Ethics and governance for digital disease surveillance

An individual's trust in government is a major determinant in the decision to voluntarily join a public health initiative

BACKGROUND

Recently, there has been a growing trend of incorporating technology using health applications by official organizations such as health organizations and governmental bodies. In response to the COVID-19 pandemic, Israel implemented a health application to be voluntarily downloaded by citizens (VA). Additionally, the Israeli authorities used mandatory mobile tracking to monitor citizens' movements (GT). The current study aims to identify the factors associated with individuals' decisions to download the voluntary contact-tracing app. We hypothesized that (a) attitudes toward GT will mediate the relations between trust in the healthcare system and downloading VA, and (b) attitudes toward GT will mediate the relations between the perceived COVID-19 threat and downloading VA.

METHODS

Data were collected among 741 respondents who completed an online survey on July 19-21, 2020. The survey was designed to represent the diversity of the Israeli population. A logistic regression was calculated with downloading VA as the dependent variable and trust in the healthcare system, perceived threat and attitudes toward GT as independent variables. Then, the extent to which attitudes toward GT mediated the associations between trust in the healthcare system and downloading VA and between the perceived threat and downloading the voluntary app was assessed using the Process procedure.

RESULTS

The findings reveal that 47.1% of respondents perceive governmental tracing as an invasion of privacy, while 24.4% report that it increased their sense of security. About a third report having downloaded the voluntary app. Both research hypotheses were supported; attitudes played a mediating role in the relationship between an individual's level of trust and an individual's level of perceived threat and behavior, i.e., higher trust and higher perceived COVID-19 threat were associated with more favorable attitudes towards GT, which was associated with more substantial odds of downloading VA.

CONCLUSIONS

The results emphasize the crucial importance of public trust. Building trust with the public is essential to encourage voluntary actions that ultimately benefit public health. Achieving and maintaining the public's trust requires addressing concerns about the potential misuse of government power and fostering an environment that promotes voluntary participation and engagement.

A Comprehensive Survey of Deep Learning Approaches in Image Processing

The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined image processing, tracing their evolution from early innovations to the latest state-of-the-art developments. It also analyzes the progression of architectural designs and learning paradigms that have significantly enhanced the ability to process and interpret complex visual data. Key advancements, such as techniques improving model efficiency, generalization, and robustness, are examined, showcasing DL's ability to address increasingly sophisticated image-processing tasks across diverse domains. Metrics used for rigorous model evaluation are also discussed, underscoring the importance of performance assessment in varied application contexts. The impact of DL in image processing is highlighted through its ability to tackle complex challenges and generate actionable insights. Finally, this survey identifies potential future directions, including the integration of emerging technologies like quantum computing and neuromorphic architectures for enhanced efficiency and federated learning for privacy-preserving training. Additionally, it highlights the potential of combining DL with emerging technologies such as edge computing and explainable artificial intelligence (AI) to address scalability and interpretability challenges. These advancements are positioned to further extend the capabilities and applications of DL, driving innovation in image processing.

Survey of Citizens' Preferences for Combined Contact Tracing App Features During a Pandemic: Conjoint Analysis

BACKGROUND

During the COVID-19 pandemic, an increased need for novel solutions such as digital contact tracing apps to mitigate virus spread became apparent. These apps have the potential to enhance public health initiatives through timely contact tracing and infection rate reduction. However, public and academic scrutiny has emerged around the adoption and use of these apps due to privacy concerns.

OBJECTIVE

This study aims to investigate public attitudes and preferences for contact tracing apps, specifically in Japan, using conjoint analysis to examine what specifications the public values most in such apps. By offering a nuanced understanding of the values that citizens prioritize, this study can help balance public health benefits and data privacy standards when designing contact tracing apps and serve as reference data for discussions on legal development and social consensus formation in the future.

METHODS

A cross-sectional, web-based questionnaire survey was conducted to determine how various factors related to the development and integration of infectious disease apps affect the public's intention to use such apps. Individuals were recruited anonymously by a survey company. All respondents were asked to indicate their preferences for a combination of basic attributes and infectious disease app features for conjoint analysis. The respondents were randomly divided into 2 groups: one responded to a scenario where the government was assumed to be the entity dealing with infectious disease apps (ie, the government cluster), and the other responded to a scenario where a commercial company was assumed to be this entity (ie, the business cluster). Samples of 500 respondents from each randomly selected group were used as target data.

RESULTS

For the government cluster, the most important attribute in scenario A was distributor rights (42.557), followed by public benefits (29.458), personal health benefits (22.725), and profit sharing (5.260). For the business cluster, the most important attribute was distributor rights (45.870), followed by public benefits (32.896), personal health benefits (13.994), and profit sharing (7.240). Hence, personal health benefits tend to be more important in encouraging active app use than personal financial benefits. However, the factor that increased motivation for app use the most was the public health benefits of cutting infections by half. Further, concern about the use of personal data collected by the app for any secondary purpose was a negative incentive, which was more significant toward app use compared to the other 3 factors.

CONCLUSIONS

The findings suggest that potential app users are positively motivated not only by personal health benefits but also by contributing to public health. Thus, a combined approach can be taken to increase app use.

"Lives versus livelihoods": Conflict and coherence between policy objectives in the COVID-19 pandemic

Many policies were put in place during the COVID-19 pandemic in the United States to manage the negative impact of the coronavirus. Limiting severe illness and death was one important objective of these policies, but it is widely acknowledged by public health ethicists that pandemic policies needed to consider other factors. Drawing on semi-structured interviews with 38 people across 17 states who participated in the state-level COVID-19 pandemic policy process, we examine how those actors recounted their engagement with four different objectives over the course of the pandemic: protecting public health with respect to COVID-19 (which we refer to as pathogen-focused disease prevention), protecting the economy, promoting the public's broader health and wellbeing, and preserving and restoring individual freedoms. We describe the different ways that pathogen-focused disease prevention was thought to have conflicted with, or to have been coherent with, the other three policy objectives over the course of the pandemic. In tracing the shifting relationships between objectives, we highlight four reasons put forward by the participants for why policy changes occurred throughout the pandemic: a change on the part of decisionmaker(s) regarding the perceived acceptability of the negative effects of a policy on one or more policy objectives; a change in the epistemic context; a change in the 'tools in the toolbox'; and a change in the public's attitudes that affected the feasibility of a policy. We conclude by considering the ethical implications of the shifting relationships that were described between objectives over the course of the pandemic.

