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Barriers to the Large-Scale Adoption of a COVID-19 Contact Tracing App in Germany: Survey Study.

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TLDR
In this paper, the authors analyzed potential barriers to the large-scale adoption of the official contact tracing app that was introduced in Germany on June 16, 2020 and found that 81% of the population aged 18 to 77 years possess the devices and ability to install the official app and that 35% are also willing to install and use it.
Abstract
Background: During the COVID-19 pandemic, one way to reduce further transmissions of SARS-CoV-2 is the widespread use of contact tracing apps Such apps keep track of proximity contacts and warn contacts of persons who tested positive for an infection Objective: In this study, we analyzed potential barriers to the large-scale adoption of the official contact tracing app that was introduced in Germany on June 16, 2020 Methods: Survey data were collected from 3276 adults during the week the app was introduced using an offline-recruited, probability-based online panel of the general adult population in Germany Results: We estimate that 81% of the population aged 18 to 77 years possess the devices and ability to install the official app and that 35% are also willing to install and use it Potential spreaders show high access to devices required to install the app (92%) and high ability to install the app (91%) but low willingness (31%) to correctly adopt the app, whereas for vulnerable groups, the main barrier is access (62%) Conclusions: The findings suggest a pessimistic view on the effectiveness of app-based contact tracing to contain the COVID-19 pandemic We recommend targeting information campaigns at groups with a high potential to spread the virus but who are unwilling to install and correctly use the app, in particular men and those aged between 30 and 59 years In addition, vulnerable groups, in particular older individuals and those in lower-income households, may be provided with equipment and support to overcome their barriers to app adoption

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Journal ArticleDOI

The Dutch COVID-19 Contact Tracing App (the CoronaMelder): Usability Study.

TL;DR: CoronaMelder as mentioned in this paper is a COVID-19 contact tracing app based on a Google/Apple Exposure Notification approach and aims to combat the spread of the coronavirus among individuals by notifying those who are at increased risk of infection due to proximity to someone who later tests positive for COVID.
Journal ArticleDOI

Early Acceptability of a Mobile App for Contact Tracing During the COVID-19 Pandemic in France: National Web-Based Survey.

TL;DR: In this article, the authors evaluated the acceptability of a COVID-19 contact tracing mobile app among the French population and investigated the barriers to its use, finding that only 19.2% of all participants were app-supporting, whereas half of them (504/1003, 50.3%) were reluctant.
Journal ArticleDOI

Explaining citizens’ resistance to use digital contact tracing apps: A mixed-methods study

TL;DR: In this article , the authors explored the determinants and consequences of citizens' resistance to use digital contact tracing (DCT) apps using a sequential two-stage mixed-methods approach.
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Mining user reviews of COVID contact-tracing apps: An exploratory analysis of nine European apps

TL;DR: In this article , the authors used a commercial app-review analytics tool to extract and mine the user reviews of nine European national apps and found that users are generally dissatisfied with the nine apps under study.
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Hybrid harmony search algorithm for social network contact tracing of COVID-19.

TL;DR: In this article, a new approach using a hybrid harmony search (HHS) algorithm that casts the problem of finding strongly connected components (SCCs) to contact tracing is devised.
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TL;DR: A mathematical model for infectiousness was developed to estimate the basic reproductive number R0 and to quantify the contribution of different transmission routes and the requirements for successful contact tracing, and the combination of two key parameters needed to reduce R0 to less than 1 was determined.
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Second-Level Digital Divide: Differences in People's Online Skills

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