Traditional Contact Tracing Research Articles

Linking contact tracing with genomic surveillance to deconvolute SARS-CoV-2 transmission on a university campus

Contact tracing and genomic data, approaches often used separately, have both been important tools in understanding the nature of SARS-CoV-2 transmission. Linked analysis of contact tracing and sequence relatedness of SARS-CoV-2 genomes from a regularly sampled university environment were used to build a multilevel transmission tracing and confirmation system to monitor and understand transmission on campus. Our investigation of an 18-person cluster stemming from an athletic team highlighted the importance of linking contact tracing and genomic analysis. Through these findings, it is suggestive that certain safety protocols in the athletic practice setting reduced transmission. The linking of traditional contact tracing with rapid-return genomic information is an effective approach for differentiating between multiple plausible transmission scenarios and informing subsequent public health protocols to limit disease spread in a university environment.

COVID-19 contact tracing apps and the governance of collective action: social nudges, deliberation, and solidarity in Europe and beyond

ABSTRACT During the COVID-19 pandemic, Digital Contact Tracing (DCT) tools were deployed by governments in Europe and beyond as a novel mobile technology to assist traditional manual contact tracing to track individuals who have come in close contact with an infected person. The public debate on this topic focused strongly on the protection of individual privacy. While this debate is important, it fails to address important governance questions – such as, for example, that DCT tools took on the role of social nudges, namely, tools of soft regulation that calibrate information flows so as to “push” people to act in ways that promote collective purposes. Social nudges include a range of norms and values that, however, are built into the technological and social features of the nudge, rather than rendering them open to public scrutiny and debate. Although the use of contact tracing apps is being phased out, the digitization of contact tracing can be seen as a case study of the broader trend towards digitization of the provision of health services. Debates of their governance thus have broader implications for the governance of data driven tools deployed for public health purposes in times of crisis.

Tracing and testing multiple generations of contacts to COVID-19 cases: cost-benefit trade-offs.

Traditional contact tracing tests the direct contacts of those who test positive. But, by the time an infected individual is tested, the infection starting from the person may have infected a chain of individuals. Hence, why should the testing stop at direct contacts, and not test secondary, tertiary contacts or even contacts further down? One deterrent in testing long chains of individuals right away may be that it substantially increases the testing load, or does it? We investigate the costs and benefits of such multi-hop contact tracing for different number of hops. Considering diverse contact networks, we show that the cost–benefit trade-off can be characterized in terms of a single measurable attribute, the initial epidemic growth rate. Once this growth rate crosses a threshold, multi-hop contact tracing substantially reduces the outbreak size compared with traditional tracing. Multi-hop even incurs a lower cost compared with the traditional tracing for a large range of values of the growth rate. The cost–benefit trade-offs can be classified into three phases depending on the value of the growth rate. The need for choosing a larger number of hops becomes greater as the growth rate increases or the environment becomes less conducive toward containing the disease.

Privacy and contact tracing efficacy.

As the COVID-19 pandemic emerged, public health authorities and software designers considered the possibility that smartphones could be used for contact tracing to control disease spread. Smartphone-based contact tracing was attractive in part because it promised to allow the tracing of contacts that might not be reported using traditional contact tracing methods. Comprehensive contact tracing raises distinctive privacy concerns, however, that have not been previously explored. Contacts outside of an individual's ordinary social network are more likely to be privacy-sensitive, making fear that such contacts will be disclosed a potential disincentive to adoption of smartphone contact tracing. Here, we modify the standard SEIR infectious disease transmission model to incorporate contact tracing and perform a series of simulations aimed at studying the importance of tracing socially distant (and potentially privacy-sensitive) contacts. We find that, for a simple model network, ensuring that distant contacts are traced is surprisingly unimportant as long as contact tracing adoption is sufficiently high. These results suggest that policy-makers designing contact tracing systems should be willing to trade off comprehensiveness for more widespread adoption.

Evaluating COVID-19 Exposure Notification Effectiveness With SimAEN: A Simulation Tool Designed for Public Health Decision Making.

