App-based Contact Tracing Research Articles

Privacy-Preserving Contact Tracing for Curbing the Spread of Infectious Disease

The world is facing one of the greatest public health threats in modern history. Various techniques based on contact tracing have been developed to support non-pharmaceutical interventions. The growing evidence shows that app-based contact tracing can reduce the spread of COVID-19 if a certain proportion of the population uses the apps. However, the risk of privacy breaches that comes with such apps has long been a public concern which may hinder the uptake of the apps. In this paper, the authors attempt to find a solution to complete the spatiotemporal intersection computation without exposing the infected patient location and the user location to one another. The authors implement the solution in the WeChat applet to aid the local health center. This study conducts experiments for six scenarios to justify the applicability of the applet. Experiment results indicate that the applet is a promising non-pharmaceutical tool for curbing the spread of COVID-19.

Resident Willingness to Participate in Digital Contact Tracing in a COVID-19 Hotspot: Findings From a Detroit Panel Study.

Digital surveillance tools and health informatics show promise in counteracting diseases but have limited uptake. A notable illustration of the limits of such tools is the general failure of digital contact tracing in the United States in response to COVID-19. We investigated the associations between individual characteristics and the willingness to use app-based contact tracing in Detroit, a majority-minority city that experienced multiple waves of COVID-19 outbreaks and deaths since the start of the pandemic. The aim of this study was to examine variations among residents in the willingness to download a contact tracing app on their phones to provide public health officials with information about close COVID-19 contact during summer 2020. To examine residents' willingness to participate in digital contact tracing, we analyzed data from 2 waves of the Detroit Metro Area Communities Study, a population-based survey of Detroit, Michigan residents. The data captured 1873 responses from 991 Detroit residents collected in June and July 2020. We estimated a series of multilevel logit models to gain insights into differences in the willingness to participate in digital contact tracing across a variety of individual attributes, including race/ethnicity, degree of trust in the government, and level of education, as well as interactions among these variables. Our results reflected widespread reluctance to participate in digital contact tracing in response to COVID-19, as less than half (826/1873, 44.1%) of the respondents said they would be willing to participate in app-based contact tracing. Compared to White respondents, Black (odds ratio [OR] 0.45, 95% CI 0.23-0.86) and Latino (OR 0.32, 95% CI 0.11-0.99) respondents were significantly less willing to participate in digital contact tracing. Trust in the government was positively associated with the willingness to participate in digital contact tracing (OR 1.17, 95% CI 1.07-1.27), but this effect was the strongest for White residents (OR 2.14, 95% CI 1.55-2.93). We found similarly divergent patterns of the effects of education by race. While there were no significant differences among noncollege-educated residents, White college-educated residents showed greater willingness to use app-based contact tracing (OR 6.12, 95% CI 1.86-20.15) and Black college-educated residents showed less willingness (OR 0.46, 95% CI 0.26-0.81). Trust in the government and education contribute to Detroit residents' wariness of digital contact tracing, reflecting concerns about surveillance that cut across race but likely arise from different sources. These findings point to the importance of a culturally informed understanding of health hesitancy for future efforts hoping to leverage digital contact tracing. Though contact tracing technologies have the potential to advance public health, unequal uptake may exacerbate disparate impacts of health crises.

Epidemics with asymptomatic transmission: Subcritical phase from recursive contact tracing.

The challenges presented by the COVID-19 epidemic have created a renewed interest in the development of new methods to combat infectious diseases, and it has shown the importance of preparedness for possible future diseases. A prominent property of the SARS-CoV-2 transmission is the significant fraction of asymptomatic transmission. This may influence the effectiveness of the standard contact tracing procedure for quarantining potentially infected individuals. However, the effects of asymptomatic transmission on the epidemic threshold of epidemic spreading on networks have rarely been studied explicitly. Here we study the critical percolation transition for an arbitrary disease with a nonzero asymptomatic rate in a simple epidemic network model in the presence of a recursive contact tracing algorithm for instant quarantining. We find that, above a certain fraction of asymptomatic transmission, standard contact tracing loses its ability to suppress spreading below the epidemic threshold. However, we also find that recursive contact tracing opens a possibility to contain epidemics with a large fraction of asymptomatic or presymptomatic transmission. In particular, we calculate the required fraction of network nodes participating in the contact tracing for networks with arbitrary degree distributions and for varying recursion depths and discuss the influence of recursion depth and asymptomatic rate on the epidemic percolation phase transition. We anticipate recursive contact tracing to provide a basis for digital, app-based contact tracing tools that extend the efficiency of contact tracing to diseases with a large fraction of asymptomatic transmission.

