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Covid-19 Tracking: Germany 1 COVID-19 TRACKING: GERMANY 1 1 Psychological Factors Shaping Public Responses to COVID-19 Digital Contact 2 Tracing Technologies in Germany 1* 1 2,3 4 3 Anastasia Kozyreva , Philipp Lorenz-Spreen , Stephan Lewandowsky , Paul M. Garrett , 1 1 1 4 Stefan M. Herzog , Thorsten Pachur , and Ralph Hertwig 1 5 Center for Adaptive Rationality, Max Planck Institute for Human Development, Germany 2 6 School of Psychological Science, University of Bristol, United Kingdom 3 7 School of Psychological Sciences, University of Western Australia 4 8 Melbourne School of Psychological Sciences, University of Melbourne, Australia * 9 Corresponding author, E-mail: [email protected] COVID-19 TRACKING: GERMANY 2 10 Abstract 11 The COVID-19 pandemic has seen one of the first large-scale uses of digital contact tracing 12 to track a chain of infection and contain the spread of a virus. The new technology has 13 posed challenges both for governments aiming at high and effective uptake and for citizens 14 weighing its benefits (e.g., protecting others’ health) against the potential risks (e.g., loss of 15 data privacy). Our cross-sectional survey with repeated measures across four samples in 16 Germany (N = 4, 357) focused on psychological factors contributing to the public adoption 17 of digital contact tracing. We found that public acceptance of privacy-encroaching 18 measures (e.g., granting the government emergency access to people’s medical records or 19 location tracking data) decreased over the course of the pandemic. Intentions to use 20 contact tracing apps—hypothetical ones or the Corona-Warn-App launched in Germany in 21 June 2020—were high. Users and non-users of the Corona-Warn-App differed in their 22 assessment of its risks and benefits, in their knowledge of the underlying technology, and in 23 their reasons to download or not to download the app. Trust in the app’s perceived 24 security and belief in its effectiveness emerged as psychological factors playing a key role in 25 its adoption. We incorporate our findings into a behavioral framework for digital contact 26 tracing and provide policy recommendations. 27 Keywords: COVID-19 | digital contact tracing | privacy | public attitudes | 28 Corona-Warn-App COVID-19 TRACKING: GERMANY 3 29 Psychological Factors Shaping Public Responses to COVID-19 Digital Contact 30 Tracing Technologies in Germany 31 Public health interventions, economic aid, and behavioral regulations have all been enlisted 32 to curb the damage of the COVID-19 pandemic (Habersaat et al., 2020; World Health 33 Organization, 2020). Before vaccines became available, behavioral measures—restricting 34 social mixing and public gatherings, tracing the contacts of those infected, and 35 implementing a combination of physical distancing rules and hygiene measures (Robert 36 Koch Institute, 2020)—were the most promising way to contain the pandemic. 37 Technological solutions have also helped stem the spread of COVID-19 (Grantz et al., 2020; 38 Oliver et al., 2020). Indeed, with the exception of the Ebola outbreak in West Africa in 39 2014–2016 (Danquah et al., 2019), the COVID-19 pandemic has seen the first large-scale 40 use of digital contact tracing for epidemiological purposes (Kahn & Johns Hopkins Project 41 on Ethics and Governance of Digital Contact Tracing Technologies, 2020). This study 42 examines the psychological factors that have contributed to the adoption of tracking apps 43 during the COVID-19 pandemic. 44 Smartphone tracking apps use GPS, telecommunication, or Bluetooth data to create a 45 list of contacts with whom a user may have been co-located (Oliver et al., 2020). This 46 contact information is stored locally on the phone or on a centralized server. If a person 47 later tests positive for COVID-19 and shares their infection status with an app, all users in 48 their contact list can be notified instantly, allowing them to self-isolate and get tested, thus 49 ideally helping to slow the spread of the virus (Ferretti et al., 2020). 50 To date, about 50 countries have introduced COVID-19 contact tracing apps; most use 51 Bluetooth tracking technologies (O’Neill et al., 2020). The Corona-Warn-App, launched in 52 Germany in June 2020, is an open-source Bluetooth-based decentralized smartphone app 53 (https://www.coronawarn.app/en) that aims to ease the burden of the pandemic on local 54 public health authorities by complementing their offline contact tracing efforts. The app 55 employs a privacy-preserving model, collecting anonymized contact data that are stored COVID-19 TRACKING: GERMANY 4 56 locally on the user’s smartphone. Like Spain’s Radar COVID app and the United 57 Kingdom’s NHS COVID-19 app, the Corona-Warn-App is based on the Exposure 58 Notification system developed by Google and Apple. 