Contact Tracing Apps Research Articles

Effectiveness of contact tracing apps for SARS-CoV-2: an updated systematic review

Objective – To systematically review evidence on effectiveness of contact tracing apps (CTAs) for SARS-CoV-2 on epidemiological and clinical outcomes Design – Update of a systematic review (https://doi.org/10.1136/bmjopen-2021-050519) Data sources - EMBASE (OVID), MEDLINE (PubMed), BioRxiv, and MedRxiv were searched up to June 9th 2021 Study selection – Studies, empirical or model-based, assessing effect of CTAs for SARS-CoV-2 on quarantine rate, reproduction number (R), total number of infections, hospitalization, mortality, and other epidemiologically and clinically relevant outcomes, were eligible for inclusion. Data extraction – Empirical and model-based studies were both critically appraised based on dedicated quality and risk of bias assessment checklists. Data on type of study (i.e., empirical or model-based), sample size, (simulated) time horizon, study population, CTA type (and associated interventions), comparator, and outcomes assessed, were extracted. Key findings were extracted and narratively summarized. Specifically for model-based studies, characteristics and values of important model parameters were collected. Results – 5123 studies were identified, of which 27 studies (five empirical, 22 model-based studies) were eligible and included in this review. All empirical studies were observational (non-randomized) studies and either at unclear or high risk of bias, mostly due to uncontrolled confounding. Risk of bias of model-based studies was considered high for 7 of 22 studies. Most studies demonstrated beneficial effects of CTAs on R, total number of infections, hospitalization, and mortality. Effect size was dependent on other model parameter values (e.g., proportion of asymptomatic individuals, testing delays), but in general a beneficial effect was observed at CTA adoption rates of 20% and over. Conclusions – CTAs are potentially effective at reducing SARS-CoV-2 related epidemiological and clinical outcomes, though effect size depends on other model parameter values. Methodologically sound comparative empirical studies on effectiveness of CTAs are lacking and would be desirable to confirm findings from model-based studies.

A Cluster Analysis of the Acceptance of a Contact Tracing App-The Identification of Profiles for the Italian Immuni Contact Tracing App.

Digital contact tracing apps have been introduced by governments as a strategy to limit the spread of the COVID-19 pandemic. Digital contact tracking is an alternative to traditional contact tracing performed by human tracers who have to reconstruct each contact an infected person had in the recent past by means of interviews. The Italian government proposed the Immuni digital contact tracking app as a solution. Immuni uses Bluetooth technology to anonymously register all close contacts a person had: if she tests positive for COVID-19 then all registered contacts are notified. The main aim of the paper is to propose a cluster analysis of some factors concerning the possible acceptance of the Immuni app to build behaviour profiles that explain and predict the possible behaviours of the respondents. The factors considered referred to three different pillars: the technological pillar, investigated by considering factors from the technology acceptance models family; the health pillar, where variables of the health belief model were used; and the sociopolitical pillar, where some values of the respondents were considered as possible barriers to or facilitators of the acceptance of this technology. As a result of the cluster analysis, three behavioural profiles were built: the ProApp profile, the Hesitant profile, and the AntiApp profile. The first is the profile grouping the respondents who intend to use the contact tracing app; the second is more about people who are favourable of the use of the app, but some issues such as privacy reduce the strength of their intention; the last profile is about people who are less favourable to use the app. We are confident that the behaviour profiles found would be useful to build more tailored communication campaigns to help promote the use of the app by managing factors that could either be facilitators or barriers.

Effectiveness of Ehteraz digital contact tracing app versus conventional contact tracing in managing the outbreak of COVID-19 in the State of Qatar

IntroductionContact tracing is an integral component of the overall public health strategy for controlling the outbreak of infectious diseases such as COVID-19. In addition to the conventional contact tracing, Qatar...

Optimizing the mitigation of epidemic spreading through targeted adoption of contact tracing apps

The ongoing COVID-19 pandemic is the first epidemic in human history in which digital contact tracing has been deployed at a global scale. Tracking and quarantining all the contacts of individuals who test positive for a virus can help slow down an epidemic, but the impact of contact tracing is severely limited by the generally low adoption of contact-tracing apps in the population. We derive here an analytical expression for the effectiveness of contact-tracing app installation strategies in a susceptible-infected-recovered (SIR) model on a given contact graph. We propose a decentralized heuristic to improve the effectiveness of contact tracing under fixed adoption rates, which targets a set of individuals to install contact-tracing apps and can be easily implemented. Simulations on a large number of real-world contact networks confirm that this heuristic represents a feasible alternative to the current state of the art.