Exposure notification (EN) supplements traditional contact tracing by using proximity sensors in smartphones to record close contact between persons. This ledger is used to alert persons of potential SARS-CoV-2 exposure, so they can quarantine until their infection status is determined. We describe a model that estimates the impact of EN implementation on reducing the spread of SARS-CoV-2 and on the workload of public health officials, in combination with other key public health interventions such as traditional contact tracing, face mask wearing, and testing. We created an agent-based model, Simulated Automated Exposure Notification (SimAEN), to explore the effectiveness of EN to slow the spread of SARS-CoV-2. We varied selected simulation variables, such as population adoption of EN and EN detector sensitivity configurations, to illustrate the potential effects of EN. We executed 20 simulations with SimAEN for each scenario and derived results for each simulation. When more sensitive versus more specific EN configurations were compared, the effective reproductive number, RE, was minimally affected (a decrease <0.03). For scenarios with increasing levels of EN adoption, an increasing number of additional infected persons were identified through EN, and total infection counts in the simulated population decreased; RE values for this scenario decreased with increasing EN adoption (a decrease of 0.1 to 0.2 depending on the scenario). Estimates from SimAEN can help public health officials determine which levels of EN adoption in combination with other public health interventions can maximize prevention of COVID-19 while minimizing unnecessary quarantine in their jurisdiction.

A methodology for evaluating digital contact tracing apps based on the COVID-19 experience

Controlling the spreading of infectious diseases has been shown crucial in the COVID-19 pandemic. Traditional contact tracing is used to detect newly infected individuals by tracing their previous contacts, and by selectively checking and isolating any individuals likely to have been infected. Digital contact tracing with the utilisation of smartphones was contrived as a technological aid to improve this manual, slow and tedious process. Nevertheless, despite the high hopes raised when smartphone-based contact tracing apps were introduced as a measure to reduce the spread of the COVID-19, their efficiency has been moderately low. In this paper, we propose a methodology for evaluating digital contact tracing apps, based on an epidemic model, which will be used not only to evaluate the deployed Apps against the COVID-19 but also to determine how they can be improved for future pandemics. Firstly, the model confirms the moderate effectiveness of the deployed digital contact tracing, confirming the fact that it could not be used as the unique measure to fight against the COVID-19, and had to be combined with additional measures. Secondly, several improvements are proposed (and evaluated) to increase the efficiency of digital control tracing to become a more useful tool in the future.

COVID-19 collaborative screening: An action-research project for large scale contact tracing in Northern Portugal

In late November 2020, when Europe reached the highest 14-day incidence of COVID-19 cases, the resource-intensive and time-consuming traditional contact tracing performed by Public Health was challenged. In this context, innovative approaches were necessary to guarantee a timely interruption of disease transmission.“COVID-19 Collaborative Screening” Project was developed as a faster solution, not only because the contact tracing process is simpler for the operator, but mainly because it is possible to quickly scale up the number of operators involved. It was designed to interrupt family and social transmission chains, in a partnership with theLocal Public Health Services – allowing these services to dedicate to scenarios of more complex risk assessment, using the traditional contact tracing. To perform contact tracing, this method involves Public Servants, Armed Forces and Medical Dentists. The Project also promotes participatory citizenship, by delegating to the citizen the responsibility of registering his/hers contacts with high-risk exposure in an online form, in contrast to the traditional contact tracing method which is more health professional-dependent.Until the end of January 2021, the Project has trained eight teams, enrolling a total of 213 professionals, and was implemented in eight Health Regions (with an estimated population of 1,346,150 inhabitants).The Project was successful at facing the delays in case interview and contact tracing. The strategy implemented by ColabCOVID is assembled as a sustainable, reproducible and scalable platform and is ready to be re-implemented to face the emergence of more contagious variants, as well as an eventual forthcoming health threat.

Drivers of downloading and reasons for not downloading COVID-19 contact tracing and exposure notification apps: A national cross-sectional survey.

BackgroundBluetooth-enabled smartphone apps have been developed and implemented in different sites globally to help overcome capacity limitations of traditional interview-based COVID-19 contact tracing. Two apps are currently available in Canada: ABTraceTogether exclusively in Alberta and COVID Alert in nine other provinces and territories. This study aims to examine factors associated with downloading of these apps to inform targeted promotion and marketing to increase app uptake.MethodsWe performed a cross-sectional survey with adult participants (≥18 years old) from an online national panel. Participants were asked if they had downloaded an app and, if applicable, reasons for not downloading. Logistic regression was used to identify sociodemographic factors and trusted information sources associated with downloading and reasons for not downloading.ResultsOf the included 4,503 respondents (36% response rate), 1,394 (31%) had downloaded an app. Demographic and socioeconomic factors positively associated with app download were: 1) being female, 2) higher household income, 3) higher education level attained, and 4) more liberal political views. The odds of downloading an app were higher for participants who trusted health-related information sources, and lower for those who trusted internet searches, family and friend, or Facebook. The most cited reasons for not downloading were related to data security concerns and perceived lack of benefit from the apps.InterpretationThese findings identify sociodemographic segments with the lowest app uptake, reasons for not downloading and trusted information sources to inform targeted promotion and marketing strategies to improve uptake of apps to facilitate contact tracing.

