Give more data, awareness and control to individual citizens, and they will help COVID-19 containment

Uptake and Utilization of the COVID-19 Alert App within a University Community in New York

The rapid onset of the COVID-19 pandemic elicited a swift response to control the virus ubiquitous within the United States. Expanded telehealth and health informatics became critical components of the pandemic response. The aim of this study was to assess the utilization of the COVID-19 New York (NY) Alert App and identify the perceived benefits and limitations of the App. A cross-sectional design was employed to collect data by using questionnaires with closed-ended and open-ended questions. The survey was developed and administered during March through April 2021. The study found that the highest rated benefit from using the COVID-19 NY Alert App was receiving alerts about being in close proximity to individuals diagnosed with COVID-19. Results showed that ineffective (insufficient and inappropriate) usage was the highest rated potential challenge for using the App. Study subjects were likely to download this Alert App when they perceived more benefits and less barriers to using the App. Findings from this study can help improve utilization of the App and inform development of similar tele-education tools. The study illuminated considerations for health information applications in scaling-up traditional COVID-19 tracing efforts and may facilitate the design of similar emergency preparedness health technology.

One Digital Health Intervention for Monitoring Human and Animal Welfare in Smart Cities: Viewpoint and Use Case

Smart cities and digital public health are closely related. Managing digital transformation in urbanization and living spaces is challenging. It is critical to prioritize the emotional and physical health and well-being of humans and their animals in the dynamic and ever-changing environment they share. Human-animal bonds are continuous as they live together or share urban spaces and have a mutual impact on each other's health as well as the surrounding environment. In addition, sensors embedded in the Internet of Things are everywhere in smart cities. They monitor events and provide appropriate responses. In this regard, accident and emergency informatics (A&EI) offers tools to identify and manage overtime hazards and disruptive events. Such manifold focuses fit with One Digital Health (ODH), which aims to transform health ecosystems with digital technology by proposing a comprehensive framework to manage data and support health-oriented policies. We showed and discussed how, by developing the concept of ODH intervention, the ODH framework can support the comprehensive monitoring and analysis of daily life events of humans and animals in technologically integrated environments such as smart homes and smart cities. We developed an ODH intervention use case in which A&EI mechanisms run in the background. The ODH framework structures the related data collection and analysis to enhance the understanding of human, animal, and environment interactions and associated outcomes. The use case looks at the daily journey of Tracy, a healthy woman aged 27 years, and her dog Mego. Using medical Internet of Things, their activities are continuously monitored and analyzed to prevent or manage any kind of health-related abnormality. We reported and commented on an ODH intervention as an example of a real-life ODH implementation. We gave the reader examples of a "how-to" analysis of Tracy and Mego's daily life activities as part of a timely implementation of the ODH framework. For each activity, relationships to the ODH dimensions were scored, and relevant technical fields were evaluated in light of the Findable, Accessible, Interoperable, and Reusable principles. This "how-to" can be used as a template for further analyses. An ODH intervention is based on Findable, Accessible, Interoperable, and Reusable data and real-time processing for global health monitoring, emergency management, and research. The data should be collected and analyzed continuously in a spatial-temporal domain to detect changes in behavior, trends, and emergencies. The information periodically gathered should serve human, animal, and environmental health interventions by providing professionals and caregivers with inputs and "how-to's" to improve health, welfare, and risk prevention at the individual and population levels. Thus, ODH complementarily combined with A&EI is meant to enhance policies and systems and modernize emergency management.

The efficacy of Raspberry Pi-based automatic voice message education on knowledge level and prevention behavior of high-risk population

Introduction: The number of confirmed COVID-19 cases has increased in Indonesia. Preventive measures are believed to break the chain of transmission of COVID-19. Therefore, increasing knowledge through health education is essential to improve preventive behavior in the community. The study aims to determine the efficacy of implementing health education using Raspberry Pi based automatic voice massages in increasing high risk populations’ knowledge and prevention behavior. Design and Methods: This study was a quasi-interventional method with a pre-posttest research design and a non-equivalent control group, consisting of 30 respondents in each group. Control group received health education through leaflet sharing, while intervention group received health education through Raspberry-pi based automatic voice massages. Results: This study showed that there were no significant different in knowledge between control and intervention group after obtaining health education. Meanwhile, the intervention group showed higher score in knowledge regarding COVID-19. Moreover, the prevention behavior was significantly improved in both groups after acquiring health education through leaflets and automatic voice messages. Conclusions: Health education using Raspberry Pi based automatic voice messages improved both knowledge and preventive behavior regarding COVID 19 in high risk population.

