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. 2022 Mar 11;19(6):3323.
doi: 10.3390/ijerph19063323.

High Acceptance of COVID-19 Tracing Technologies in Taiwan: A Nationally Representative Survey Analysis

Paul M Garrett  1 , Yu-Wen Wang  2 , Joshua P White  1 , Yoshihsa Kashima  1 , Simon Dennis  1   3 , Cheng-Ta Yang  2   4
Affiliations

High Acceptance of COVID-19 Tracing Technologies in Taiwan: A Nationally Representative Survey Analysis

Paul M Garrett et al. Int J Environ Res Public Health. .

Abstract

Taiwan has been a world leader in controlling the spread of SARS-CoV-2 during the COVID-19 pandemic. Recently, the Taiwan Government launched its COVID-19 tracing app, 'Taiwan Social Distancing App'; however, the effectiveness of this tracing app depends on its acceptance and uptake among the general population. We measured the acceptance of three hypothetical tracing technologies (telecommunication network tracing, a government app, and the Apple and Google Bluetooth exposure notification system) in four nationally representative Taiwanese samples. Using Bayesian methods, we found a high acceptance of all three tracking technologies, with acceptance increasing with the inclusion of additional privacy measures. Modeling revealed that acceptance increased with the perceived technology benefits, trust in the providers' intent, data security and privacy measures, the level of ongoing control, and one's level of education. Acceptance decreased with data sensitivity perceptions and a perceived low policy compliance by others among the general public. We consider the policy implications of these results for Taiwan during the COVID-19 pandemic and in the future.

Keywords: COVID-19; SARS-CoV-2; Taiwan; contact tracing; health policy; privacy; privacy calculus; public health; tracking technologies.

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Conflict of interest statement

Author S.D. is the CEO of Unforgettable Research Services Pty Ltd. (URS), which specializes in providing privacy-preserving experience-sampling data collection and analysis services. While this did not influence the manuscript preparation, data collection, or sharing of our data and materials, we wish to declare this for the sake of full transparency. There are no patents, products in development or marketed products associated with this research to declare.

Figures

Figure 1
Figure 1
COVID-19 mobile tracing technologies, storage options, and the three tracing scenarios surveyed in the current study. A detailed description of each tracing scenario is presented in the supplementary materials.
Figure 2
Figure 2
(A) The survey order, the number of items in each item-block (right), and associated Likert response options and values (bottom). (B) A break-down of the key items in this paper. Additional acceptance items are presented in navy blue for those participants who responded ‘no’ to the scenario acceptance item.
Figure 3
Figure 3
Ordinal regression mean posterior distributions for items assessing perceptions of each hypothetical tracing technology. Colored error bars display the 95% highest posterior density interval (HDI), black dots display the posterior mean, and black lines display where HDIs do not overlap within an item. Dotted lines depict boundaries separating the latent space into ordinal responses (1 = none to 6 = extremely; 4 = moderate).
Figure 4
Figure 4
The acceptability and the conditional acceptability of each hypothetical tracing technology. Error bars are 95% Bayesian credible intervals. Highest density intervals within each technology do not overlap.
Figure 5
Figure 5
Bayesian generalized linear mixed effects model of tracing technology acceptance. Bars represent 50% of the parameter distribution centered on the parameter mean, tails display the 95% highest density interval. Opaque variables show instances where the posterior interval does not overlap zero.
See this image and copyright information in PMC

References

    1. Wu W.-K., Liou J.-M., Hsu C.-C., Lin Y.-H., Wu M.-S. Pandemic preparedness in Taiwan. Nat. Biotechnol. 2020;38:932–933. doi: 10.1038/s41587-020-0630-0. - DOI - PubMed
    1. Wang C.J., Ng C., Brook R.H. Response to COVID-19 in Taiwan: Big data analytics, new technology, and proactive testing. JAMA. 2020;323:1341–1342. doi: 10.1001/jama.2020.3151. - DOI - PubMed
    1. Delen D., Eryarsoy E., Davazdahemami B. No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic. JMIR Public Health Surveill. 2020;6:e19862. doi: 10.2196/19862. - DOI - PMC - PubMed
    1. Roser M., Ritchie H., Ortiz-Ospina E., Hasell J. Taiwan: Coronavirus Pandemic Country Profile. Our World in Data. [(accessed on 2 April 2021)]. Available online: https://ourworldindata.org/coronavirus/country/taiwan.
    1. Liu L. Taiwan Launches Social Distancing App. [(accessed on 5 April 2021)]. Available online: https://www.taiwannews.com.tw/en/news/4193136.

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