What Users Think of COVID-19 Contact-Tracing Apps: An Analysis of Eight European Apps

We mine insights from the user reviews of COVID-19 contact-tracing apps from eight European countries to determine what end users think of the apps and the main problems that have been reported.

Engineering Mobile Apps for Disaster Management: The Case of COVID-19 Apps in the Google Play Store

Several mobile apps have been released to the public in response to the COVID-19 pandemic. The majority of these apps are developed under a tight schedule, with immense social and political pressure. This pressure can lead to malfunctions with serious consequences.

ABAFOR: A Blockchain-based Privacy-Preserving Architecture for Efficient Contact Tracing and GIS Analysis

The Coronavirus disease 2019 has manifested into a global pandemic spreading into almost all the countries and territories in the world. Contact tracing, followed by testing and isolation, have been identified as important tools in containing the proliferation of the disease. Because of manual contact tracing limitations, smartphone apps for digital contact tracing have been deployed by authorities in multiple countries. However, many of these apps have faced criticism because of interoperability, privacy and security issues. This paper proposes an open architecture based on blockchain technology that addresses some of these criticisms. It enables interoperability through a publicly-readable consortium blockchain database, preserving privacy by distributing users’ partial identities among multiple decentralised authorities and providing security through digital signature and asymmetric key encryption. In addition, the architecture provides a fast proof-of-authority based consensus algorithm using reputation as a stake to add and validate blocks on the blockchain efficiently and a swift contact tracing algorithm using Spatio-temporal and key-valued databases.

COVID-19 and the fear of other unknowns: challenges and lessons learned from a digital contact tracing activity in the Rohingya camps in Cox’s Bazar, Bangladesh

Contact tracing can play an important role in controlling infectious disease outbreaks such as the COVID-19 pandemic. Containing the spread of COVID-19 is crucial in humanitarian settings such as in the Rohingya camps in Cox’s Bazar, Bangladesh. This manuscript describes the COVID-19 contact tracing activities undertaken by a group of researchers and implementers in Cox’s Bazar, Bangladesh. The paper details the design and development of the Commcare ‘Contact tracing and case monitoring app’, subsequent implementation of the contact tracing activity, challenges faced during the implementation process, and the strategies adopted by the research team to overcome these challenges. The research team leveraged the suite of template applications for COVID-19 response developed by Dimagi in response to the COVID-19 pandemic. Research partners organized a series of brainstorming meetings and workshops with relevant stakeholders to finalize the ‘COVID- 19 contact tracing and case monitoring app’ for final implementation. This app was implemented in 10 Rohingya camps from Ukhiya and Teknaf sub-districts of Cox’s Bazar for 4.5 months from 1st January 2021 to 15th May 2021. Due to a restriction on internet availability in the Rohingya camps by the government of the host country, the research team had to adopt a manual approach to implement the contact tracing activity. During these 4.5 months, 249,452 individuals from 10 Rohingya camps were screened for COVID-19 case registration. Of all the screened individuals, 431 were identified as COVID suspected cases, and 77 were identified as confirmed cases. The research team experienced several implementation challenges such as inexperience of contact tracers with the nature of the work, convincing the community to register in a digital system, obtaining information around COVID-19 symptoms, and many cultural, linguistic, gender, and other social barriers. The team adopted challenge-specific mitigation strategies for the effective implementation of the activity. The modalities of operation adopted by the team engaged with this present intervention to overcome the difficulties experienced in its conduction can hopefully provide some guidance to future parties attempting to conduct similar activities in complex humanitarian settings.

