# Background

The novel coronavirus or COVID-19 was declared a pandemic by the World Health Organization on March 11th, 2020 (World Health Organization, 2020). The COVID-19 virus transmits early in its life cycle relative to other coronaviruses, (e.g., SARS), during which time many individuals present as asymptomatic (Zhou et al., 2020). The moderate to high transmissibility of this virus has required governments to consider moderate to extreme measures in order to prevent further transmissions. Indeed, the nature of the COVID-19 pandemic may require governments to use big data technologies to help contain its spread (Bonsall, Parker, & Fraser, 2020).

Countries that have managed to “flatten the curve” - the process of slowing the exponential transmission rate of the virus, for example, Singapore and Taiwan - have employed collocation tracking through mobile Wi-Fi, GPS, and Bluetooth as a strategy to mitigate the impact of COVID-19 (Wang et al., 2020). Through collocation tracking, government agencies may observe who you have been in contact with and when this contact occurred, allowing for the rapid implementation of appropriate measures to reduce the spread of COVID-19.

The effectiveness of collocation tracking relies on the willingness of the population to support such measures, implying that government policy-making should be informed by the likelihood of public compliance. Gaining the social license - broad community acceptance beyond formal legal requirements - for collocation tracking requires the perceived public health benefits to outweigh concerns of personal privacy, security, and any potential risk of harm.

This report forms the preliminary results of a longitudinal cross-cultrual study mapping the evolution of people’s attitudes towards government tracking during the COVID-19 crisis. We aim to understand (1) the factors that influence the social license around governmental use of location tracking data in an emergency, (2) how this may change over time, and (3) how this may differ between countries.

The results we present here were collected through a representative survey of Australian residents that assessed their attitudes towards Government tracking during the COVID-19 pandemic. We presented participants with one of two scenarios describing different government tracking methods that may reduce the spread of COVID-19. We then question participants’ attitudes towards the proposed methods.

The two Government tracking scenarios differed in two important ways. In scenario one, participants could opt-in to being tracked by the Government and the collected data could only be used to contact those who may have been exposed to COVID-19. In scenario two, all people using a mobile phone would have their data tracked with no possibility to opt-out, and tracking data could be used to issue fines and arrests for violations of lockdown orders. The two scenarios are presented below:

## Scenario One

The COVID-19 pandemic has rapidly become a worldwide threat. Containing the virus’ spread is essential to minimise the impact on the healthcare system, the economy, and save many lives. The Taiwanese Government might consider using smartphone tracking data to identify and contact those who may have been exposed to people with COVID-19. This would help reduce community spread by identifying those most at risk and allowing health services to be appropriately targeted. Only people that downloaded a government app and agreed to be tracked and contacted would be included in the project. The more people that download and use this app the more effectively the Government would be able to contain the spread of COVID-19. Data would be stored in an encrypted format on a secure server accessible only to the Taiwanese Government. Data would only be used to contact those who might have been exposed to COVID-19. 流行病COVID-19 已經迅速地威脅了全球人類的健康。為了減少對醫療保健系統、經濟的影響，並挽救許多生命，「如何限制病毒傳播」是當前最重要的議題。台灣政府可能考慮使用智慧型手機定位追蹤數據，用來識別和聯繫那些可能已經接觸過COVID-19患者的人。用此定位追蹤技術可以辨識出具有高風險的族群並精準地給予治療，能夠降低社區傳染的風險。但不是所有台灣人都需要參與此計畫，而是只有下載政府提供的應用程式（手機APP），並同意進行追蹤和聯繫的人，才會被包含在該計畫中。因此，下載和使用此應用程式的人越多，政府就能更加有效地限制COVID-19的傳播。而這些定位追蹤的數據將以加密格式儲存在安全的伺服器上，只有台灣政府能讀取該伺服器資料。同時，這些資料僅能用於聯繫可能暴露在COVID-19感染風險下的民眾，不會另做其他用途。

## Scenario Two

The COVID-19 pandemic has rapidly become a worldwide threat. Containing the virus’ spread is essential to minimise the impact on the healthcare system, the economy, and save many lives. The Taiwanese Government might consider using phone tracking data supplied by telecommunication companies to identify and contact those who may have been exposed to people with COVID-19. This would help reduce community spread by identifying those most at risk and allowing health services to be appropriately targeted. All people using a mobile phone would be included in the project, with no possibility to opt-out. Data would be stored in an encrypted format on a secure server accessible only to the Taiwanese Government who may use the data to locate people who were violating lockdown orders and enforce them with fines and arrests where necessary. Data would also be used to inform the appropriate public health response and to contact those who might have been exposed to COVID-19, and individual quarantine orders could be made on the basis of this data.

# Results

## Status of the initial data collected

These results represent a snapshot from the first 584 participants collected for the project, Establishing the social licence for Government tracking in Taiwan. This representative sample was gathered through the data collection platform SurvetCake.

Notes on cleaning the data.

