Contact Tracing System Research Articles

A Framework for Infectious Disease Monitoring With Automated Contact Tracing—A Case Study of COVID-19

Throughout human history, deadly infectious diseases emerged occasionally. Even with the present-day advanced healthcare systems, the COVID-19 has caused more than six million deaths worldwide (as of 27 July 2022). Currently, researchers are working to develop tools for better and effective management of the pandemic. “Contact tracing” is one such tool to monitor and control the spread of the disease. However, manual contact tracing is labor-intensive and time-consuming. Therefore, manually tracking all potentially infected individuals is a great challenge, especially for an infectious disease like COVID-19. To date, many digital contact tracing applications were developed and used globally to restrain the spread of COVID-19. In this work, we perform a detailed review of the current digital contact tracing technologies. We mention some of their key limitations and propose a fully integrated system for contact tracing of infectious diseases using COVID-19 as a case study. Our system has four main modules—1) case maps; 2) exposure detection; 3) screening; and 4) health indicators that take multiple inputs like users’ self-reported information, measurement of physiological parameters, and information of the confirmed cases from the public health, and keeps a record of contact histories using Bluetooth technology. The system can potentially evaluate the users’ risk of getting infected and generate notifications to alert them about the exposure events, risk of infection, or abnormal health indicators. The system further integrates the Web-based information on confirmed COVID-19 cases and screening tools, which potentially increases the adoption rate of the system.

Android Based Digital Contact Tracing and Health Monitoring System for Covid 19 Pandemic

"ENGINEER", established in 1973 by The Institution of Engineers, is a Journal for dissemination of Engineering knowledge, published quarterly.If you are interested in contributing an Original Technical Paper based on research by the author(s) (intended for Section I) or an Original Article of Professional or Technical interest related to Engineering. (Section II) to this journal please go through the publication checklist which can be downloaded from here.Cover DescriptionCover images show a few views of the Maduru Oya reservoir and construction of right bank canal. The Institution of Engineers, Sri Lanka, acknowledges Eng. L.D. Amila Sanjeewa of the Department of Irrigation for providing images and related information of the project.

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones.

Due to the prevalence of COVID-19, providing safe environments and reducing the risks of virus exposure play pivotal roles in our daily lives. Contact tracing is a well-established and widely-used approach to track and suppress the spread of viruses. Most digital contact tracing systems can detect direct face-to-face contact based on estimated proximity, without quantifying the exposed virus concentration. In particular, they rarely allow for quantitative analysis of indirect environmental exposure due to virus survival time in the air and constant airborne transmission. In this work, we propose an indoor spatiotemporal contact awareness framework (iSTCA), which explicitly considers the self-containing quantitative contact analytics approach with spatiotemporal information to provide accurate awareness of the virus quanta concentration in different origins at various times. Smartphone-based pedestrian dead reckoning (PDR) is employed to precisely detect the locations and trajectories for distance estimation and time assessment without the need to deploy extra infrastructure. The PDR technique we employ calibrates the accumulative error by identifying spatial landmarks automatically. We utilized a custom deep learning model composed of bidirectional long short-term memory (Bi-LSTM) and multi-head convolutional neural networks (CNNs) for extracting the local correlation and long-term dependency to recognize landmarks. By considering the spatial distance and time difference in an integrated manner, we can quantify the virus quanta concentration of the entire indoor environment at any time with all contributed virus particles. We conducted an extensive experiment based on practical scenarios to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.7 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment.

Living in the era of codes: a reflection on China’s health code system

This article offers a critical analysis of China’s health code system, a data-powered pandemic control and contact tracing system that supposedly subjects all individuals in the country to its panopticon control, a surveillance system that monitors and categorises the Chinese population into the healthy (green), the dubious (yellow), and the unhealthy (red). The article highlights the pretence of surveillance as care and the digital divide that normalises discrimination against the elderly and other digitally left-behind population. It also illustrates how, from policy making and technological design to user engagement, the health code system is implemented, optimised, and used in everyday life to meet the needs of the vulnerable population. The health code is better taken as a medium of adaptable and communicative process that can reset the relation between the system and the lifeworld. It is the process of interchange between the system and the lifeworld that deserves our critical attention.