A Cluster Analysis of the Acceptance of a Contact Tracing App-The Identification of Profiles for the Italian Immuni Contact Tracing App.

Digital contact tracing apps have been introduced by governments as a strategy to limit the spread of the COVID-19 pandemic. Digital contact tracking is an alternative to traditional contact tracing performed by human tracers who have to reconstruct each contact an infected person had in the recent past by means of interviews. The Italian government proposed the Immuni digital contact tracking app as a solution. Immuni uses Bluetooth technology to anonymously register all close contacts a person had: if she tests positive for COVID-19 then all registered contacts are notified. The main aim of the paper is to propose a cluster analysis of some factors concerning the possible acceptance of the Immuni app to build behaviour profiles that explain and predict the possible behaviours of the respondents. The factors considered referred to three different pillars: the technological pillar, investigated by considering factors from the technology acceptance models family; the health pillar, where variables of the health belief model were used; and the sociopolitical pillar, where some values of the respondents were considered as possible barriers to or facilitators of the acceptance of this technology. As a result of the cluster analysis, three behavioural profiles were built: the ProApp profile, the Hesitant profile, and the AntiApp profile. The first is the profile grouping the respondents who intend to use the contact tracing app; the second is more about people who are favourable of the use of the app, but some issues such as privacy reduce the strength of their intention; the last profile is about people who are less favourable to use the app. We are confident that the behaviour profiles found would be useful to build more tailored communication campaigns to help promote the use of the app by managing factors that could either be facilitators or barriers.

Assessing personal exposure to COVID-19 transmission in public indoor spaces based on fine-grained trajectory data: A simulation study

The coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges to worldwide health systems in quick response to epidemics. The assessment of personal exposure to COVID-19 in enclosed spaces is critical to identifying potential infectees and preventing outbreaks. However, traditional contact tracing methods rely heavily on a manual interview, which is costly and time consuming given the large population involved. With advanced indoor localisation techniques, it is possible to collect people's footprints accurately by locating their smartphones. This study presents a new framework for the assessment of personal exposure to COVID-19 carriers using their fine-grained trajectory data. An integral model was established to quantify the exposure risk, in which the spatial and temporal decay effects are simultaneously considered when modelling the airborne transmission of COVID-19. Regarding the obstacle effect of the indoor layout on airborne transmission, a weight graph based on the space syntax technique was further introduced to constrain the transmission strength between subspaces that are less inter-visible. The proposed framework was demonstrated by a simulation study, in which external comparison and internal analysis were conducted to justify its validity and robustness in different scenarios. Our method is expected to promote the efficient identification of potential infectees and provide an extensible spatial–temporal model to simulate different control measures and examine their effectiveness in a built environment.

DIMY: Enabling privacy-preserving contact tracing

The infection rate of COVID-19 and the rapid mutation ability of the virus has forced governments and health authorities to adopt lockdowns, increased testing, and contact tracing to reduce the virus’s spread. Digital contact tracing has become a supplement to the traditional manual contact tracing process. However, although several digital contact tracing apps are proposed and deployed, these have not been widely adopted due to apprehensions surrounding privacy and security. In this paper, we present a blockchain-based privacy-preserving contact tracing protocol,“Did I Meet You” (DIMY). The protocol provides full-lifecycle data privacy protection on the devices as well as the back-end servers to address most of the privacy concerns associated with existing protocols. We have employed Bloom filters to provide efficient privacy-preserving storage and have used the Diffie–Hellman key exchange for secret sharing among the participants. We show that DIMY provides resilience against many well-known attacks while introducing negligible overheads. DIMY’s footprint on the storage space of clients’ devices and back-end servers is also significantly lower than other similar state-of-the-art apps.

Innovative and Integrated Contact Tracing: Indian Health Service, Arizona, December 2020-January 2021.

SARS-CoV-2 has exposed limitations of public health mitigation measures such as traditional case investigations and contact tracing. The Whiteriver Service Unit is a rural, acute care hospital on the Fort Apache Indian Reservation in Arizona with integrated health care delivery and public health services. During the first wave of COVID-19 cases in May-June 2020, we developed an innovative case investigation contact tracing approach that relied heavily on cross-trained personnel, in-person encounters, and baseline clinical evaluations. A second COVID-19 surge during December 13, 2020-January 31, 2021, caused incidence to peak at 413 cases per 100 000 community members. During that second surge, we investigated all 769 newly identified COVID-19 cases and notified 1911 (99.4%) of 1922 reported contacts. Median time interval from nasopharyngeal specimen collection to both case investigation and contact notification was 0 days (range, 0-5 days and 0-13 days, respectively). Our primary lesson was the importance of cross-trained personnel who integrated tasks along the testing-tracing continuum (eg, in-person interviews, prompt referral for additional testing and evaluation). These successive steps fed forward to identify new cases and their respective contacts. Our innovative community-based approach was both successful and efficient; our experience suggests that when adapted based on local needs, case investigation and contact tracing remain valuable and feasible public health tools, even in rural, resource-limited settings.