Network-Based Contact Tracing for Detection of Covid-19 Contagions: A Privacy-Preserving Approach

The outbreak of coronavirus 2019 (Covid-19) has imposed a worldwide lockdown, changing the way people live and work, and pressuring the healthcare systems in many countries. Contact tracing based on smartphone apps (app-based contact tracing) has emerged as a possible solution to trace contagions and enforce more sustainable and selective quarantines. However, these apps require a very high adoption rate to reach the critical mass for effective contact tracing. As an alternative, network-based contact tracing, which exploits geo-localization in next generation networks (e.g., 5G) can be used by mobile operators (MOs) to passively trace users' mobility and contacts, provided that a targeted localization accuracy of down to one meter can be achieved. To effectively trace contagions, the identities of positive individuals, which are known by governmental authorities (GAs), are also required. Hence, in network-based contact tracing, MOs and GAs must exchange users' privacy-sensitive data, as geo-locations and infection status, to compute the likelihood that an individual has been infected. To address the privacy issues raised by network contact tracing, after presenting an overview of app-based vs. network-based contact tracing systems, we propose a protocol to make network-based systems compliant with stringent privacy requirements. From extensive simulations, we observe that the cost to guarantee privacy (evaluated in terms of data overhead introduced by the protocol) is acceptable. Finally, we elaborate on the advantages of the proposed system (e.g., more efficient monitoring of virus spread), as well as on the open issues that should be solved before its adoption (e.g., extensive research on potential privacy leakage is required).

Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence-Enabled Social Media Analysis.

BackgroundThe emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2.ObjectiveIn this study, we sought to explore the suitability of artificial intelligence (AI)–enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom.MethodsWe extracted and analyzed over 10,000 relevant social media posts across an 8-month period, from March 1 to October 31, 2020. We used an initial filter with COVID-19–related keywords, which were predefined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app–related keywords and a geographical filter. We developed and utilized a hybrid, rule-based ensemble model, combining state-of-the-art lexicon rule-based and deep learning–based approaches.ResultsOverall, we observed 76% positive and 12% negative sentiments, with the majority of negative sentiments reported in the North of England. These sentiments varied over time, likely influenced by ongoing public debates around implementing app-based contact tracing by using a centralized model where data would be shared with the health service, compared with decentralized contact-tracing technology.ConclusionsVariations in sentiments corroborate with ongoing debates surrounding the information governance of health-related information. AI-enabled social media analysis of public attitudes in health care can help facilitate the implementation of effective public health campaigns.

Barriers to the Large-Scale Adoption of a COVID-19 Contact Tracing App in Germany: Survey Study.

BackgroundDuring the COVID-19 pandemic, one way to reduce further transmissions of SARS-CoV-2 is the widespread use of contact tracing apps. Such apps keep track of proximity contacts and warn contacts of persons who tested positive for an infection.ObjectiveIn this study, we analyzed potential barriers to the large-scale adoption of the official contact tracing app that was introduced in Germany on June 16, 2020.MethodsSurvey data were collected from 3276 adults during the week the app was introduced using an offline-recruited, probability-based online panel of the general adult population in Germany.ResultsWe estimate that 81% of the population aged 18 to 77 years possess the devices and ability to install the official app and that 35% are also willing to install and use it. Potential spreaders show high access to devices required to install the app (92%) and high ability to install the app (91%) but low willingness (31%) to correctly adopt the app, whereas for vulnerable groups, the main barrier is access (62%).ConclusionsThe findings suggest a pessimistic view on the effectiveness of app-based contact tracing to contain the COVID-19 pandemic. We recommend targeting information campaigns at groups with a high potential to spread the virus but who are unwilling to install and correctly use the app, in particular men and those aged between 30 and 59 years. In addition, vulnerable groups, in particular older individuals and those in lower-income households, may be provided with equipment and support to overcome their barriers to app adoption.