Cross-sectional study on COVID-19 vaccine hesitancy and determinants in healthcare students: interdisciplinary trainings on vaccination are needed

BackgroundTo ensure the success of COVID-19 vaccination, public authorities need to have the support of the entire population and build vaccine confidence. Identifying and understanding the determinants of vaccine acceptance is essential for conducting vaccine strategy. The aim was to estimate vaccine hesitancy among healthcare students in France and to investigate the associated factors.MethodsA web-based cross-sectional study was conducted in a large French University in greater Paris area, among 4927 healthcare students from the different training courses such as medicine studies, midwifery studies, physiotherapy studies, nurse studies and others health studies. The study was conducted between January 21 and February 8, 2021 based on a questionnaire including 25 single or multiple-choice questions, made using the free software Limesurvey. The link of the questionnaire was distributed to the students by the teachers and the student associations. The SAGE group definition of vaccine hesitancy was used. All estimates were weighted using the gender and training courses category of all healthcare students registered for the 2020–2021 year. Crude and adjusted weighted odds ratio (wOR) and 95% confidence interval (95%CI) were estimated using logistic regression.ResultsA total of 1465 healthcare students answered. A proportion of 44.5% (95%CI = [41.7–47.3]) of them were considered as hesitant. Women were more hesitant (50.9, 95%CI = [48.0–53.9]) than men (21.6, 95%CI = [15.2–28.0]). Vaccine hesitancy was significantly associated with gender (wOR = 0.27, 95%CI = [0.18–0.39]) and training courses: medical students were less likely to be hesitant than students in the common and first year of several health studies (wOR = 0.48, 95%CI = [0.33–0.70]) while nursing students were more than 5 times more likely to be hesitant (wOR = 5.20, 95%CI = [3.71–7.28]). Students who did an internship during the epidemic (wOR = 0.53, 95%CI = [0.41–0.69]) and who downloaded the mobile contact-tracing mobile app “TousAntiCovid” (wOR = 0.34, 95%CI = [0.26–0.44]) were significantly less likely to be hesitant.ConclusionsOverall vaccine hesitancy among healthcare students was high, substantial differences were found between training courses. To reduce these disparities, interdisciplinary lectures on vaccines for all healthcare students may be implemented and evaluated.

Resistance to contact tracing applications: The implementation process in a social context

The success of new technologies such as contact tracing mobile applications depends on large-scale end-user adoption. However, the implementation may encounter resistance, since the uncertainty surrounding novel technology may raise anxiety, and persuasion efforts to promote use can evoke reactance. Thereby, anxiety and reactance are two forms of resistance to new technology. Little is known about the role of resistance over the course of the innovation implementation process, in a social environment where technology functionality depends on adoption by others. Therefore, this four-wave longitudinal study followed adoption of the Dutch COVID-19 contact tracing app during four months (N = 1120), and explored the time dynamics and interplay of reactance to freedom threat, anxiety, and perceived social norms on app use. Mixed-effect analyses showed that anxiety and, subtly, reactance decreased with time; initial freedom threat predicted later reactance. App use related negatively to reactance and anxiety; and positively to positive social norms. Over time, the norm effect was mediated by lower reactance and anxiety. The results imply that resistance is pervasive, suggest that self-perceived app use norms may be key to overcoming resistance to new applications, and demonstrate that theories predicting innovation or technology acceptance benefit from studying predictors over time.

A Layered Adopter-Structure Model for the Download of COVID-19 Contact Tracing Apps: A System Dynamics Study for mHealth Penetration.

(1) Background: Contact tracing and notification apps for coronavirus disease 2019 (COVID-19) are installed on smartphones and are intended to detect contact with another person’s device. A high installation rate is important for these apps to enable them to be effective countermeasures against the silent transmission of diseases. However, the installation rate varies among apps and regions and the penetration dynamics of these applications are unclear. (2) Methods: The download behavior of contact tracing applications was investigated using publicly available datasets. The increase in downloads was modeled using a system dynamics model derived from the product growth model. (3) Results: The imitation effects present in the traditional product growth model were not observed in COVID-19 contact tracing apps. The system dynamics model, without the imitation effect, identified the downloads of the Australian COVIDSafe app. The system dynamics model, with a layered adopter, identified the downloads of the Japanese tracing app COCOA. The spread of COVID-19 and overall anti-COVID-19 government intervention measures in response to the spread of infection seemed to result in an increase in downloads. (4) Discussion: The suggested layered structure of users implied that individualized promotion for each layer was important. Addressing the issues among users who are skeptical about adoption is pertinent for optimal penetration of the apps.