'Staying in the lane' of public health? Boundary-work in the roles of state health officials and experts in COVID-19 policymaking

The state-level COVID-19 response in the United States necessitated collaboration between governor' offices, health departments and numerous other departments and outside experts. To gain insight into how health officials and experts contributed to advising on COVID-19 policies, we conducted semi-structured interviews with 25 individuals with a health specialisation who were involved in COVID-19 policymaking, taking place between February and December 2022. We found two diverging understandings of the role of health officials and experts in COVID-19 policymaking: the role of 'staying in the lane' of public health in terms of the information that they collected, their advocacy for policies and their area of expertise and the role of engaging in the balancing of multiple considerations, such as public health, feasibility and competing objectives (such as the economy) in the crafting of pandemic policy. We draw on the concept of boundary-work to examine how these roles were constructed. We conclude by considering the appropriateness as well as the ethical implications of these two approaches to public health policymaking.

Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review

BACKGROUND

The COVID-19 pandemic has prompted the deployment of digital technologies for public health surveillance globally. The rapid development and use of these technologies have curtailed opportunities to fully consider their potential impacts (eg, for human rights, civil liberties, privacy, and marginalization of vulnerable groups).

OBJECTIVE

We conducted a scoping review of peer-reviewed and gray literature to identify the types and applications of digital technologies used for surveillance during the COVID-19 pandemic and the predicted and witnessed consequences of digital surveillance.

METHODS

Our methodology was informed by the 5-stage methodological framework to guide scoping reviews: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the findings. We conducted a search of peer-reviewed and gray literature published between December 1, 2019, and December 31, 2020. We focused on the first year of the pandemic to provide a snapshot of the questions, concerns, findings, and discussions emerging from peer-reviewed and gray literature during this pivotal first year of the pandemic. Our review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines.

RESULTS

We reviewed a total of 147 peer-reviewed and 79 gray literature publications. Based on our analysis of these publications, we identified a total of 90 countries and regions where digital technologies were used for public health surveillance during the COVID-19 pandemic. Some of the most frequently used technologies included mobile phone apps, location-tracking technologies, drones, temperature-scanning technologies, and wearable devices. We also found that the literature raised concerns regarding the implications of digital surveillance in relation to data security and privacy, function creep and mission creep, private sector involvement in surveillance, human rights, civil liberties, and impacts on marginalized groups. Finally, we identified recommendations for ethical digital technology design and use, including proportionality, transparency, purpose limitation, protecting privacy and security, and accountability.

CONCLUSIONS

A wide range of digital technologies was used worldwide to support public health surveillance during the COVID-19 pandemic. The findings of our analysis highlight the importance of considering short- and long-term consequences of digital surveillance not only during the COVID-19 pandemic but also for future public health crises. These findings also demonstrate the ways in which digital surveillance has rendered visible the shifting and blurred boundaries between public health surveillance and other forms of surveillance, particularly given the ubiquitous nature of digital surveillance.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1136/bmjopen-2021-053962.

Global matters of epidemiology and the ethical challenges of addressing the health of populations

PURPOSE

The American College of Epidemiology (ACE) held its 2022 Annual Meeting, September 8-11, with a conference theme of 'Pandemic of Misinformation: Building Trust in Epidemiology'. The ACE Ethics Committee hosted a symposium session in recognition of the global spotlight placed on epidemiology and public health due to the COVID-19 crisis. The ACE Ethics Committee invited previous Chairs of the Ethics Committee and current President of the International Epidemiological Association to present at the symposium session. This paper aims to highlight the ethical challenges presented during the symposium session.

METHODS

Three speakers with diverse backgrounds representing expertize from the fields of ethics, epidemiology, public health, clinical trials, pharmacoepidemiology, statistics, law, and public policy, covering perspectives from the U.S., Europe, and Southeast Asia were selected to present on the ethical challenges in epidemiology and public health applying a global theme. Dr. D. Weed presented on 'Causation, Epidemiology and Ethics'; Dr. C.M. Pandey presented on the 'Ethical Challenges in the Practice of Digital Epidemiology'; and Dr. J. Acquavella presented on 'Departures from Scientific Objectivity: A Cause of Eroding Trust in Epidemiology.'

RESULTS

The collective goal to improve the public's health was a mutually shared theme across the three distinct areas. We highlight the common ethical guidance and principle-based approaches that have served epidemiology and public health in framing and critical analysis of novel challenges, including autonomy, beneficence, justice, scientific integrity, duties to the profession and community, and developing and maintaining public trust; however, gaps remain in how best to address health inequalities and the novel emergence and pervasiveness of misinformation and disinformation that have impacted the health of the global community. We introduce an ethical framework of translational bioethics that places considerations of the social determinants of health at the forefront.

CONCLUSIONS

The COVID-19 pandemic required an expedited public health response and, at the same time, placed the profession of epidemiology and public health, its system, and structures, under the microscope like never before. This article illustrates that revisiting our foundations in research and practice and orienting contemporary challenges using an ethical lens can assist in identifying and furthering the health of populations globally.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

BACKGROUND

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

OBJECTIVE

This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

METHODS

A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

RESULTS

Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

CONCLUSIONS

The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Current ethical and social issues in epidemiology

PURPOSE

The American College of Epidemiology held its 2021 Annual Meeting virtually, September 8-10, with a conference theme of 'From Womb to Tomb: Insights from Health Emergencies'. The American College of Epidemiology Ethics Committee hosted a symposium session in recognition of the ethical and social challenges brought to light by the coronavirus disease 2019 pandemic and on the occasion of the publication of the third edition of the classic text, Ethics and Epidemiology. The American College of Epidemiology Ethics Committee invited the book editor and contributing authors to present at the symposium session titled 'Current Ethical and Social Issues in Epidemiology.' The purpose of this paper is to further highlight the ethical challenges and presentations.