Framework for Prioritizing Contact Tracing and Mass Testing of COVID-19 Using Graph Theory

Contact tracing has become one of the most useful tools for fighting the novel Corona Virus (COVID-19) pandemic worldwide. The underlining philosophy of contact tracing is determining people who have been in contact with infected persons and thus isolate them from becoming agents of onward transmission of the virus. Slow tracing of contacts and inconsistent or inaccurate information provided by patients usually leads to the spread of the virus along a trajectory at the healthcare systems' blindside. This has led to the proposal of app-based contact tracing solutions. This paper proposes an SQL-based framework that transforms simple interaction data entries into interaction graphs and applies graph theory to prioritize the contact tracing process. The framework returns nodes or individual IDs together with values called Risk_Points to enable individuals' selection for isolation and treatment. Results on simulated data show that the proposed framework can help slow the virus's rate of transmission.

Mobile App-based contact Tracing During COVID-19-Simple and Without Recall Bias.

Mobile App-based contact Tracing During COVID-19-Simple and Without Recall Bias.

Belief of having had unconfirmed Covid-19 infection reduces willingness to participate in app-based contact tracing

Contact tracing and lockdown are health policies being used worldwide to combat the coronavirus (COVID-19). The UK National Health Service (NHS) Track and Trace Service has plans for a nationwide app that notifies the need for self-isolation to those in contact with a person testing positive for COVID-19. To be successful, such an app will require high uptake, the determinants and willingness for which are unclear but essential to understand for effective public health benefit. The objective of this study was to measure the determinants of willingness to participate in an NHS app-based contact-tracing programme using a questionnaire within the Care Information Exchange (CIE)—the largest patient-facing electronic health record in the NHS. Among 47,708 registered NHS users of the CIE, 27% completed a questionnaire asking about willingness to participate in app-based contact tracing, understanding of government advice, mental and physical wellbeing and their healthcare utilisation—related or not to COVID-19. Descriptive statistics are reported alongside univariate and multivariable logistic regression models, with positive or negative responses to a question on app-based contact tracing as the dependent variable. 26.1% of all CIE participants were included in the analysis (N = 12,434, 43.0% male, mean age 55.2). 60.3% of respondents were willing to participate in app-based contact tracing. Out of those who responded ‘no’, 67.2% stated that this was due to privacy concerns. In univariate analysis, worsening mood, fear and anxiety in relation to changes in government rules around lockdown were associated with lower willingness to participate. Multivariable analysis showed that difficulty understanding government rules was associated with a decreased inclination to download the app, with those scoring 1–2 and 3–4 in their understanding of the new government rules being 45% and 27% less inclined to download the contact-tracing app, respectively; when compared to those who rated their understanding as 5–6/10 (OR for 1–2/10 = 0.57 [CI 0.48–0.67]; OR for 3–4/10 = 0.744 [CI 0.64–0.87]), whereas scores of 7–8 and 9–10 showed a 43% and 31% respective increase. Those reporting an unconfirmed belief of having previously had and recovered from COVID-19 were 27% less likely to be willing to download the app; belief of previous recovery from COVID-19 infection OR 0.727 [0.585–0.908]). In this large UK-wide questionnaire of wellbeing in lockdown, a willingness for app-based contact tracing over an appropriate age range is 60%—close to the estimated 56% population uptake, and substantially less than the smartphone-user uptake considered necessary for an app-based contact tracing to be an effective intervention to help suppress an epidemic. Difficulty comprehending government advice and uncertainty of diagnosis, based on a public health policy of not testing to confirm self-reported COVID-19 infection during lockdown, therefore reduce willingness to adopt a government contact-tracing app to a level below the threshold for effectiveness as a tool to suppress an epidemic.

Concerns and Misconceptions About the Australian Government's COVIDSafe App: Cross-Sectional Survey Study.

