is a comprehensive exploration of the current body of knowledge within a particular research field. It is an important tool used by scholars and practitioners to further the development of existing knowledge as well as to identify potential directions for future research (Fourie, 2020). Such a paper can provide a useful insight into the various aspects of the process that the researcher may have overlooked, as well as highlighting potential areas of improvement (Gall et al., 2020). It can also provide a useful source of ideas and inspiration for the researcher as it can provide an overview of the various approaches used by other researchers in the field (Göpferich, 2009). Case study papers using a topical review of the literature have been used to help frame and inform research topics, problems and best practices for some time. They are typically used to explore a topic in greater depth and to provide an overview of the literature to improve the world of practice to provide a foundation for future comprehensive empirical research. Case study papers can provide research value by helping to identify gaps in the literature and by providing a general direction for further research. They can also be used to provide a starting point for research questions and hypotheses and to help identify potential areas of inquiry.

This study explores best practices in public health surveillance and epidemic response that can help strengthen public health infrastructure by informing the development of effective surveillance systems and emergency response plans, as well as improving data collection and analysis capabilities within Native American and Indigenous American communities in the United States that also have the option to include new technologies like artificial intelligence (AI) with similar outbreaks in the future.

The literature review did not include any primary data collection, so the existing available research may have limited the findings. The scope of the study was limited to published literature, which may not have reported all relevant findings. For example, unpublished studies, field studies and industry reports may have provided additional insights not included in the literature review. This research has significant value based on the limited amount of studies on how infectious diseases can severely impact Native American communities in the United States, leading to unnecessary and preventable suffering and death. As a result, research on viable best practices is needed on the best practices in public health surveillance and epidemic response in Native American and Indigenous American communities through historical events and critical incident analysis.

Research on public health surveillance and epidemic response in Native American communities can provide insights into the challenges faced by these communities and help identify potential solutions to improve their capacity to detect, respond to and prevent infectious diseases using innovative approaches and new technologies like AI.

More research on public health surveillance and epidemic response can inform policies and interventions to improve access to healthcare for Native American populations, such as increasing availability of healthcare services, providing culturally appropriate health education and improving communication between providers and patients. By providing better public health surveillance and response capacity, research can help reduce the burden of infectious diseases in Native American communities and ultimately lead to improved public health outcomes.

Introduction: With the proliferation and amalgamation of technology and the emergence of artificial intelligence and the internet of things, society is now facing a rapid explosion in big data. However, this explosion needs to be handled with care. Ethically managing big data is of great importance. If left unmanageable, it can create a bubble of data waste and not help society achieve human well-being, sustainable economic growth, and development.

Purpose: This chapter aims to understand different perspectives of big data. One philosophy of big data is defined by its volume and versatility, with an annual increase of 40% per annum. The other view represents its capability in dealing with multiple global issues fuelling innovation. This chapter will also offer insight into various ways to deal with societal problems, provide solutions to achieve economic growth, and aid vulnerable sections via sustainable development goals (SDGs).

Methodology: This chapter attempts to lay out a review of literature related to big data. It examines the implication that the big data pool potentially influences ideas and policies to achieve SDGs. Also, different techniques associated with collecting big data and an assortment of significant data sources are analysed in the context of achieving sustainable economic development and growth.

Findings: This chapter presents a list of challenges linked with big data analytics in governance and achievement of SDG. Different ways to deal with the challenges in using big data will also be addressed.

Adopting digital technology could facilitate the public health response to the COVID-19 pandemic. Some analysts argue that countries that adopted digital technology in their health sector have managed to control the virus better (Whitelaw et al., 2020). For instance, countries with more comprehensive contact tracing have significantly lower fatality rates (Yalaman et al., 2021). Moreover, World Health Organization (WHO) believes this technology is a crucial enabler for countries to meet the current challenge (WHO. Regional Office for the Western Pacific & University of Melbourne, 2021). In this regard, this study aims to quantitatively find the relationship between the technological advancement of countries and COVID-19 health outcomes, using seven technological indices that measure technological advancement.

The authors used the multiple linear regression method to answer the research questions. The first analysis focuses on a cross section of all countries worldwide, and the second focuses on European countries for which weekly death statistics exist after the pandemic.

The findings support those countries with more technological abilities managed to control the virus’s mortality better, as evidenced by the negative link between the mortality rate of COVID-19 and the technological factors at the national level. Results also reveal that technology adoption decreases the death risk due to COVID-19 in countries with more elderly people. The authors may argue that technological advancement positively correlates with the number of deaths and diagnosed cases because the authors can better collect data or because the virus spreads due to higher economic and business activities. However, such technological advancement significantly decreases the death risk (lower mortality rate in the first analysis and lower mortality rate for elderly people in the second analysis).

Three important conclusions could be made from the results: a lower mortality rate is generally expected for countries adopting advanced technology; technological advancement significantly decreases the death risk for elderly people; and a higher technology adoption level does not necessarily result in fewer diagnosed cases of/death due to COVID-19.

Although some studies have focused on e-health applications in the public health response to the COVID-19 pandemic, no studies, to the best of the authors’ knowledge, have tried to quantify its efficacy, most especially on the global level.

This chapter reviews traditional intelligence work, primarily how intelligence was perceived and conducted in the industrial economy. The review includes economic sectors with dedicated intelligence functions such as military, law enforcement, and national security. The review also includes secondary intelligence work in all other economic sectors. Looking across all these examples, the authors present a traditional life cycle model of intelligence work and highlight this traditional view of intelligence’s tactical and reactive approach. The chapter details the historical evolution and common intelligence elements in military, business, law enforcement, judicial forensics, national security, market, financial, medical, digital, and computer forensics.

