Critical Human Security and Post-Covid Public Policy[1] Blog series. Blog No. 4
By Minjun Hong, Seoul National University
Introduction
The COVID-19 pandemic has accelerated the pace of ‘digitalisation’, shedding light on the opportunities and the challenges it presents (Almeida et al., 2020; Amankwah-Amoah et al., 2021). While traditional infection control approaches and the digitalisation level of the healthcare system were not the sole determinants of a successful COVID-19 pandemic strategy (Baumgart, 2020; Ting et al., 2020), it is undeniable that the integration of digital technologies into the pandemic response assisted in flattening the COVID-19 incidence curves and in coping with the impact of the virus (Faraj et al., 2021; Whitelaw et al., 2020) as the comparison of South Korea (hereafter Korea) and England demonstrate. However, the rapid adoption of digital technologies also raised concerns around human security, specifically in relation to the digital divide and privacy concerns (Pandey & Pal, 2020; Budd et al, 2020).
This blog provides an overview of contact tracing policies and the digital divide in South Korea and England to explore the intersections between digitalisation and critical human security. Whilst the critical human security approach is comprised of a range of integrated dimensions of life, including health, economic, food, environmental and community security, as well as personal and political freedom (Kennett, Kwon and Lee, 2003; UN, 2016; Newman, 2010), digitalisation represents a cross-cutting dynamic that intersects these different dimensions. Drawing on publicly available survey data, secondary data and official documents the blog highlights particularly how contact tracing policies during the COVID-19 pandemic in England and South Korea affected and shaped the health and personal security of vulnerable populations.
Contact Tracing Policies in South Korea and England
South Korea rapidly employed Information and Communications Technology (ICT) to supplement traditional epidemiological investigation in the early stages of the pandemic (You, 2020; Cohen et al., 2020). The COVID-19 Smart Management System (SMS) was launched on March 26, 2020. It digitised the entire contact tracing process, providing real-time data on confirmed cases, and significantly reducing contact tracing time (Ko, 2023). The SMS collected data from multiple sources and analysed digital records, enabling it to pinpoint transmission routes and identify infection hotspots (Hong et al., 2023; Horgan et al., 2020).
Tracking information was made available to the public through various channels, including briefings, websites, and SMS messages (Lee & Lee, 2020). Therefore, the early adoption of digital technologies played a critical role in the successful implementation of the 3T (test-trace-treat) strategy and contributed to what was considered an effective responses to the COVID-19 pandemic (Majeed et al., 2020; Whitelaw et al., 2020; Park et al., 2020; Heo et al., 2020), avoiding the need for systemic lockdowns or restrictions on in-person interactions (Lee and Lee, 2020).
The UK government also initially implemented contact tracing during the early stage of the pandemic but with considerably less success than in Korea and by March 12, 2020, the UK government had made the decision to halt contact tracing due to a shortage in testing capacity (Briggs et al., 2020) and an inability of the system to cope with the number of cases. However, as the number of confirmed cases and deaths continued to increase rapidly, contact tracing was resumed.
From May 5, a pilot for utilising the NHS contact-tracing app was implemented (Samuel et al., 2021) with an app adopting Bluetooth technology and contact information stored in a central database rather than solely on an individual’s phone (Williams et al., 2021). This approach raised concerns regarding not only the Bluetooth technology but also the centralised data collection model, privacy infringements, and government surveillance (Cresswell et al., 2021; Samuel et al., 2022).
In response to these concerns the government abandoned the centralised model and started developing a decentralised model in collaboration with Apple and Google (Cresswell et al., 2021). The new NHS COVID-19 app was launched in September 2020, utilising a decentralised model with data processing occurring on the users’ smartphones (Jones & Thompson, 2021; Pepper et al., 2022), and included other functions such as QR code scanning for venue check-in when visiting public places and the booking of tests. According to the Oxford Research Group approximately 60% of the population needed to utilise the app to halt the spread of the pandemic (Seto et al., 2021).
However, the NHS COVID-19 app was far from successful, with only a low percentage of individuals in England receiving their COVID-19 test results within 24 hours (15.1% of people for the week ending October 14 2020) (Mellor, 2020), the download count being fairly insignificant (Briggs et al., 2020; Mellor, 2020; Ceci, 2022; Lewandowsky et al., 2021) and a generally low utilisation of the app.
Digital divides observed during the pandemic
Table 1 presents the awareness and usage experience of information services in South Korea, focusing particularly on older people who were considered most vulnerable to the health impacts of the COVID-19 virus. The table reveals that the percentage of older people who were aware of information services in 2020 was only 21%, which was significantly lower when compared to other groups. Furthermore, even among those who were aware of these services, the actual usage rate among the elderly was only 13.39%. In 2021, the awareness of information services among the elderly increased to a level similar to other vulnerable groups. However, the proportion of actual users remained low in comparison to other groups. This underscores the need to evaluate whether the promotion of information services and systems was effectively translated into their actual utilisation, as well as to highlight the diversity of experience amongst different groups.
Table 1. Awareness and usage experience of COVID information services in South Korea
| Group | Awareness of services | Experience of service usage | ||||
| 2020 | 2021 | 2022 | 2020 | 2021 | 2022 | |
| General population | 4850 (69.29) | 6349 (90.70) | 6318 (90.26) | 4042 (57.74) | 5318 (75.97) | 5230 (74.71) |
| Farmers & Fishers | 1715 (77.95) | 1657 (75.32) | 1648 (74.91) | 1212 (55.09) | 1205 (54.77) | 1157 (52.59) |
| People with disabilities | 1697 (77.14) | 1646 (74.82) | 1741 (79.17) | 1249 (56.77) | 1204 (54.73) | 1361 (61.89) |
| Low-income | 1920 (87.27) | 1861 (84.59) | 1761 (80.05) | 1468 (66.73) | 1480 (67.27) | 1376 (62.55) |
| Elderly | 483 (21.00) | 1822 (79.22) | 1823 (79.26) | 308 (13.39) | 1177 (51.17) | 1247 (54.22) |
Unit: frequency (percentage)
Note: Information services refers to internet/mobile information services such as confirmed COVID-19 cases, infected person’s whereabouts and timeline information, and COVID-19 screening clinics.