A knowledge graph-based method for epidemic contact tracing in public transportation

Contact tracing is an effective measure by which to prevent further infections in public transportation systems. Considering the large number of people infected during the COVID-19 pandemic, digital contact tracing is expected to be quicker and more effective than traditional manual contact tracing, which is slow and labor-intensive. In this study, we introduce a knowledge graph-based framework for fusing multi-source data from public transportation systems to construct contact networks, design algorithms to model epidemic spread, and verify the validity of an effective digital contact tracing method. In particular, we take advantage of the trip chaining model to integrate multi-source public transportation data to construct a knowledge graph. A contact network is then extracted from the constructed knowledge graph, and a breadth-first search algorithm is developed to efficiently trace infected passengers in the contact network. The proposed framework and algorithms are validated by a case study using smart card transaction data from transit systems in Xiamen, China. We show that the knowledge graph provides an efficient framework for contact tracing with the reconstructed contact network, and the average positive tracing rate is over 96%.

Learning from the initial deployment of digital contact tracing apps

Learning from the initial deployment of digital contact tracing apps

Development and Evaluation of Bluetooth Low-Energy Device for Electronic Encounter Metrics.

The coronavirus disease 2019 (COVID-19) pandemic led to the need for tracking of physical contacts and potential exposure to disease. Traditional contact tracing can be augmented by electronic tools called "electronic contact tracing" or "exposure notification.". Some methods were built to work with smartphones; however, smartphones are not prevalent in some high-contact areas (e.g., schools and nursing homes). We present the design and initial testing of low-cost, highly privacy preserving wearable exposure notification devices. Several devices were constructed based on existing hardware and operated independently of a smartphone. The method (devices and analyses) was not able to reliably use the received signal strength indicator (RSSI) as a proxy for distance between pairs of devices; the accuracy of RSSI as a proxy for distance decreased dramatically outside of the idealized conditions. However, even an imperfect device could be useful for research on how people use and move through spaces. With some improvement, these devices could be used to understand disease spread and human or animal interaction in indoor environments.

A Case Study of Bluetooth Technology as a Supplemental Tool in Contact Tracing.

We present results from a 7-day trial of a Bluetooth-enabled card by the New Zealand Ministry of Health to investigate its usefulness in contact tracing. A comparison of the card with traditional contact tracing, which relies on self-reports of contacts to case investigators, demonstrated significantly higher levels of internal consistency in detected contact events by Bluetooth-enabled cards with 88% of contact events being detected by both cards involved in an interaction as compared to 64% for self-reports of contacts to case investigators. We found no clear evidence of memory recall worsening in reporting contact events that were further removed in time from the date of a case investigation. Roughly 66% of contact events between trial participants that were indicated by cards went unreported to case investigators, simultaneously highlighting the shortcomings of traditional contact tracing and the value of Bluetooth technology in detecting contact events that may otherwise go unreported. At the same time, cards detected only 65% of self-reported contact events, in part due to increasing non-compliance as the study progressed. This would suggest that Bluetooth technology can only be considered as a supplemental tool in contact tracing and not a viable replacement to traditional contact tracing unless measures are introduced to ensure greater compliance.

Models for Digitally Contact-Traced Epidemics

Contacts between people are the absolute drivers of contagious respiratory infections. For this reason, limiting and tracking contacts is a key strategy for the control of the COVID-19 epidemic. Digital contact tracing has been proposed as an automated solution to scale up traditional contact tracing. However, the required penetration of contact tracing apps within a population to achieve a desired target in the control of the epidemic is currently under discussion within the research community. In order to understand the effects of digital contact tracing, several mathematical models have been proposed. In this article, we survey the main ones and we propose a compartmental SEIR model with which it is possible, differently from the models in the related literature, to derive closed-form conditions regarding the control of the epidemic as a function of the contact tracing apps penetration and the testing efficiency. Closed-form conditions are crucial for the understandability of models, and thus for decision makers (including digital contact tracing designers) to correctly assess the dependencies within the epidemic. With our model, we find that digital contact tracing alone can rarely tame an epidemic: for unrestrained COVID-19, this would require a testing turnaround of around 1 day and app uptake above 80% of the population, which are very difficult to achieve in practice. However, digital contact tracing can still be effective if complemented with other mitigation strategies, such as social distancing and mask-wearing.