1. The second and third rows of the raw CSV file were deleted before analysis. These rows contained header information that interferred with loading into R.
2. Twenty two participants were removed prior to analysis for indicating a country of residence other than Taiwan.
3. 73 participants did not pass the attention check.

The final sample for analysis was 584 participants.

# Descriptive Statistics

Gender was evenly divided between men and women. Within our sample, participants most frequently reported as having a university education (61.6%) or a higher school education (38%). Ages ranged from 18 years to 72 years (M = 29.3 years, SD = 11.5 years). The distribution of reported ages was positively skewed within the age range 20–80, and under represented for ages 80+.

  #Total Gender identification: Percentages Gender Men 45.2 Women 54.5 Other Prefer not to say 0.3 #Total cases 584
  #Total Level of education: Percentages Education < High School 0.3 High School 38.0 University 61.6 #Total cases 584

Distribution of ages.

## Descriptive Statistics
## COVIDdata$age ## N: 584 ## ## age ## ----------------- -------- ## Mean 29.32 ## Std.Dev 11.55 ## Min 18.00 ## Q1 20.00 ## Median 24.00 ## Q3 37.00 ## Max 72.00 ## MAD 7.41 ## IQR 17.00 ## CV 0.39 ## Skewness 1.16 ## SE.Skewness 0.10 ## Kurtosis 0.77 ## N.Valid 584.00 ## Pct.Valid 100.00 # Impacts of COVID Participants reported as being under lockdown for an average of 0.66 (SD = 2.94) days, with the most frequent amount of time in lockdown reported as zero days (n = 517; 88.5%). Three point four percent of participants reported as having lost their job due to COVID-19. The most common source of COVID-19 information came from Newspaper (60.8%) and Social media (27.7%), followed by TV (8%). Of the 584 participants, one (0.002%) reported that they had tested positive with COVID-19, and 8 (1.4%) indicated they knew someone who had tested positive with COVID-19. hist(COVIDdata$COVID_ndays_4, xlab="Days in lockdown",main="",  breaks = 50)

  #Total I have lost my job: Percentages I lost my job No 96.6 Yes 3.4 #Total cases 584
  #Total Information source: Percentages Information source Newspaper (printed or online) 60.8 Social media 27.7 Friends/family 0.9 Radio Television 8.0 Other 2.6 #Total cases 584
  #Total Somebody I know tested positive for COVID-19: Percentages Tested pos someone I know No 98.6 Yes 1.4 #Total cases 584

# Perceived Risk of COVID

When asked about COVID-19 within the Taiwanese population, participants most frequently reported the virus to be moderate in severity and that the virus posed a somewhat harmful risk to their personal health. Responses were both normally distributed around these moderate values.

When asked about their concern over testing positive to COVID-19, participants were normally distributed and centered on moderatly concerned. When asked about their concern over someone they know testing positive to COVID-19, participants responses were negatively skewed, showing a bias in their concerned for the health of others. A strong correlation was observed between personal harm and concern for others (r = .69).

##                    COVID_gen_harm COVID_pers_harm COVID_pers_concern
## COVID_gen_harm              1.000           0.312              0.394
## COVID_pers_harm             0.312           1.000              0.386
## COVID_pers_concern          0.394           0.386              1.000
## COVID_concern_oth           0.332           0.335              0.686
##                    COVID_concern_oth
## COVID_gen_harm                 0.332
## COVID_pers_harm                0.335
## COVID_pers_concern             0.686
## COVID_concern_oth              1.000

# Perceived Impact of Government Tracking

The following violine plots characterizes participant’s confidence that in each scenario Government tracking would:

1. Reduce their likelihood of contracting COVID-19
2. Allow them to resume their normal lives more rapidly
3. Reduce spread of COVID-19 in the community.

Participants were more confident that they would not contract COVID-19 and that they would resume their normal activities under scenario two. Participants were confident that both scenarios would reduce the spread of COVID-19.

## Acceptability of Government Tracking

The following table displays participant’s acceptability of Government tracking, as probed by a single item immediately after reading scenario one or two. For scenario one, the question refers to whether a participant would download the app. For the scenario two, the question refers to the acceptability of the tracking mandated by government. In both instances, participants generally reported that the measures were acceptable. Acceptability was not significant between scenario one (78.5%) and scenario two (84.1%; $$\chi^2$$ = 2.618).

#Total
Scenario Type
Scenario One   Acceptability of policy   No  21.5
Yes  78.5
#Total cases  270
Scenario Two   Acceptability of policy   No  15.9
Yes  84.1
#Total cases  314
##
##  Pearson's Chi-squared test with Yates' continuity correction
##
## data:  unlabel(accept1$value) and unlabel(accept1$key)
## X-squared = 2.6178, df = 1, p-value = 0.1057

The following table displays participant’s acceptability of Government tracking as probed after they have answer a series of questions about a scenario. The general trend in acceptability remains after answering questions about Government tracking, with acceptability being even between scenario one (78.1%) and scenario two (78.0%; $$\chi^2$$ = 8.5314e-31). After answer questions about the scenario’s tracking methods, participants became less accepting of Government tracking in scenario tow (a reduction of 6%).