METHODS

Three speakers with expertise in ethics, health law, health policy, global health, health information technology, and translational research in epidemiology and public health were selected to present on the social and ethical issues in the current landscape. Dr. S Coughlin presented on the 'Ethical and Social Issues in Epidemiology', Dr. L Beskow presented on 'Ethical Challenges in Genetic Epidemiology', and Dr. K Goodman presented on the 'Ethics of Health Informatics'.

RESULTS

New digital sources of data and technologies are driving the ethical challenges and opportunities in epidemiology and public health as it relates to the three emerging topic areas identified: (1) digital epidemiology, (2) genetic epidemiology, and (3) health informatics. New complexities such as the reliance on social media to control infectious disease outbreaks and the introduction of computing advancements are requiring re-evaluation of traditional bioethical frameworks for epidemiology research and public health practice. We identified several cross-cutting ethical and social issues related to informed consent, benefits, risks and harms, and privacy and confidentiality and summarized these alongside more nuanced ethical considerations such as algorithmic bias, group harms related to data (mis)representation, risks of misinformation, return of genomic research results, maintaining data security, and data sharing. We offered an integrated synthesis of the stages of epidemiology research planning and conduct with the ethical issues that are most relevant in these emerging topic areas.

CONCLUSIONS

New realities exist for epidemiology and public health as professional groups who are faced with addressing population health, and especially given the recent pandemic and the widespread use of digital tools and technologies. Many ethical issues can be understood in the context of existing ethical frameworks; however, they have yet to be clearly identified or connected with the new technical and methodological applications of digital tools and technologies currently in use for epidemiology research and public health practice. To address current ethical challenges, we offered a synthesis of traditional ethical principles in public health science alongside more nuanced ethical considerations for emerging technologies and aligned these with lifecycle stages of epidemiology research. By critically reflecting on the impact of new digital sources of data and technologies on epidemiology research and public health practice, specifically in the control of infectious outbreaks, we offered insights on cultivating these new areas of professional growth while striving to improve population health.

Use of digital technologies for public health surveillance during the COVID-19 pandemic: A scoping review

Throughout the COVID-19 pandemic, a variety of digital technologies have been leveraged for public health surveillance worldwide. However, concerns remain around the rapid development and deployment of digital technologies, how these technologies have been used, and their efficacy in supporting public health goals. Following the five-stage scoping review framework, we conducted a scoping review of the peer-reviewed and grey literature to identify the types and nature of digital technologies used for surveillance during the COVID-19 pandemic and the success of these measures. We conducted a search of the peer-reviewed and grey literature published between 1 December 2019 and 31 December 2020 to provide a snapshot of questions, concerns, discussions, and findings emerging at this pivotal time. A total of 147 peer-reviewed and 79 grey literature publications reporting on digital technology use for surveillance across 90 countries and regions were retained for analysis. The most frequently used technologies included mobile phone devices and applications, location tracking technologies, drones, temperature scanning technologies, and wearable devices. The utility of digital technologies for public health surveillance was impacted by factors including uptake of digital technologies across targeted populations, technological capacity and errors, scope, validity and accuracy of data, guiding legal frameworks, and infrastructure to support technology use. Our findings raise important questions around the value of digital surveillance for public health and how to ensure successful use of technologies while mitigating potential harms not only in the context of the COVID-19 pandemic, but also during other infectious disease outbreaks, epidemics, and pandemics.

Equitable Design and Use of Digital Surveillance Technologies During COVID-19: Norms and Concerns

Given the unprecedented scale of digital surveillance in the COVID-19 pandemic, designing and implementing digital technologies in ways that are equitable is critical now and in future epidemics and pandemics. Yet to date there has been very limited consideration about what is necessary to promote their equitable design and implementation. In this study, literature relating to the use of digital surveillance technologies during epidemics and pandemics was collected and thematically analyzed for ethical norms and concerns related to equity and social justice. Eleven norms are reported, including procedural fairness and inclusive approaches to design and implementation, designing to rectify or avoid exacerbating inequities, and fair access. Identified concerns relate to digital divides, stigma and discrimination, disparate risk of harm, and unfair design processes. We conclude by considering what dimensions of social justice the norms promote and whether identified concerns can be addressed by building the identified norms into technology design and implementation practice.

In the shadow of privacy: Overlooked ethical concerns in COVID-19 digital epidemiology

The COVID-19 pandemic witnessed a surge in the use of health data to combat the public health threat. As a result, the use of digital technologies for epidemic surveillance showed great potential to collect vast volumes of data, and thereby respond more effectively to the healthcare challenges. However, the deployment of these technologies raised legitimate concerns over risks to individual privacy. While the ethical and governance debate focused primarily on these concerns, other relevant issues remained in the shadows. Leveraging examples from the COVID-19 pandemic, this perspective article aims to investigate these overlooked issues and their ethical implications. Accordingly, we explore the problem of the digital divide, the role played by tech companies in the public health domain and their power dynamics with the government and public research sector, and the re-use of personal data, especially in the absence of adequate public involvement. Even if individual privacy is ensured, failure to properly engage with these other issues will result in digital epidemiology tools that undermine equity, fairness, public trust, just distribution of benefits, autonomy, and minimization of group harm. On the contrary, a better understanding of these issues, a broader ethical and data governance approach, and meaningful public engagement will encourage adoption of these technologies and the use of personal data for public health research, thus increasing their power to tackle epidemics.

A computer modeling method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2

PURPOSE

No method is available to systematically study SARS-CoV-2 transmission dynamics using the data that rideshare companies share with government agencies. We developed a proof-of-concept method for the analysis of SARS-CoV-2 transmissions between rideshare passengers and drivers.