BackgroundTimely and effective contact tracing is an essential public health measure for curbing the transmission of COVID-19. App-based contact tracing has the potential to optimize the resources of overstretched public health departments. However, its efficiency is dependent on widespread adoption.ObjectiveThis study aimed to investigate the uptake of the Australian Government’s COVIDSafe app among Australians and examine the reasons why some Australians have not downloaded the app.MethodsAn online national survey, with representative quotas for age and gender, was conducted between May 8 and May 11, 2020. Participants were excluded if they were a health care professional or had been tested for COVID-19.ResultsOf the 1802 potential participants contacted, 289 (16.0%) were excluded prior to completing the survey, 13 (0.7%) declined, and 1500 (83.2%) participated in the survey. Of the 1500 survey participants, 37.3% (n=560) had downloaded the COVIDSafe app, 18.7% (n=280) intended to do so, 27.7% (n=416) refused to do so, and 16.3% (n=244) were undecided. Equally proportioned reasons for not downloading the app included privacy (165/660, 25.0%) and technical concerns (159/660, 24.1%). Other reasons included the belief that social distancing was sufficient and the app was unnecessary (111/660, 16.8%), distrust in the government (73/660, 11.1%), and other miscellaneous responses (eg, apathy and following the decisions of others) (73/660, 11.1%). In addition, knowledge about COVIDSafe varied among participants, as some were confused about its purpose and capabilities.ConclusionsFor the COVIDSafe app to be accepted by the public and used correctly, public health messages need to address the concerns of citizens, specifically privacy, data storage, and technical capabilities. Understanding the specific barriers preventing the uptake of contact tracing apps provides the opportunity to design targeted communication strategies aimed at strengthening public health initiatives, such as downloading and correctly using contact tracing apps.

Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study.

BackgroundThe 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.ObjectiveThe objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic.MethodsWe 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.ResultsWe 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.ConclusionsEpidemiological 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.

Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Evidence

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 socio-economic costs. One exit strategy under consideration is a mobile phone app that traces close contacts of those infected with COVID-19. Contact tracing apps work best when used by a large proportion of the population. The objective of this study is to investigate user acceptability of a contact-tracing app in five countries hit by the pandemic. Methods: We conducted cross-sectional anonymous online surveys in France, Germany, Italy, the UK and the US. The survey asked about intentions to use a contact-tracing app under various scenarios. Our study sample consists of 5995 adults. Findings: We find very strong support for a contact-tracing app, with an overall acceptance rate of 74.8%. Acceptability is high regardless of the respondent's country or individual background, or the scenario considered. Trust in government is an important determinant of support. Interpretation: Acceptability of app-based contact tracing for COVID-19 was widely supported in five countries hit by the pandemic to various degrees and with different attitudes towards privacy. Our findings are therefore encouraging for the viability of this approach. Funding Statement: Economic and Social Research Council (grant ES/R011710/1), the University of Oxford and Volkswagen Foundation (grant Consequences of Artificial Intelligence for Urban Societies''). Declaration of Interests: None declared. Ethics Approval Statement: Ethics approval was obtained from the University of Oxford (reference number ECONCIA20-21-06).

Use of a mobile application for Ebola contact tracing and monitoring in northern Sierra Leone: a proof-of-concept study

BackgroundThe 2014–2016 Ebola epidemic in West Africa was the largest Ebola epidemic to date. Contact tracing was a core surveillance activity. Challenges with paper-based contact tracing systems include incomplete identification of contacts, delays in communication and response, loss of contact lists, inadequate data collection and transcription errors.The aim of this study was to design and evaluate an electronic system for tracing contacts of Ebola cases in Port Loko District, Sierra Leone, and to compare this with the existing paper-based system. The electronic system featured data capture using a smartphone application, linked to an alert system to notify the District Ebola Response Centre of symptomatic contacts.MethodsThe intervention was a customised three-tier smartphone application developed using Dimagi’s CommCare platform known as the Ebola Contact Tracing application (ECT app). Eligible study participants were all 26 Contact Tracing Coordinators (CTCs) and 86 Contact Tracers (CTs) working in the 11 Chiefdoms of Port Loko District during the study period (April–August 2015). Case detection was from 13th April to 17th July 2015. The CTCs and their CTs were provided with smartphones installed with the ECT app which was used to conduct contact tracing activities. Completeness and timeliness of contact tracing using the app were compared with data from April 13th-June 7th 2015, when the standard paper-based system was used.ResultsFor 25 laboratory-confirmed cases for whom paper-based contact tracing was conducted, data for only 39% of 408 contacts were returned to the District, and data were often incomplete. For 16 cases for whom app-based contact tracing was conducted, 63% of 556 contacts were recorded as having been visited on the app, and the median recorded duration from case confirmation to first contact visit was 70 h.ConclusionThere were considerable challenges to conducting high-quality contact tracing in this setting using either the paper-based or the app-based system. However, the study demonstrated that it was possible to implement mobile health (mHealth) in this emergency setting. The app had the benefits of improved data completeness, storage and accuracy, but the challenges of using an app in this setting and epidemic context were substantial.