Source: National information society agency
Table 2 shows the proportion of people in Korea whose reason for not utilising COVID-19 information services was due to lack of knowledge or difficulty of usage. Elderly people experienced more difficulty in using the services in 2020 and 2021 than other groups. The proportion of people on low-incomes and with disabilities who expressed their lack of knowledge or difficulty in using the technology as a reason for not utilising COVD-19 information services also gradually increased.
Table 2. Reasons for not utilising COVID-19 information service in South Korea: lack of knowledge/difficulty in usage
| Group | 2020 | 2021 | 2022 |
| General population | 43.7 % | 65.0 % | 60.3 % |
| Farmers & Fishers | 59.8 % | 78.0 % | 75.3 % |
| People with disabilities | 49.3 % | 67.5 % | 75.6 % |
| Low-income | 54.9 % | 63.3 % | 70.7 % |
| Elderly | 66.9 % | 79.2 % | 74.2 % |
Source: National information society agency
There is no publicly accessible survey data for the United Kingdom so the figures reported draw from multiple articles and are summarised in Table 3. After the app’s release, discrepancies in usage by demographic factors were apparent. There were differences in the willingness to install or use contact tracing apps, with a higher percentage of people 65 and above less likely to download the app compared to those under 65 (Dowthwaite, et al, 2021), Dowthwaite et al (2021) also reported in their study that whilst BAME respondents were more likely to have had some experience with COVID-19 compared to white respondents, BAME respondents were less likely to install the app than white respondents.
According to Jones & Thompson (2021), the reasons for not utilising the app were not having downloaded it, not desiring to, or being unable to purchase it. In addition, some people experienced challenges in utilising a smartphone and a lack of understanding of how to install and use the app.
Table 3. Research related to awareness of and experience of using the COVID-19 app among vulnerable groups in the United Kingdom
| Research | Survey | Questionnaire | Response | |
| General / Not vulnerable | Vulnerable | |||
| Ipsos Mori, 2020a | May 1-10, 2020 | Likely to download a smartphone app | Overall: 62%
Managerial, administrative or professional jobs: 73% Masters PhD: 71% |
65+: 55%
Routine & manual worker, state pensioners, the unemployed: 50% GCSEs or equiv: 59% |
| Not being in a position to download app | Overall: 5% | 65+: 17% | ||
| Ipsos Mori, 2020b | July 17 – 29, 2020 | Likely to download app | 18-24: 57% | 65+: 41% |
| Likely to use it to report symptoms | 18-24: 76% | 65+: 48% | ||
| Ipsos Mori, 2021 | Nov 13-24, 2020 | Support government’s use of app | White: 63%
Overall: 61% |
BAME: 48%
55+: 67% |
| Dowthwaite et al., 2021 | Dec 11 and 21, 2020 | Downloaded the app | White: 50.2%
-65: 48.5% |
BAME: 41.7%
65+: 52.0% |
| Downloaded then deleted | White: 7.4%
-65: 9.0% |
BAME: 13.9%
65+: 1.6% |
||
| Do not intend to download of the app | White: 26.9%
-65: 25.2% |
BAME: 20.9%
65+: 34.6% |
||
| Ceci, 2021 | – July 2021 | Have app, using correctly | 18-24: 9%
25-49: 19% |
50-64: 22%
65+: 29% |
| Deleted the app | 18-24: 17%
25-49: 12% |
50-64: 9%
65+: 6% |
||
| Never had the app | 18-24: 31%
25-49: 34% |
50-64: 41%
65+: 47% |
||
Note: BAME refers to ‘Black, Asian, and Minority Ethnic’.
Source: Ipsos Mori, 2020a; Ipsos Mori, 2020b; Ipsos Mori, 2021; Dowthwaite et al., 2021; Ceci, 2021
Conclusion
Clearly, as the public health and policy approach has shifted to one of “living with COVID-19” in both Korea and the UK, issues related to accessing information services and their implications particularly for vulnerable populations must be a priority in governing risk and promoting human security. Effective utilisation of digital technologies can enhance the implementation of COVID-19 strategies as the evidence from Korea indicates. It also has a key role to play as an instrument for protecting populations as we move beyond the pandemic period to ‘living with COVID-19’. However, the rapid adoption of digital technologies does not guarantee effectiveness. Adopting and utilising information technology related to specific crises can vary depending on a variety of factors including age, education and digital literacy, as well as wider concerns regarding the security of information.
The data collected and explored in this blog has indicated that awareness and utilisation of technology varied particularly around factors such as socio-economic status, ethnicity, and age suggesting that the effectiveness of pandemic responses and ICT related risk governance strategies needs to acknowledge and address this digital divide. through multi-agency working to prepare and disseminate appropriate and accessible information and instruments. At the same time, the government should consider adopting a multi-agency approach to develop and implement suitable policy communication strategies and customised support to address the ongoing digital divide “post-COVID-19” and strengthen risk governance to enhance the effectiveness of government responses in a future disaster situation.
[1] This draws on collaborative research funded by ESRC Grant Number ES/W010739/1