METHOD

To assess whether this method could enable hypothesis testing about SARS-CoV-2, we repeated ten 200-day agent-based simulations of SARS-CoV-2 propagation within the Los Angeles County rideshare network. Assuming data access for 25% of infections, we estimated an epidemiologist's ability to analyze the observable infection patterns to correctly identify a baseline viral variant A, as opposed to viral variant A with mask use (50% reduction in viral particle exchange), or a more infectious viral variant B (300% higher cumulative viral load).

RESULTS

Simulations had an average of 190,387 potentially infectious rideshare interactions, resulting in 409 average diagnosed infections. Comparison of the number of observed and expected passenger-to-driver infections under each hypothesis demonstrated our method's ability to consistently discern large infectivity differences (viral variant A vs. viral variant B) given partial data from one large city, and to discern smaller infectivity differences (viral variant A vs. viral variant A with masks) given partial data aggregated across multiple cities.

CONCLUSIONS

This novel statistical method suggests that, for the present and subsequent pandemics, government-facilitated analysis of rideshare data combined with diagnosis records may augment efforts to better understand viral transmission dynamics and to measure changes in infectivity associated with nonpharmaceutical interventions and emergent viral strains.

Ethics of digital contact tracing wearables

The success of digital COVID-19 contact tracing requires a strategy that successfully addresses the digital divide-inequitable access to technology such as smartphones. Lack of access both undermines the degree of social benefit achieved by the use of tracing apps, and exacerbates existing social and health inequities because those who lack access are likely to already be disadvantaged. Recently, Singapore has introduced portable tracing wearables (with the same functionality as a contact tracing app) to address the equity gap and promote public health. We argue that governments have an ethical obligation to ensure fair access to the protective benefits of contract tracing during the pandemic and that wearables are an effective way of addressing some important equity issues. The most contentious issues about contact tracing apps have been the potential infringements of privacy and individual liberty, especially where the use of apps or other technology (such as wearables or QR codes) is required for access to certain spaces. Here we argue that wearables, as opposed to apps alone, will make a digital contact tracing mandate more practical and explain some conditions under which such a mandate would be justified. We focus on Singapore as a case study that has recently deployed contact tracing wearables nationally, but also reference debate about wearables in Australia and New Zealand. Our analysis will be relevant to counties trialling similar portable tracing wearables.

Tracing app technology: an ethical review in the COVID-19 era and directions for post-COVID-19

We conducted a systematic literature review on the ethical considerations of the use of contact tracing app technology, which was extensively implemented during the COVID-19 pandemic. The rapid and extensive use of this technology during the COVID-19 pandemic, while benefiting the public well-being by providing information about people's mobility and movements to control the spread of the virus, raised several ethical concerns for the post-COVID-19 era. To investigate these concerns for the post-pandemic situation and provide direction for future events, we analyzed the current ethical frameworks, research, and case studies about the ethical usage of tracing app technology. The results suggest there are seven essential ethical considerations-privacy, security, acceptability, government surveillance, transparency, justice, and voluntariness-in the ethical use of contact tracing technology. In this paper, we explain and discuss these considerations and how they are needed for the ethical usage of this technology. The findings also highlight the importance of developing integrated guidelines and frameworks for implementation of such technology in the post- COVID-19 world.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10676-022-09659-6.

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.

Advancing a Data Justice Framework for Public Health Surveillance

BACKGROUND

Bioethical debates about privacy, big data, and public health surveillance have not sufficiently engaged the perspectives of those being surveilled. The data justice framework suggests that big data applications have the potential to create disproportionate harm for socially marginalized groups. Using examples from our research on HIV surveillance for individuals incarcerated in jails, we analyze ethical issues in deploying big data in public health surveillance.

METHODS

We conducted qualitative, semi-structured interviews with 24 people living with HIV who had been previously incarcerated in county jails about their perspectives on and experiences with HIV surveillance, as part of a larger study to characterize ethical considerations in leveraging big data techniques to enhance continuity of care for incarcerated people living with HIV.

RESULTS

Most participants expressed support for the state health department tracking HIV testing results and viral load data. Several viewed HIV surveillance as a violation of privacy, and several had actively avoided contact from state public health outreach workers. Participants were most likely to express reservations about surveillance when they viewed the state's motives as self-interested. Perspectives highlight the mistrust that structurally vulnerable people may have in the state's capacity to act as an agent of welfare. Findings suggest that adopting a nuanced, context-sensitive view on surveillance is essential.

CONCLUSIONS

Establishing trustworthiness through interpersonal interactions with public health personnel is important to reversing historical legacies of harm to racial minorities and structurally vulnerable groups. Empowering stakeholders to participate in the design and implementation of data infrastructure and governance is critical for advancing a data justice agenda, and can offset privacy concerns. The next steps in advancing the data justice framework in public health surveillance will be to innovate ways to represent the voices of structurally vulnerable groups in the design and governance of big data initiatives.

Challenges to biobanking in LMICs during COVID-19: time to reconceptualise research ethics guidance for pandemics and public health emergencies?

Biobanking can promote valuable health research that may lead to significant societal benefits. However, collecting, storing and sharing human samples and data for research purposes present numerous ethical challenges. These challenges are exacerbated when the biobanking efforts aim to facilitate research on public health emergencies and include the sharing of samples and data between low/middle-income countries (LMICs) and high-income countries (HICs). In this article, we explore ethical challenges for COVID-19 biobanking, offering examples from two past infectious disease outbreaks in LMICs where biobanking activities contributed to the perpetuation of global inequities. We focus on how the ethical imperative to promote the common good during public health emergencies can conflict with protecting the interests of biobank participants. We discuss how conducting biobank research under a waiver of informed consent during public health emergencies is ethically permissible, provided guidance is in place to prevent biopiracy and exploitation of vulnerable communities. We also highlight the need for biobank collaborations between LMICs and HICs to promote capacity building and benefit sharing. Finally, we offer guidance to promote the ethical oversight of biobanks and biobank research during the COVID-19 pandemic or other future public health emergencies.

