Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study.
Samuel Altmann,Luke Milsom,Hannah Zillessen,Raffaele Blasone,Frederic Gerdon,Ruben L. Bach,Frauke Kreuter,Frauke Kreuter,Daniele Nosenzo,Séverine Toussaert,Johannes Abeler +10 more
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TLDR
Investigation of the user acceptability of a contact-tracing app in five countries hit by the COVID-19 pandemic found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level CO VID-19 mortality rates.Abstract:
Background: The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs. One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19. Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention.
Objective: The objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic.
Methods: We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections. We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States. We measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries.
Results: We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption.
Conclusions: Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19.read more
Citations
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Journal ArticleDOI
I use a COVID-19 contact-tracing app. Do you? Regulatory focus and the intention to engage with contact-tracing technology
TL;DR: In this paper, the intention to use COVID-19 contact tracing apps and goal-directed motivation was found to be positively associated with regulatory focus, whereas the relationship between prevention focus and apps usage intention was mediated by privacy and information security concerns.
Journal ArticleDOI
App Use and Usability of a Barcode-Based Digital Platform to Augment COVID-19 Contact Tracing: Postpilot Survey and Paradata Analysis.
TL;DR: MyCOVIDKey as mentioned in this paper is a mobile-based web application to assist COVID-19 contact tracing efforts during a 6-week pilot period, which was conducted on the Vanderbilt University campus in Nashville, Tennessee.
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Investigating factors that affect the adoption of Covid-19 contact-tracing apps. A privacy calculus perspective
TL;DR: In this article, the authors investigated the relative importance of privacy concerns and perceived benefits in relation to the decision to use the app adopting a privacy calculus perspective extended by trust in app privacy and technological knowledge about the app.
Journal ArticleDOI
Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand
Ardvin Kester S. Ong,Yogi Tri Prasetyo,Nattakit Yuduang,Reny Nadlifatin,Satria Fadil Persada,Kirstien Paola E. Robas,Thanatorn Chuenyindee,Thapanat Buaphiban +7 more
TL;DR: In this paper , a study aimed to explore factors affecting the actual use (AU) of the application through the use of machine learning algorithms (MLA) such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN).
Proceedings ArticleDOI
The Rise and Fall of COVID-19 Contact-Tracing Apps: when NFRs Collide with Pandemic
TL;DR: In this paper, the authors provide a contextual perspective of the government commissioned contact-tracing apps from four countries to understand the non-functional requirements (NFRs) and socio-technical factors that hindered the success of these apps.
References
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Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand
Neil M. Ferguson,Daniel J Laydon,G Nedjati Gilani,Natsuko Imai,Kylie E. C. Ainslie,Marc Baguelin,Sangeeta N. Bhatia,A Boonyasiri,Z Cucunuba Perez,Gina Cuomo-Dannenburg,Amy Dighe,Ilaria Dorigatti,Han Fu,Katy A. M. Gaythorpe,W Green,Arran Hamlet,Wes Hinsley,Lucy C Okell,S Van Elsland,H Thompson,Robert Verity,Erik M. Volz,Haowei Wang,Y Wang,Patrick G T Walker,Caroline E. Walters,Peter Winskill,Charles Whittaker,Christl A. Donnelly,Steven Riley,Azra C. Ghani +30 more
TL;DR: Neil M Ferguson, Daniel Laydon, Gemma Nedjati-Gilani, Natsuko Imai, Kylie Ainslie, Sangeeta Bhatia, Adhiratha Boonyasiri, Zulma Cucunubá, Gina Cuomo-Dannenburg, Amy Dighe, Ilaria Dorigatti, Han Fu, Katy Gaythorpe, Will Green, Arran Hamlet, Wes Hinsley, Lucy C Okell.
Journal ArticleDOI
Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing.
Luca Ferretti,Chris Wymant,Michelle Kendall,Lele Zhao,Anel Nurtay,Lucie Abeler-Dörner,Michael Parker,David Bonsall,Christophe Fraser +8 more
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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