59 The epidemiological impact of digital contact tracing apps remains low (Burdinski 60 et al., 2021) or uncertain (although the NHS app has been reported to have a positive 61 impact; Wymant et al., 2021): No country has been able to prevent more widespread 62 outbreaks without implementing harsher restrictions. Most studies agree that higher 63 participation rates are needed to increase the efficacy of digital contact tracing. In most 64 countries, the uptake of digital contact tracing apps has not reached the target of 60% (see 65 Appendix Figure A1 and Table A1), a critical threshold derived from early simulation 66 models (Hinch et al., 2020; Whitelaw et al., 2020). However, more recent simulation studies 67 suggest that even levels of adoption above 20% can have a mitigating impact (Aleta et al., 68 2020; Bianconi et al., 2021); recent evidence suggests this to be the case in the United 69 Kingdom (Wymant et al., 2021). It is therefore crucial to understand the reasons 70 underlying people’s decision to download and use the app. Only then will it be possible to 71 develop measures that can help to make digital contact tracing an effective long-term 72 epidemiological measure (Colizza et al., 2021). 73 The effectiveness of digital contact tracing technologies depends on a combination of 74 related but distinct factors (see Figure1 for a graphical representation; see also Colizza 75 et al., 2021; Rodríguez et al., 2021), including (1) functionality: the app’s architecture 76 (e.g., which protocol or exposure notification system it uses) and its privacy and risk 77 models; (2) integration: how the app is integrated into a larger environment such as the 78 public health system and how test results are uploaded (e.g., via QR codes); (3) 79 communication: including how the risks and benefits of the app are communicated to the 80 public; (4) usage: including adoption of the technology (number of downloads), continuous 81 and correct use of the app (e.g., keeping it installed and keeping Bluetooth on), and 82 compliance (e.g., willingness and ability to upload test results); (5) detection: including key COVID-19 TRACKING: GERMANY 5 83 effectiveness metrics such as the number of positive test results uploaded as a proportion of 84 all clinically diagnosed infections in the population, the app’s overall detection rate (i.e., 85 the proportion of an infected person’s contacts who are notified by the app—including 86 contacts unknown to the infected individual), and detection accuracy (i.e., the proportion 87 of detected contacts that is free from both false positives and false negatives; see Redmiles, 88 2020); and (6) response: complying with risk warnings and taking appropriate measures 89 (e.g., taking a test or self-isolating) following risk exposure notification. Digital Contact Tracing Effectiveness - App’s architecture Integration within - Public - Adoption (downloads) - positive test results - Follow-up - App’s privacy the local health communication - Adherence uploaded behavior (testing, model system, including - Marketing (continuous usage) - N of close and self-isolation) - App’s risk model testing and campaigns - Compliance hidden contacts uploading of test - Media coverage (uploading test results) traced results - Accuracy and speed FUNCTIONALITY INTEGRATION COMMUNICATION USAGE DETECTION RESPONSE Figure 1: Factors contributing to the effectiveness of digital contact tracing technologies. Expanded based on the analysis by Rodríguez et al. (2021). 90 Here, we focus on the adoption of digital contact tracing technologies. Numerous 91 factors can influence people’s decision to download and use an app. For instance, a study 92 in Germany revealed higher adoption rates of the Corona-Warn-App among respondents 93 with a higher risk of severe illness, respondents who follow behavioral guidelines (e.g., 94 wearing a mask), and respondents who trust the national government, the healthcare 95 system, and science in general (Munzert et al., 2021). A study from France likewise found 96 that higher trust in government is associated with higher acceptability and increased use of 97 contact tracing apps (Guillon & Kergall, 2020); similar findings have been reported for the 98 United Kingdom (Lewandowsky et al., 2021). 99 A U.S. study on the willingness to adopt warning apps has shown, using hypothetical 100 scenarios, that people consider both the risks and the benefits of such technologies 101 (Redmiles, 2020). Benefits include knowing about one’s risk exposure, feeling altruistic, COVID-19 TRACKING: GERMANY 6 102 and protecting others; risks include privacy costs and costs of mobile data use. Another 103 U.S. study found that people value both accuracy and privacy in a tracking app (Kaptchuk 104 et al., 2020). An international study likewise highlighted the importance of privacy 105 concerns, finding that 37% of participants would not download an app even if it protected 106 their data perfectly (Simko et al., 2020). At the same time, studies in Australia (Garrett, 107 White, et al., 2021), the United Kingdom (Lewandowsky et al., 2021), and among young 108 adults in Taiwan (Garrett, Wang, et al., 2021) have shown high acceptance of potential 109 tracking technologies, especially in the presence of privacy-preserving conditions.
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