Public opinion of the Irish "COVID Tracker" digital contact tracing App: A national survey.

ObjectiveThis study aims to gather public opinion on the Irish “COVID Tracker” digital contact tracing (DCT) App, with particular focus on App usage, usability, usefulness, technological issues encountered, and potential changes to the App.MethodsA 35-item online questionnaire was deployed for 10 days in October 2020, 3 months after the launch of the Irish DCT App.ResultsA total of 2889 completed responses were recorded, with 2553 (88%) respondents currently using the App. Although four in five users felt the App is easy to download, is easy to use and looks professional, 615 users (22%) felt it had slowed down their phone, and 757 (28%) felt it had a negative effect on battery life. Seventy-nine percent of respondents reported the App's main function is to aid contact tracing. Inclusion of national COVID-19 trends is a useful ancillary function according to 87% of respondents, and there was an appetite for more granular local data. Overall, 1265 (44%) respondents believed the App is helping the national effort, while 1089 (38%) were unsure.ConclusionsDCT Apps may potentially augment traditional contact tracing methods. Despite some reports of negative effects on phone performance, just 7% of users who have tried the App have deleted it. Ancillary functionality, such as up-to-date regional COVID-19, may encourage DCT App use. This study describes general positivity toward the Irish COVID Tracker App among users but also highlights the need for transparency on effectiveness of App-enabled contact tracing and for study of non-users to better establish barriers to use.

Understanding SARS-CoV-2 Transmission: Linking Traditional Contact Tracing with Genomic Surveillance of a SARS-CoV-2 Student Cluster on a Large Urban Campus

Understanding SARS-CoV-2 Transmission: Linking Traditional Contact Tracing with Genomic Surveillance of a SARS-CoV-2 Student Cluster on a Large Urban Campus

Using Venn Diagrams to Evaluate Digital Contact Tracing: Panel Survey Analysis.

BackgroundMitigation of the spread of infection relies on targeted approaches aimed at preventing nonhousehold interactions. Contact tracing in the form of digital proximity tracing apps has been widely adopted in multiple countries due to its perceived added benefits of tracing speed and breadth in comparison to traditional manual contact tracing. Assessments of user responses to exposure notifications through a guided approach can provide insights into the effect of digital proximity tracing app use on managing the spread of SARS-CoV-2.ObjectiveThe aim of this study was to demonstrate the use of Venn diagrams to investigate the contributions of digital proximity tracing app exposure notifications and subsequent mitigative actions in curbing the spread of SARS-CoV-2 in Switzerland.MethodsWe assessed data from 4 survey waves (December 2020 to March 2021) from a nationwide panel study (COVID-19 Social Monitor) of Swiss residents who were (1) nonusers of the SwissCovid app, (2) users of the SwissCovid app, or (3) users of the SwissCovid app who received exposure notifications. A Venn diagram approach was applied to describe the overlap or nonoverlap of these subpopulations and to assess digital proximity tracing app use and its associated key performance indicators, including actions taken to prevent SARS-CoV-2 transmission.ResultsWe included 12,525 assessments from 2403 participants, of whom 50.9% (1222/2403) reported not using the SwissCovid digital proximity tracing app, 49.1% (1181/2403) reported using the SwissCovid digital proximity tracing app and 2.5% (29/1181) of the digital proximity tracing app users reported having received an exposure notification. Most digital proximity tracing app users (75.9%, 22/29) revealed taking at least one recommended action after receiving an exposure notification, such as seeking SARS-CoV-2 testing (17/29, 58.6%) or calling a federal information hotline (7/29, 24.1%). An assessment of key indicators of mitigative actions through a Venn diagram approach reveals that 30% of digital proximity tracing app users (95% CI 11.9%-54.3%) also tested positive for SARS-CoV-2 after having received exposure notifications, which is more than 3 times that of digital proximity tracing app users who did not receive exposure notifications (8%, 95% CI 5%-11.9%).ConclusionsResponses in the form of mitigative actions taken by 3 out of 4 individuals who received exposure notifications reveal a possible contribution of digital proximity tracing apps in mitigating the spread of SARS-CoV-2. The application of a Venn diagram approach demonstrates its value as a foundation for researchers and health authorities to assess population-level digital proximity tracing app effectiveness by providing an intuitive approach for calculating key performance indicators.