Privacy protection methods of location services in big data

The rapid development of mobile communication technology not only brings convenience and fun to our life, but also brings a series of problems such as privacy disclosure. Therefore, it is very necessary to study the privacy protection method based on location service to strengthen the security of location privacy. The purpose of this work is to improve the security of location privacy and prevent the disclosure of user privacy by studying the characteristics of location services and privacy protection methods. This article first describes the characteristics of the important location privacy protection law, and then studies the structural characteristics and operation process of the location privacy protection law. This work evaluates the advantages and disadvantages of different methods, and finally compares the performance of several privacy protection algorithms through experimental analysis. Through the research of hiding space method, two-level cache method based on user grid, differential privacy protection method and experimental analysis of the algorithm, an effective privacy protection algorithm can be obtained. It can better protect the location privacy of users. For example, dual-active in the hidden space algorithm has the best privacy protection performance. Compared with other algorithms, the success rate of generating hidden space is increased by more than 10%, and the time of generating hidden space is shortened by about a quarter. The algorithm It has certain practical value and significance for use in the privacy protection of users.

How is “solidarity” understood in discussions about contact tracing apps? An overview

Background

In the context of the COVID-19 pandemic, there is much discussion about contact tracing apps, their use to contain the spread of the virus as well as the ethical, legal, and social aspects of their development, implementation, acceptance, and use. In these discussions, authors frequently mention “solidarity” when making key points in arguments. At the same time, authors rarely specify how they understand “solidarity”. This lack of specification about how they understand “solidarity” can lead to misunderstandings in discussions.

Objective

To prevent such misunderstandings, it is important to specify how one understands “solidarity” when mentioning it in the discussions on contact tracing apps. Therefore, the aim of this paper is to elaborate how “solidarity” is understood in the context of contact tracing apps, i.e., how different authors understand “solidarity” when using it in discussions about these apps.

Methods

In order to find out how different authors understand “solidarity” when discussing contact tracing apps, I conduct a literature review. I collect papers from several databases, inductively work out central differences and similarities between the different uses of “solidarity”, and use them to code and analyze relevant passages.

Results

In the final sample, five different understandings of “solidarity” in the context of contact tracing apps can be identified. These understandings differ in how different authors (1) imagine the basic concept of solidarity, i.e., what “solidarity” refers to, (2) how they temporally relate solidarity to contact tracing apps, and (3) how they perceive the causal interactions between solidarity and contact tracing apps, i.e., the different ways in which solidarity and contact tracing apps influence each other.

Conclusions

The five understandings of “solidarity” in the context of contact tracing apps presented here can serve as guidance for how “solidarity” can be understood in discussions—thus contributing to a better mutual understanding and preventing communicative misunderstandings.

Addressing the socioeconomic divide in computational modeling for infectious diseases

The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics. Here, the authors provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.

Data privacy during pandemics: a systematic literature review of COVID-19 smartphone applications

BACKGROUND

On January 8, 2020, the Centers for Disease Control and Prevention officially announced a new virus in Wuhan, China. The first novel coronavirus (COVID-19) case was discovered on December 1, 2019, implying that the disease was spreading quietly and quickly in the community before reaching the rest of the world. To deal with the virus' wide spread, countries have deployed contact tracing mobile applications to control viral transmission. Such applications collect users' information and inform them if they were in contact with an individual diagnosed with COVID-19. However, these applications might have affected human rights by breaching users' privacy.

METHODOLOGY

This systematic literature review followed a comprehensive methodology to highlight current research discussing such privacy issues. First, it used a search strategy to obtain 808 relevant papers published in 2020 from well-established digital libraries. Second, inclusion/exclusion criteria and the snowballing technique were applied to produce more comprehensive results. Finally, by the application of a quality assessment procedure, 40 studies were chosen.