Privacy-preserving COVID-19 contact tracing solution based on blockchain

The COVID-19 pandemic has severely affected daily life and caused a great loss to the global economy. Due to the very urgent need for identifying close contacts of confirmed patients in the current situation, the development of automated contact tracing app for smart devices has attracted more attention all over the world. Compared with expensive manual tracing approach, automated contact tracing apps can offer fast and precise tracing service, however, over-pursing high efficiency would lead to the privacy-leaking issue for app users.By combing with the benign properties (e.g., anonymity, decentralization, and traceability) of blockchain, we propose an efficient privacy-preserving solution in automated tracing scenario. Our main technique is a combination of non-interactive zero-knowledge proof and multi-signature with public key aggregation. By means of aggregating multiple signatures from different contacts at the mutual commitment phase, we only need fewer zero-knowledge proofs to complete the task of identifying contacts. It inherently leads to the benefits of saving storage and consuming less time for running verification algorithm on blockchain. Furthermore, we perform an experimental comparison by timing the execution of signature verification with and without aggregate signature, respectively. It shows that our solution can actually preserve the full-fledged privacy protection property with a lower computational cost.

In times of crisis: Public perceptions toward COVID-19 contact tracing apps in China, Germany, and the United States

The adoption of COVID-19 contact tracing apps (CTAs) has been proposed as an important measure to contain the spread of COVID-19. Based on a cross-national dataset, this article analyzes public perceptions toward CTAs and the factors that drive CTA acceptance in China, Germany, and the United States. We find that public acceptance of CTAs is significantly higher in China as compared with Germany and the United States. Despite very different sociopolitical contexts, there are striking similarities in the factors that drive CTA acceptance in all three countries. Citizens are willing to accept digital contact tracing despite concerns about privacy infringement and government surveillance, as long as the apps are perceived as effective in lowering infection rates and providing health information. This creates a chicken-and-egg problem for CTAs in Germany and the United States where CTAs are voluntary: a high citizen adoption rate is necessary for CTAs to be effective, but CTAs are only effective if adoption rates are high.

DIMY: Enabling privacy-preserving contact tracing

The infection rate of COVID-19 and the rapid mutation ability of the virus has forced governments and health authorities to adopt lockdowns, increased testing, and contact tracing to reduce the virus’s spread. Digital contact tracing has become a supplement to the traditional manual contact tracing process. However, although several digital contact tracing apps are proposed and deployed, these have not been widely adopted due to apprehensions surrounding privacy and security. In this paper, we present a blockchain-based privacy-preserving contact tracing protocol,“Did I Meet You” (DIMY). The protocol provides full-lifecycle data privacy protection on the devices as well as the back-end servers to address most of the privacy concerns associated with existing protocols. We have employed Bloom filters to provide efficient privacy-preserving storage and have used the Diffie–Hellman key exchange for secret sharing among the participants. We show that DIMY provides resilience against many well-known attacks while introducing negligible overheads. DIMY’s footprint on the storage space of clients’ devices and back-end servers is also significantly lower than other similar state-of-the-art apps.

How Good is Good Enough? Quantifying the Impact of Benefits, Accuracy, and Privacy on Willingness to Adopt COVID-19 Decision Aids

An increasing number of data-driven decision aids are being developed to provide humans with advice to improve decision-making around important issues such as personal health and criminal justice. For algorithmic systems to support human decision-making effectively, people must be willing to use them. Yet, prior work suggests that accuracy and privacy concerns may both deter potential users and limit the efficacy of these systems.We expand upon prior research by empirically modeling how accuracy and privacy influence intent to adopt algorithmic systems. We focus on an algorithmic system designed to aid people in a globally-relevant decision context with tangible consequences: the COVID-19 pandemic. We use statistical techniques to analyze surveys of 4,615 Americans to (1) evaluate the effect of both accuracy and privacy concerns on reported willingness to install COVID-19 apps; (2) examine how different groups of users weigh accuracy relative to privacy; and (3) we empirically develop the first statistical models, to our knowledge, of how theamountof benefit (e.g., error rate) anddegreeof privacy risk in a data-driven decision aid may influence willingness to adopt.

Leveraging Polio Geographic Information System Platforms in the African Region for Mitigating COVID-19 Contact Tracing and Surveillance Challenges: Viewpoint.