The purpose of the study is to investigate critical factors affecting individuals’ intention to adopt internet of things (IoT) products in healthcare.

An integrated model was developed based on technology acceptance model (TAM), innovation diffusion theory (IDT), technological innovativeness (TI), protection motivation theory and privacy calculus theory. The model was tested with 426 respondents (222 females, 204 males) using partial least square structural equation model with all data grouped by gender.

Based on the results of the complete model, perceived advantage (PA), image and perceived ease of use (PEOU) constructs have a significant effect on intention to adopt IoT healthcare technology products. The results show that for females, compatibility and trialability have more impact on PEOU whereas for males PA has more impact on PEOU. Image, perceived privacy risk, perceived vulnerability have more impact on males when compared to females.

Research conducted only among Turkish people.

This study investigated adoption of future technology, “internet of things”, products in healthcare from a behavioral perspective by integrating various theories. The reason is that before launching any technology into the market, its facilitative factors should be researched for the people who are going to use this in their daily routine.

Climate variability, accompanied by rapid urbanization and rising population disproportionality, impacts urban poor settlements. This paper aims to analyse the climate resilience for the urban poor in Ahmedabad through the lens of WASH development strategies. To assess the adaptive capacities of urban poor communities, a framework in the form of a vulnerability matrix has been used consisting of four key parameters – tenure, basic services, mobilization and partnership and disaster management capacities. The matrix implicitly recommends area-specific interventions to boost adaptive capacities and improve resilience based on WASH services.

This paper was designed to assess the climate resilience of WASH services in the urban poor settlements of Ahmedabad city. In all, seven slums were selected using a stratified sampling approach considering topography, access to WASH services and urban heat island effect. These slums were then assessed using a theoretical framework having four key parameters – tenure, basic service, mobilization and partnership and disaster management capacities. The data for the analysis was collected from both secondary and primary sources. For the latter, semi-structured interviews with key stakeholders, observational field visits and focused group discussions with the communities were done.

A ladder form of assessment matrix was derived from a thorough literature review and various pre-existing theories. This matrix consists of four key parameters – tenure, basic service, mobilization and partnership and disaster management capacities. The slums were evaluated by applying this framework, and direct and indirect relationships were established between the said parameters.

This paper was adapted in the light of various obstacles put forward by the Covid-19 pandemic. Some of the interviews with the bureaucrats and external researchers were conducted online, while the engagement with the slum dwellers was in-person, considering appropriate social and/or physical distancing norms. Implications of the Covid-19 second wave restricted the involvement of researchers with the communities at an ethnographic level.

The ladder form of vulnerability assessment framework has been developed and contextualized using the insights from literature review, field visits and multi-stakeholder consultations. It was helpful in identifying aspects that require suitable interventions for improving and imparting resilience among the urban poor settlements. The learnings from this paper are significant for planners and decision-makers in identifying and prioritizing context-specific future projects for a city.

The purpose of this paper is to understand what dimensions consumers prefer to track using wearable technology to achieve a healthier lifestyle and how these tracking dimensions are related.

An online survey was conducted with potential consumers in the USA, and a series of Pearson’s correlation and regression analysis and multiple regressions was conducted.

The most preferred self-tracking dimensions, tracking dimensions on others, most private tracking dimensions, most variable dimensions, and the dimensions that need to be improved were identified. The results of this study showed positive relationships overall among similar types of tracking dimensions, such as among dimensions of physical health condition (disease and disorder symptoms and general vital signs), mental health condition (stress level and mood/feeling), healthy lifestyle (fitness, and pose and posture), and productivity and task management (work productivity, location, and time management).

Designers are encouraged to make wearable technology products that are durable, easy to care for, attractive in design, comfortable to wear and use, able to track preferred dimensions, appropriate for various consumers, unobtrusive, portable, and small. This research will guide wearable technology and fashion industry professionals in the development process of wearable technology to benefit consumers by helping them be more self-aware, empowering them to develop a healthier lifestyle, and ultimately increasing their quality of life and well-being.

The COVID-19 pandemic represents a unique challenge for public health worldwide. In this context, smartphone-based tracking apps play an important role in controlling transmission. However, privacy concerns may compromise the population’s willingness to adopt this mobile health (mHealth) technology. Based on the privacy calculus theory, this study aims to examine what factors drive or hinder adoption and disclosure, considering the moderating role of age and health status.

A cross-sectional survey was conducted in a European country hit by the pandemic that has recently launched a COVID-19 contact-tracing app. Data from 504 potential users was analyzed through partial least squares structural equation modeling.

Results indicate that perceived benefits and privacy concerns impact adoption and disclosure and confirm the existence of a privacy paradox. However, for young and healthy users, only benefits have a significant effect. Moreover, older people value more personal than societal benefits while for respondents with a chronical disease privacy concerns outweigh personal benefits.

The study contributes to consumer privacy research and to the mHealth literature, where privacy issues have been rarely explored, particularly regarding COVID-19 contact-tracing apps. The study re-examines the privacy calculus by incorporating societal benefits and moving from a traditional “self-focus” approach to an “other-focus” perspective. This study further adds to prior research by examining the moderating role of age and health condition, two COVID-19 risk factors. This study thus offers critical insights for governments and health organizations aiming to use these tools to reduce COVID-19 transmission rates.