A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research—A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee

Artificial and augmented intelligence (AI) and machine learning (ML) methods are expanding into the health care space. Big data are increasingly used in patient care applications, diagnostics, and treatment decisions in allergy and immunology. How these technologies will be evaluated, approved, and assessed for their impact is an important consideration for researchers and practitioners alike. With the potential of ML, deep learning, natural language processing, and other assistive methods to redefine health care usage, a scaffold for the impact of AI technology on research and patient care in allergy and immunology is needed. An American Academy of Asthma Allergy and Immunology Health Information Technology and Education subcommittee workgroup was convened to perform a scoping review of AI within health care as well as the specialty of allergy and immunology to address impacts on allergy and immunology practice and research as well as potential challenges including education, AI governance, ethical and equity considerations, and potential opportunities for the specialty. There are numerous potential clinical applications of AI in allergy and immunology that range from disease diagnosis to multidimensional data reduction in electronic health records or immunologic datasets. For appropriate application and interpretation of AI, specialists should be involved in the design, validation, and implementation of AI in allergy and immunology. Challenges include incorporation of data science and bioinformatics into training of future allergists-immunologists.

The ecology of COVID-19 and related environmental and sustainability issues

Around the globe, human behavior and ecosystem health have been extensively and sometimes severely affected by the unprecedented COVID-19 pandemic. Most efforts to study these complex and heterogenous effects to date have focused on public health and economics. Some studies have evaluated the pandemic's influences on the environment, but often on a single aspect such as air or water pollution. The related research opportunities are relatively rare, and the approaches are unique in multiple aspects and mostly retrospective. Here, we focus on the diverse research opportunities in disease ecology and ecosystem sustainability related to the (intermittent) lockdowns that drastically reduced human activities. We discuss several key knowledge gaps and questions to address amid the ongoing pandemic. In principle, the common knowledge accumulated from invasion biology could also be effectively applied to COVID-19, and the findings could offer much-needed information for future pandemic prevention and management.

Public acceptability of COVID-19 control measures in Singapore, Hong Kong, and Malaysia: A cross-sectional survey

Background

Several countries have implemented control measures to limit SARS-CoV-2 spread, including digital contact tracing, digital monitoring of quarantined individuals, and testing of travelers. These raise ethical issues around privacy, personal freedoms, and equity. However, little is known regarding public acceptability of these measures.

Methods

In December 2020, we conducted a survey among 3635 respondents in Singapore, Hong Kong, and Malaysia to understand public perceptions on the acceptability of COVID-19 control measures.

Findings

Hong Kong respondents were much less supportive of digital contact tracing and monitoring devices than those in Malaysia and Singapore. Around three-quarters of Hong Kong respondents perceived digital contact tracing as an unreasonable restriction of individual freedom; <20% trusted that there were adequate local provisions preventing these data being used for other purposes. This was the opposite in Singapore, where nearly 3/4 of respondents agreed that there were adequate data protection rules locally. In contrast, only a minority of Hong Kong respondents viewed mandatory testing and vaccination for travelers as unreasonable infringements of privacy or freedom. Less than 2/3 of respondents in all territories were willing to be vaccinated against COVID-19, with a quarter of respondents undecided. However, support for differential travel restrictions for vaccinated and unvaccinated individuals was high in all settings.

Interpretation

Our findings highlight the importance of sociopolitical context in public perception of public health measures and emphasize the need to continually monitor public attitudes toward such measures to inform implementation and communication strategies.

Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology

Osteoarthritis is a degenerative joint disease that affects millions of people worldwide. Current guidelines emphasize the importance of regular physical activity as a preventive measure against disease progression and as a valuable strategy for pain and functionality management. Despite this, most patients with osteoarthritis are inactive. Modern technological advances have led to the implementation of digital devices, such as wearables and smartphones, showing new opportunities for healthcare professionals and researchers to monitor physical activity and therefore engage patients in daily exercising. Additionally, digital devices have emerged as a promising tool for improving frequent health data collection, disease monitoring, and supporting public health surveillance. The leveraging of digital data has laid the foundation for developing a new concept of epidemiological study, known as "Digital Epidemiology". Analyzing real-world data can change the way we observe human behavior and suggest health interventions, as in the case of physical exercise and osteoarthritic patients. Furthermore, large-scale data could contribute to personalized and precision medicine in the future. Herein, an overview of recent clinical applications of wearables for monitoring physical activity in patients with osteoarthritis and the benefits of exploiting real-world data in the context of digital epidemiology are discussed.

Fostering participation in digital contact tracing

Digital contact tracing is a promising digital public health intervention to manage epidemics. However, in order to reach its full potential, the technology has to be widely adopted and used. During the SARS-CoV-2 pandemic, this has not necessarily been the case. We review the literature with a focus on how participation in digital contact tracing could be fostered and provide policy recommendations on how to increase its adoption and usage as well as recommendations for further research.

Comparative Study of Government Response Measures and Epidemic Trends for COVID-19 Global Pandemic

The ongoing novel coronavirus (COVID-19) epidemic has evolved into a full range of challenges that the world is facing. Health and economic threats caused governments to take preventive measures against the spread of the disease. This study aims to provide a correlation analysis of the response measures adopted by countries and epidemic trends since the COVID-19 outbreak. This analysis picks 13 countries for quantitative assessment. We select a trusted model to fit the epidemic trend curves in segments and catch the characteristics based on which we explore the key factors of COVID-19 spread. This review generates a score table of government response measures according to the Likert scale. We use the Delphi method to obtain expert judgments about the government response in the Likert scale. Furthermore, we find a significant negative correlation between the epidemic trend characteristics and the government response measure scores given by experts through correlation analysis. More stringent government response measures correlate with fewer infections and fewer waves in the infection curves. Stringent government response measures curb the spread of COVID-19, limit the number of total infectious cases, and reduce the time to peak of total cases. The clusters of the results categorize the countries into two specific groups. This study will improve our understanding of the prevention of COVID-19 spread and government response.