BackgroundThe ongoing COVID-19 pandemic in Africa is an urgent public health crisis. Estimated models projected over 150,000 deaths and 4,600,000 hospitalizations in the first year of the disease in the absence of adequate interventions. Therefore, electronic contact tracing and surveillance have critical roles in decreasing COVID-19 transmission; yet, if not conducted properly, these methods can rapidly become a bottleneck for synchronized data collection, case detection, and case management. While the continent is currently reporting relatively low COVID-19 cases, digitized contact tracing mechanisms and surveillance reporting are necessary for standardizing real-time reporting of new chains of infection in order to quickly reverse growing trends and halt the pandemic.ObjectiveThis paper aims to describe a COVID-19 contact tracing smartphone app that includes health facility surveillance with a real-time visualization platform. The app was developed by the AFRO (African Regional Office) GIS (geographic information system) Center, in collaboration with the World Health Organization (WHO) emergency preparedness and response team. The app was developed through the expertise and experience gained from numerous digital apps that had been developed for polio surveillance and immunization via the WHO’s polio program in the African region.MethodsWe repurposed the GIS infrastructures of the polio program and the database structure that relies on mobile data collection that is built on the Open Data Kit. We harnessed the technology for visualization of real-time COVID-19 data using dynamic dashboards built on Power BI, ArcGIS Online, and Tableau. The contact tracing app was developed with the pragmatic considerations of COVID-19 peculiarities. The app underwent testing by field surveillance colleagues to meet the requirements of linking contacts to cases and monitoring chains of transmission. The health facility surveillance app was developed from the knowledge and assessment of models of surveillance at the health facility level for other diseases of public health importance. The Integrated Supportive Supervision app was added as an appendage to the pre-existing paper-based surveillance form. These two mobile apps collected information on cases and contact tracing, alongside alert information on COVID-19 reports at the health facility level; the information was linked to visualization platforms in order to enable actionable insights.ResultsThe contact tracing app and platform were piloted between April and June 2020; they were then put to use in Zimbabwe, Benin, Cameroon, Uganda, Nigeria, and South Sudan, and their use has generated some palpable successes with respect to COVID-19 surveillance. However, the COVID-19 health facility–based surveillance app has been used more extensively, as it has been used in 27 countries in the region.ConclusionsIn light of the above information, this paper was written to give an overview of the app and visualization platform development, app and platform deployment, ease of replicability, and preliminary outcome evaluation of their use in the field. From a regional perspective, integration of contact tracing and surveillance data into one platform provides the AFRO with a more accurate method of monitoring countries’ efforts in their response to COVID-19, while guiding public health decisions and the assessment of risk of COVID-19.

Exploring privacy paradox in contact tracing apps adoption

PurposeUnderstanding the privacy concerns of individuals in the adoption of contact tracing apps is critical for the successful control of pandemics like COVID-19. This paper explores the privacy paradox in the adoption of contact tracing apps in Australia.Design/methodology/approachA comprehensive review of the related literature has been conducted, leading to the development of a conceptual model based on the privacy calculus theory and the antecedent-privacy concern-outcome framework. Such a model is then tested and validated using structural equation modelling on the survey data collected in Australia.FindingsThe study shows that perceived benefit, perceived privacy risk and trust have significant influences on the adoption of contact tracing apps. It reveals that personal innovativeness and trust have significant and negative influences on perceived privacy risk. The study further finds out that personal innovativeness is insignificant to perceived benefit. It states that perceived ease of use has an insignificant influence on perceived privacy risk in the adoption of contact tracing apps.Originality/valueThis study is the first attempt to use the privacy calculus theory and the antecedent–privacy concern–outcome framework for exploring the privacy paradox in adopting contact tracing apps. This leads to a better understanding of the privacy concerns of individuals in the adoption of contact tracing apps. Such an understanding can help formulate targeted strategies and policies for promoting the adoption of contact tracing apps and inform future epidemic control through effective contact tracing for better emergency management.