#Total
Scenario Type
Scenario One   Acceptability of policy   No  21.9
Yes  78.1
#Total cases  270
Scenario Two   Acceptability of policy   No  22.0
Yes  78.0
#Total cases  314
##
##  Pearson's Chi-squared test with Yates' continuity correction
##
## data:  unlabel(accept2$value) and unlabel(accept2$key)
## X-squared = 8.5314e-31, df = 1, p-value = 1

## Conditional Acceptance of Government Tracking

The following results were collected from those people who indicated that they would not download the app (scenario one) or who indicated that Government tracking was not acceptable (scenario two).

The following table describes acceptability of Government tracking if a sunset clause were included in the tracking policy, for example, limiting Government tracking to a period of six months after which the data would be destroyed. Of those participants who viewed scenario two, 37.3% deemed tracking to be acceptable under a sunset clause. In scenario one, only 36.2% changed their attitude and deemed tracking accepatable under a sunset clause.

#Total
Scenario Type
Scenario One   Acceptability with sunset   No  62.7
Yes  37.3
#Total cases  59
Scenario Two   Acceptability with sunset   No  63.8
Yes  36.2
#Total cases  69

The following tables describe acceptability of Government tracking if i) the tracking data were to stay on the phone and only be uploaded with the consent of the individual (scenario one only), and ii) if participants were able to opt-in to the data collection (scenario two only).

#Total
Acceptability with local storage
No  27.1
Yes  72.9
#Total cases  59
#Total
Acceptability with opt out
No  37.7
Yes  62.3
#Total cases  69

These responses were combined into a quasi-interval scale using the following coding scheme: YES=acceptable; NO=not acceptable unless sunset or optout; -1=not acceptable unless the inclusion of sunset or opt-out (but not both); -2=not acceptable under any circumstances

#Total
Acceptability of policy
-2  6.3
-1  8.4
No  7.2
Yes  78.1
#Total cases  584

The next graph shows responses to the following items (abridged from survey):

1. How easy is it for people to decline participation in the proposed project?
2. To what extent is the Government only collecting the data necessary?
3. How sensitive is the data being collected in the proposed project?
4. How serious is the risk of harm that could arise from the proposed project?
5. How much do you trust the Government to use the tracking data only to deal with the COVID-19 pandemic?
6. How much do you trust the Government to be able to ensure the privacy of each individual?
7. How secure is the data that would be collected for the proposed project?
8. To what extent do people have ongoing control of their data?

The following results hold across both scenario one and scenario two. Participants generally displayed a high degree of Government trust and indicated that the Government was only collecting data necessary for COVID-19 tracing. Participants generally believed that the Government would ensure their privacy and secure their data. Although participants did not view the risk of harm in collecting this data as particularly high, they did understand that the data collected was sensitive (i.e., in need of security and privacy).

# Influence of World View

We relate a composite of the 3 worldview items to the composite of the 4 items probing perceived risk from COVID. Worldview is scored such that greater values reflect greater libertarianism. The appeared to be no effect of libertarian world-view on acceptability of Government tracking within Taiwanese participants.

## geom_smooth() using method = 'loess' and formula 'y ~ x'

##
##  Pearson's product-moment correlation
##
## data:  COVIDdata$Worldview and COVIDdata$COVIDrisk
## t = -1.7144, df = 582, p-value = 0.08699
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.15115035  0.01030818
## sample estimates:
##         cor
## -0.07088538

Similarly, we relate the composite of the 3 worldview items to the composite of the two trust-in-government items. There appears to be a very small negative relationship between Government trust and libertarian world-view within Taiwanese participants.

## geom_smooth() using method = 'loess' and formula 'y ~ x'
## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).

##
##  Pearson's product-moment correlation
##
## data:  COVIDdata$Worldview and COVIDdata$govtrust
## t = -2.3502, df = 582, p-value = 0.0191
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.17670467 -0.01595172
## sample estimates:
##        cor
## -0.0969605

# References

Bonsall, D., Parker, M., Fraser, C. (2020).Sustainable containment of COVID-19 using smartphones in China: Scientific and ethical underpinnings for implementation of similar approaches in other settings.

The world health organization (WHO). WHO announces COVID-19 outbreak a pandemic. Retrieved from http://www.euro.who.int/en/health-topics/health-emergencies/coronavirus-covid-19/news/news/2020/3/who-announces-covid-19-outbreak-a-pandemic.

Wang, C. J., Ng, C. Y., & Brook, R. H. (2020). Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. JAMA.

Zhou, T., Liu, Q., Yang, Z., Liao, J., Yang, K., Bai, W., … & Zhang, W. (2020). Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019-nCoV. Journal of Evidence-Based Medicine.