From SARS to COVID-19: Digital infrastructures of surveillance and segregation in exceptional times

The COVID-19 pandemic, an exceptional crisis, sparked the introduction of new digital infrastructure to halt the novel coronavirus's spread. This paper explores how such digital infrastructure's impact might reverberate over the long term, by comparing Singapore, Hong Kong, and mainland China's utilization of digital technology in response to the 2003 SARS outbreak, and their responses to the 2020 COVID-19 pandemic. We find that advancements in digital technology since 2003 have boosted governments' surveillance and segregation abilities substantially-most dramatically so in China. Even though some of these new digital interventions are ostensibly designed to be temporary ones to address the needs of the immediate crisis, we argue that the resultant extensions of state power experienced during COVID-19 are likely to have profound long-term effects because they fundamentally affect sociopolitical contexts, institutional capabilities, and digital cultures. We also find that the extent to which governments can extend digital surveillance and segregation abilities during the pandemic is contingent on their respective sociopolitical, institutional, and digital cultural contexts.

Contact tracing apps for the COVID-19 pandemic: a systematic literature review of challenges and future directions for neo-liberal societies

PURPOSE

The COVID-19 pandemic has spread with increased fatalities around the world and has become an international public health crisis. Public health authorities in many countries have introduced contact tracing apps to track and trace infected persons as part of measures to contain the spread of the Severe Acute Respiratory Syndrome-Coronavirus 2. However, there are major concerns about its efficacy and privacy which affects mass acceptance amongst a population. This systematic literature review encompasses the current challenges facing this technology and recommendations to address such challenges in the fight against the COVID-19 pandemic in neo-liberal societies.

METHODS

The systematic literature review was conducted by searching databases of Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, PsycInfo and ScienceDirect using the search terms ("Contact Tracing" OR "Contact Tracing apps") AND ("COVID-19" OR "Coronavirus") to identify relevant literature. The searches were run against the title, keywords, or abstract, depending on the search platforms. The searches were conducted between January 1, 2020, through 31st January 2021. Further inputs were also taken from preprints, published government and technical reports. We explore and discuss from the selected literature, the key challenges and issues that influence unwillingness to use these contact tracing apps in neo-liberal societies which include the plausibility of abuse of user privacy rights and lack of trust in the government and public health authorities by their citizens. Other challenges identified and discussed include ethical issues, security vulnerabilities, user behaviour and participation, and technical constraints.

RESULTS AND CONCLUSION

Finally, in the analysis of this systematic literature review, recommendations to address these challenges, future directions, and considerations in the use of digital contact tracing apps and related technologies to contain the spread of future pandemic outbreaks are presented. For policy makers in neo-liberal societies, this study provides an in-depth review of issues that must be addressed. We highlight recommendations to improve the willingness to use such digital technologies and could facilitate mass acceptance amongst users.

'Personal Health Surveillance': The Use of mHealth in Healthcare Responsibilisation

There is an ongoing increase in the use of mobile health (mHealth) technologies that patients can use to monitor health-related outcomes and behaviours. While the dominant narrative around mHealth focuses on patient empowerment, there is potential for mHealth to fit into a growing push for patients to take personal responsibility for their health. I call the first of these uses 'medical monitoring', and the second 'personal health surveillance'. After outlining two problems which the use of mHealth might seem to enable us to overcome-fairness of burdens and reliance on self-reporting-I note that these problems would only really be solved by unacceptably comprehensive forms of personal health surveillance which applies to all of us at all times. A more plausible model is to use personal health surveillance as a last resort for patients who would otherwise independently qualify for responsibility-based penalties. However, I note that there are still a number of ethical and practical problems that such a policy would need to overcome. The prospects of mHealth enabling a fair, genuinely cost-saving policy of patient responsibility are slim.

Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic

Background: In order to prevent spread and improve control of infectious diseases, public health experts need to closely monitor human and animal populations. Infectious disease surveillance is an established, routine data collection process essential for early warning, rapid response, and disease control. The quantity of data potentially useful for early warning and surveillance has increased exponentially due to social media and other big data streams. Digital epidemiology is a novel discipline that includes harvesting, analysing, and interpreting data that were not initially collected for healthcare needs to enhance traditional surveillance. During the current COVID-19 pandemic, the importance of digital epidemiology complementing traditional public health approaches has been highlighted. Objective: The aim of this paper is to provide a comprehensive overview for the application of data and digital solutions to support surveillance strategies and draw implications for surveillance in the context of the COVID-19 pandemic and beyond. Methods: A search was conducted in PubMed databases. Articles published between January 2005 and May 2020 on the use of digital solutions to support surveillance strategies in pandemic settings and health emergencies were evaluated. Results: In this paper, we provide a comprehensive overview of digital epidemiology, available data sources, and components of 21st-century digital surveillance, early warning and response, outbreak management and control, and digital interventions. Conclusions: Our main purpose was to highlight the plausible use of new surveillance strategies, with implications for the COVID-19 pandemic strategies and then to identify opportunities and challenges for the successful development and implementation of digital solutions during non-emergency times of routine surveillance, with readiness for early-warning and response for future pandemics. The enhancement of traditional surveillance systems with novel digital surveillance methods opens a direction for the most effective framework for preparedness and response to future pandemics.

Do you have COVID-19? How to increase the use of diagnostic and contact tracing apps

Diagnostic and contact tracing apps are a needed weapon to contain contagion during a pandemic. We study how the content of the messages used to promote the apps influence adoption by running a survey experiment on approximately 23,000 Mexican adults. Respondents were randomly assigned to one of three different prompts, or a control condition, before stating their willingness to adopt a diagnostic app and contact tracing app. The prompt emphasizing government efforts to ensure data privacy, which has been one of the most common strategies, reduced willingness to adopt the apps by about 4 pp and 3 pp, respectively. An effective app promotion policy must understand individuals’ reservations and be wary of unintended reactions to naïve reassurances.

Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation

The global and national response to the COVID-19 pandemic has been inadequate due to a collective lack of preparation and a shortage of available tools for responding to a large-scale pandemic. By applying lessons learned to create better preventative methods and speedier interventions, the harm of a future pandemic may be dramatically reduced. One potential measure is the widespread use of contact tracing apps. While such apps were designed to combat the COVID-19 pandemic, the time scale in which these apps were deployed proved a significant barrier to efficacy. Many companies and governments sprinted to deploy contact tracing apps that were not properly vetted for performance, privacy, or security issues. The hasty development of incomplete contact tracing apps undermined public trust and negatively influenced perceptions of app efficacy. As a result, many of these apps had poor voluntary public uptake, which greatly decreased the apps' efficacy. Now, with lessons learned from this pandemic, groups can better design and test apps in preparation for the future. In this viewpoint, we outline common strategies employed for contact tracing apps, detail the successes and shortcomings of several prominent apps, and describe lessons learned that may be used to shape effective contact tracing apps for the present and future. Future app designers can keep these lessons in mind to create a version that is suitable for their local culture, especially with regard to local attitudes toward privacy-utility tradeoffs during public health crises.

Mapping research strands of ethics of artificial intelligence in healthcare: A bibliometric and content analysis

The growth of artificial intelligence in promoting healthcare is rapidly progressing. Notwithstanding its promising nature, however, AI in healthcare embodies certain ethical challenges as well. This research aims to delineate the most influential elements of scientific research on AI ethics in healthcare by conducting bibliometric, social network analysis, and cluster-based content analysis of scientific articles. Not only did the bibliometric analysis identify the most influential authors, countries, institutions, sources, and documents, but it also recognized four ethical concerns associated with 12 medical issues. These ethical categories are composed of normative, meta-ethics, epistemological and medical practice. The content analysis complemented this list of ethical categories and distinguished seven more ethical categories: ethics of relationships, medico-legal concerns, ethics of robots, ethics of ambient intelligence, patients' rights, physicians' rights, and ethics of predictive analytics. This analysis likewise identified 40 general research gaps in the literature and plausible future research strands. This analysis furthers conversations on the ethics of AI and associated emerging technologies such as nanotech and biotech in healthcare, hence, advances convergence research on the ethics of AI in healthcare. Practically, this research will provide a map for policymakers and AI engineers and scientists on what dimensions of AI-based medical interventions require stricter policies and guidelines and robust ethical design and development.

Automatic gender detection in Twitter profiles for health-related cohort studies

Objective

Biomedical research involving social media data is gradually moving from population-level to targeted, cohort-level data analysis. Though crucial for biomedical studies, social media user’s demographic information (eg, gender) is often not explicitly known from profiles. Here, we present an automatic gender classification system for social media and we illustrate how gender information can be incorporated into a social media-based health-related study.

Materials and Methods

We used a large Twitter dataset composed of public, gender-labeled users (Dataset-1) for training and evaluating the gender detection pipeline. We experimented with machine learning algorithms including support vector machines (SVMs) and deep-learning models, and public packages including M3. We considered users’ information including profile and tweets for classification. We also developed a meta-classifier ensemble that strategically uses the predicted scores from the classifiers. We then applied the best-performing pipeline to Twitter users who have self-reported nonmedical use of prescription medications (Dataset-2) to assess the system’s utility.

Results and Discussion

We collected 67 181 and 176 683 users for Dataset-1 and Dataset-2, respectively. A meta-classifier involving SVM and M3 performed the best (Dataset-1 accuracy: 94.4% [95% confidence interval: 94.0–94.8%]; Dataset-2: 94.4% [95% confidence interval: 92.0–96.6%]). Including automatically classified information in the analyses of Dataset-2 revealed gender-specific trends—proportions of females closely resemble data from the National Survey of Drug Use and Health 2018 (tranquilizers: 0.50 vs 0.50; stimulants: 0.50 vs 0.45), and the overdose Emergency Room Visit due to Opioids by Nationwide Emergency Department Sample (pain relievers: 0.38 vs 0.37).

Conclusion

Our publicly available, automated gender detection pipeline may aid cohort-specific social media data analyses (https://bitbucket.org/sarkerlab/gender-detection-for-public).

[On the ethics of corona apps]

Definition of the problem

In spring 2020, as much of the world was emerging from widespread "lockdowns" as an emergency measure to combat the spread of SARS-CoV‑2, there was sustained discussion about how to lift measures while preventing further waves of the virus and the need for further lockdowns. One strategy that attracted significant attention was the use of digital contact-tracing apps to quickly alert users of possible exposure to the virus, and to direct them into quarantine. The initially high expectations placed upon this strategy were not met-despite the implementation of a digital contact-tracing app in Germany, further restrictions have been placed on the general population in response to further waves of the virus. We consider how digital contact tracing might have been made more effective.

Arguments

We argue that there is a conflict between collecting as little data as possible, and more effective epidemic control. In contrast to the "Corona-Warn-App" that was implemented in Germany, an app that stored more information on a central server (a so-called "centralized" app) had the potential to significantly decrease viral spread. We then look at the privacy-based arguments against the centralized storage of information, suggesting that "decentralized" systems have privacy problems of their own.

Results

The German debate on digital contact tracing apps was quickly dominated by privacy concerns, to the detriment of other ethical factors such as enhancing potential effectiveness. Furthermore, the potential problems with privacy inherent in decentralized apps were obscured in the discussion. Once we recognize these two aspects, we can see that there is an argument to be made for preferring centralized digital contact-tracing apps.

Test, Trace, and Put on the Blockchain?: A Viewpoint Evaluating the Use of Decentralized Systems for Algorithmic Contact Tracing to Combat a Global Pandemic

The enormous pressure of the increasing case numbers experienced during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. The field of algorithmic contact tracing, in particular, an area of research that had previously received limited attention, has moved into the spotlight as a crucial factor in containing the pandemic. The use of digital tools to enable more robust and expedited contact tracing and notification, while maintaining privacy and trust in the data generated, is viewed as key to identifying chains of transmission and close contacts, and, consequently, to enabling effective case investigations. Scaling these tools has never been more critical, as global case numbers have exceeded 100 million, as many asymptomatic patients remain undetected, and as COVID-19 variants begin to emerge around the world. In this context, there is increasing attention on blockchain technology as a part of systems for enhanced digital algorithmic contact tracing and reporting. By analyzing the literature that has emerged from this trend, the common characteristics of the designs proposed become apparent. An archetypal system architecture can be derived, taking these characteristics into consideration. However, assessing the utility of this architecture using a recognized evaluation framework shows that the added benefits and features of blockchain technology do not provide significant advantages over conventional centralized systems for algorithmic contact tracing and reporting. From our study, it, therefore, seems that blockchain technology may provide a more significant benefit in other areas of public health beyond contact tracing.

Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing

Digital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We develop a new framework that parameterizes disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we are able to accurately track the local effective reproduction number of COVID-19 in near real time (i.e., no longer constrained by the delay of around 9 days between infection and reporting of cases) which is essential for quick assessment of the effectiveness of interventions on reducing transmissibility. Our findings show that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models.

Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing

Digital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We develop a new framework that parameterizes disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we are able to accurately track the local effective reproduction number of COVID-19 in near real time (i.e., no longer constrained by the delay of around 9 days between infection and reporting of cases) which is essential for quick assessment of the effectiveness of interventions on reducing transmissibility. Our findings show that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models.

Digital proxies of human mobility can be used to monitor social distancing, and therefore have potential to infer COVID-19 dynamics. Here, the authors integrate travel card data from Hong Kong into a transmission model and show that it can be used to track transmissibility in near real-time.

Ethical Framework for Assessing Manual and Digital Contact Tracing for COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has challenged the traditional public health balance between benefiting the good of the community through contact tracing and restricting individual liberty. This article first analyzes important technical and ethical issues regarding new smartphone apps that facilitate contact tracing and exposure notification. It then presents a framework for assessing contact tracing, whether manual or digital: the effectiveness at mitigating the pandemic; acceptability of risks, particularly privacy; and equitable distribution of benefits and risks. Both manual and digital contact tracing require public trust, engagement of minority communities, prompt COVID-19 testing and return of results, and high adherence with physical distancing and use of masks.

Balancing public health and civil liberties in times of pandemic

The ongoing COVID-19 pandemic constitutes not only a danger for public health, but may also threaten civil liberties. Looking at the examples of recent events in Poland and Hungary, the authors argue that governments may misuse pandemic for their political advantage, thus endangering public health. Political decisions taken to stem the spread of pandemics should be limited and strictly proportionate to the situation.

Will the World Ever Be the Same After COVID-19? Two Lessons from the First Global Crisis of a Digital Age

Coronavirus is the first global crisis of a digital age and the divergence in policy responses reflects the challenge of navigating an unprecedented global situation under conditions of enormous uncertainty. We ask what lessons can be learned from this experience and identify two, both of which push against mainstream interpretations of recent events. First, and contrary to the view that the crisis exposed social media and Big Tech as a source of dangerous misinformation that needs to be regulated more strictly, the paper argues that the less mediated spaces of the Internet—social media and Twitter, in particular—played an essential role in triggering a more effective policy response based around social distancing, lockdown, and containment. Second, and contrary to the view that things will go back to normal once the worst of the crisis has passed, the paper argues that, as a direct result of lockdown, the status quo has been shifted across multiple sectors of the economy. Three examples of this shift are introduced, notably the forced experimentation with digital technologies in education and health, the increased use of remote work in many companies, and a reduction in environmentally harmful behavior and decrease in pollution levels. The long-term effects of this ‘reset’ are impossible to predict, but a quick return to the ‘old normal’ seems unlikely. The paper concludes with the suggestion that this reset has created a unique historical opportunity for the reappraisal of regulatory approaches across multiple domains and exposed the need for regulatory models better aligned to a less mediated, more decentralized world. COVID-19 is a global tragedy, but—given that it has happened—it should be used as a learning experience to re-imagine a better, more socially, and environmentally responsible future.

Ethics of Digital Mental Health During COVID-19: Crisis and Opportunities

Social distancing measures due to the COVID-19 pandemic have accelerated the adoption and implementation of digital mental health tools. Psychiatry and therapy sessions are being conducted via videoconferencing platforms, and the use of digital mental health tools for monitoring and treatment has grown. This rapid shift to telehealth during the pandemic has given added urgency to the ethical challenges presented by digital mental health tools. Regulatory standards have been relaxed to allow this shift to socially distanced mental health care. It is imperative to ensure that the implementation of digital mental health tools, especially in the context of this crisis, is guided by ethical principles and abides by professional codes of conduct. This paper examines key areas for an ethical path forward in this digital mental health revolution: privacy and data protection, safety and accountability, and access and fairness.

A socio-technical framework for digital contact tracing

In their efforts to tackle the COVID-19 crisis, decision makers are considering the development and use of smartphone applications for contact tracing. Even though these applications differ in technology and methods, there is an increasing concern about their implications for privacy and human rights. Here we propose a framework to evaluate their suitability in terms of impact on the users, employed technology and governance methods. We illustrate its usage with three applications, and with the European Data Protection Board (EDPB) guidelines, highlighting their limitations.

Artificial intelligence in COVID-19 drug repurposing

Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.

The Unintended Consequences of COVID-19 Mitigation Measures on Mass Transit and Car Use

As the world adapts to COVID-19, the transport behaviour of commuters has been greatly modified. Governments and transit authorities will need strong, well-received mitigation measures and education campaigns to maintain the historically upward trend of sustainable mass transit usage following this pandemic. This study, from a survey of 1968 Canadians in early May 2020, reveals that, following the end of stay-at-home orders, commuters intend to use their cars more and mass transit less. Driving these behavioural changes are commuters’ perceptions that mass transit use will negatively impact their health safety, peace of mind, and travel experience. The results also show that certain mitigation measures, such as more frequent cleaning and mandatory hand washing, are likely to reduce this decline, whereas e-monitoring and the use of health certificates will be detrimental to mass transit ridership through user perception. These results can help lessen the environmental impact of the public returning to work by encouraging their continued use of more environmentally friendly modes of transportation.