To disclose or not to disclose? Factors related to the willingness to disclose information to a COVID-19 tracing app

ABSTRACT Contact-tracing apps have been identified as a promising technology to curb the spread of COVID-19. To be effective, a sufficient number of individuals need to install the app and disclose information like COVID-19 infection to such an app. Yet, usage data demonstrate that a large number of app users does not disclose COVID-19 infection to the app. Hence, in two studies (overall N = 1522), we investigate factors related to individuals’ willingness to actively disclose information to such an app. In a preregistered online experiment conducted two months before the app launch onto the German market, we find that disclosure willingness increases when the app’s prosocial benefit or a social-life-enabling benefit is emphasized (vs. no benefit emphasized). In a subsequent, quota-representative survey study conducted two months after the app launch onto the German market, we adapted and extended the Technology Acceptance Model 2 (TAM2) to the context of prosocial information sharing in tracing apps. We find that the perceived prosocial benefit of the app, trust in public institutions, and fear of COVID-19 are the relevant predictors. Moreover, we demonstrate that the relation between perceived prosocial benefit and disclosure willingness is moderated by perceived ease of use. Results are discussed with regard to effective implementation and communication strategies for tracing apps, and the general role of prosocial concerns for technology usage to address major societal challenges.

Digital Contact Tracing Apps for COVID-19: Development of a Citizen-Centered Evaluation Framework.

BackgroundThe silent transmission of COVID-19 has led to an exponential growth of fatal infections. With over 4 million deaths worldwide, the need to control and stem transmission has never been more critical. New COVID-19 vaccines offer hope. However, administration timelines, long-term protection, and effectiveness against potential variants are still unknown. In this context, contact tracing and digital contact tracing apps (CTAs) continue to offer a mechanism to help contain transmission, keep people safe, and help kickstart economies. However, CTAs must address a wide range of often conflicting concerns, which make their development/evolution complex. For example, the app must preserve citizens’ privacy while gleaning their close contacts and as much epidemiological information as possible.ObjectiveIn this study, we derived a compare-and-contrast evaluative framework for CTAs that integrates and expands upon existing works in this domain, with a particular focus on citizen adoption; we call this framework the Citizen-Focused Compare-and-Contrast Evaluation Framework (C3EF) for CTAs.MethodsThe framework was derived using an iterative approach. First, we reviewed the literature on CTAs and mobile health app evaluations, from which we derived a preliminary set of attributes and organizing pillars. These attributes and the probing questions that we formulated were iteratively validated, augmented, and refined by applying the provisional framework against a selection of CTAs. Each framework pillar was then subjected to internal cross-team scrutiny, where domain experts cross-checked sufficiency, relevancy, specificity, and nonredundancy of the attributes, and their organization in pillars. The consolidated framework was further validated on the selected CTAs to create a finalized version of C3EF for CTAs, which we offer in this paper.ResultsThe final framework presents seven pillars exploring issues related to CTA design, adoption, and use: (General) Characteristics, Usability, Data Protection, Effectiveness, Transparency, Technical Performance, and Citizen Autonomy. The pillars encompass attributes, subattributes, and a set of illustrative questions (with associated example answers) to support app design, evaluation, and evolution. An online version of the framework has been made available to developers, health authorities, and others interested in assessing CTAs.ConclusionsOur CTA framework provides a holistic compare-and-contrast tool that supports the work of decision-makers in the development and evolution of CTAs for citizens. This framework supports reflection on design decisions to better understand and optimize the design compromises in play when evolving current CTAs for increased public adoption. We intend this framework to serve as a foundation for other researchers to build on and extend as the technology matures and new CTAs become available.

COVID Alert: Factors Influencing the Adoption of Exposure Notification Apps Among Canadian Residents.

The continued emergence of new variants of COVID-19 such as the Delta and Omicron variants, which can cause breakthrough infections, indicates that contact tracing and exposure notification apps (ENAs) will continue to be useful for the long haul. However, there is limited work to uncover the strongest factors that influence their adoption. Using Canada's “COVID Alert” as a case study, we conducted an empirical, technology-acceptance study to investigate the key factors that account for users' intention to use ENAs and the moderating effect of important human and design factors. Our path model analysis shows that four factors significantly influence the adoption of COVID Alert among Canadian residents: perceived risk, perceived usefulness, perceived trust, and perceived compatibility. The overall model explains over 60% of intention to use, with type of design, use case (functional interface), and adoption status moderating the strength of the relationships between the four factors and intention to use. We discuss these findings and make recommendations for the design of future ENAs.