RESULTS

This review highlights privacy issues, discusses centralized and decentralized models and the different technologies affecting users' privacy, and identifies solutions to improve data privacy from three perspectives: public, law, and health considerations.

CONCLUSIONS

Governments need to address the privacy issues related to contact tracing apps. This can be done through enforcing special policies to guarantee users privacy. Additionally, it is important to be transparent and let users know what data is being collected and how it is being used.

Ethics of Smart Cities: Towards Value-Sensitive Design and Co-Evolving City Life

The digital revolution has brought about many societal changes such as the creation of “smart cities”. The smart city concept has changed the urban ecosystem by embedding digital technologies in the city fabric to enhance the quality of life of its inhabitants. However, it has also led to some pressing issues and challenges related to data, privacy, ethics inclusion, and fairness. While the initial concept of smart cities was largely technology- and data-driven, focused on the automation of traffic, logistics and processes, this concept is currently being replaced by technology-enabled, human-centred solutions. However, this is not the end of the development, as there is now a big trend towards “design for values”. In this paper, we point out how a value-sensitive design approach could promote a more sustainable pathway of cities that better serves people and nature. Such “value-sensitive design” will have to take ethics, law and culture on board. We discuss how organising the digital world in a participatory way, as well as leveraging the concepts of self-organisation, self-regulation, and self-control, would foster synergy effects and thereby help to leverage a sustainable technological revolution on a global scale. Furthermore, a “democracy by design” approach could also promote resilience.

Data-driven methods for present and future pandemics: Monitoring, modelling and managing

This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.

Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection

The efficacy of digital contact tracing against coronavirus disease 2019 (COVID-19) epidemic is debated: Smartphone penetration is limited in many countries, with low coverage among the elderly, the most vulnerable to COVID-19. We developed an agent-based model to precise the impact of digital contact tracing and household isolation on COVID-19 transmission. The model, calibrated on French population, integrates demographic, contact and epidemiological information to describe exposure and transmission of COVID-19. We explored realistic levels of case detection, app adoption, population immunity, and transmissibility. Assuming a reproductive ratio R = 2.6 and 50% detection of clinical cases, a ~20% app adoption reduces peak incidence by ~35%. With R = 1.7, >30% app adoption lowers the epidemic to manageable levels. Higher coverage among adults, playing a central role in COVID-19 transmission, yields an indirect benefit for the elderly. These results may inform the inclusion of digital contact tracing within a COVID-19 response plan.

Shared mobility in post-COVID era: New challenges and opportunities

This study is aimed at exploring the challenges and opportunities that the COVID-19 outbreak presents to the sustainability of shared mobility. To date, the sustainability of shared mobility has received little attention in the literature, and this study determines its central constructs that are critical to the sustainability of shared mobility. We accordingly conducted a three-phase Delphi approach composed of both qualitative and quantitative methods. Feedback was obtained from 18 international experts who are very knowledgeable regarding civil engineering and shared mobility, initially finding 18 challenges and 18 opportunities. Finally, we identified 12 key constructs as highly critical to the sustainability of shared mobility. The current work is an attempt to address gaps in exploring the challenges and opportunities that the COVID-19 outbreak has created in shared mobility, particularly when a comprehensive examination is needed. This study will serve as an inspiration and catalog for new studies within this field.

Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection

The efficacy of digital contact tracing against coronavirus disease 2019 (COVID-19) epidemic is debated: Smartphone penetration is limited in many countries, with low coverage among the elderly, the most vulnerable to COVID-19. We developed an agent-based model to precise the impact of digital contact tracing and household isolation on COVID-19 transmission. The model, calibrated on French population, integrates demographic, contact and epidemiological information to describe exposure and transmission of COVID-19. We explored realistic levels of case detection, app adoption, population immunity, and transmissibility. Assuming a reproductive ratio R = 2.6 and 50% detection of clinical cases, a ~20% app adoption reduces peak incidence by ~35%. With R = 1.7, >30% app adoption lowers the epidemic to manageable levels. Higher coverage among adults, playing a central role in COVID-19 transmission, yields an indirect benefit for the elderly. These results may inform the inclusion of digital contact tracing within a COVID-19 response plan.