Modeling the impact of surveillance activities combined with physical distancing interventions on COVID-19 epidemics at a local level.

Physical distancing and contact tracing are two key components in controlling the COVID-19 epidemics. Understanding their interaction at local level is important for policymakers. We propose a flexible modeling framework to assess the effect of combining contact tracing with different physical distancing strategies. Using scenario tree analyses, we compute the probability of COVID-19 detection using passive surveillance, with and without contact tracing, in metropolitan Barcelona. The estimates of detection probability and the frequency of daily social contacts are fitted into an age-structured susceptible-exposed-infectious-recovered compartmental model to simulate the epidemics considering different physical distancing scenarios over a period of 26 weeks. With the original Wuhan strain, the probability of detecting an infected individual without implementing physical distancing would have been 0.465, 0.515, 0.617, and 0.665 in designated age groups (0-14, 15-49, 50-64, and >65), respectively. As the physical distancing measures were reinforced and the disease circulation decreased, the interaction between the two interventions resulted in a reduction of the detection probabilities; however, despite this reduction, active contact tracing and isolation remained an effective supplement to physical distancing. If we relied solely on passive surveillance for diagnosing COVID-19, the model required a minimal 50% (95% credible interval, 39-69%) reduction of daily social contacts to keep the infected population under 5%, as compared to the 36% (95% credible interval, 22-56%) reduction with contact tracing systems. The simulation with the B.1.1.7 and B.1.167.2 strains shows similar results. Our simulations showed that a functioning contact tracing program would reduce the need for physical distancing and mitigate the COVID-19 epidemics.

Security and Privacy Threats and Requirements for the Centralized Contact Tracing System in Korea

As COVID-19 became a pandemic worldwide, contact tracing technologies and information systems were developed for quick control of infectious diseases in both the private and public sectors. This study aims to strengthen the data subject’s security, privacy, and rights in a centralized contact tracing system adopted for a quick response to the spread of infectious diseases due to climate change, increasing cross-border movement, etc. There are several types of contact tracing systems: centralized, decentralized, and hybrid models. This study demonstrates the privacy model for a centralized contact tracing system, focusing on the case in Korea. Hence, we define security and privacy threats to the centralized contact tracing system. The threat analysis involved mapping the threats in ITU-T X.1121; in order to validate the defined threats, we used LIDDUN and STRIDE to map the threats. In addition, this study provides security requirements for each threat defined for more secure utilization of the centralized contact tracing system.

Digital geographies of the bug: A case study of China's contact tracing systems in the COVID-19

The COVID-19 pandemic has radically expanded the role of algorithmic governance in everyday mobility. In China, urban and provincial governments have introduced health codes app as a national contract tracing and quarantine enforcement method to restrict the movements of “risky” individuals through malls, subways, railways, as well as between regions. Yet the health codes have been implemented with uneven efficacy and unexpected consequences. Drawing on glitch politics, we read these unintended consequences as “bugs” emerging from the introduction of platform-based management into everyday life. These bugs mediated individuals' lived experiences of the digital app and the hybrid space constituted by population governance, individual digital navigation, and technology. Drawing on a database of posts scraped from Zhihu, a popular Chinese question-and-answer site, we examine three dimensions of the bug: the algorithmic bug, the territorial bug, and the corporeal bug. This paper sheds light on the significance of end-user experiences in digital infrastructure and contributes to our understanding of the digital geographies of bugs in algorithmic governance and platform urbanism.

CoAvoid: Secure, Privacy-Preserved Tracing of Contacts for Infectious Diseases

To fight against infectious diseases (e.g., SARS, COVID-19, Ebola, etc.), government agencies, technology companies and health institutes have launched various contact tracing approaches to identify and notify the people exposed to infection sources. However, existing tracing approaches can lead to severe privacy and security concerns, thereby preventing their secure and widespread use among communities. To tackle these problems, this paper proposes CoAvoid, a decentralized, privacy-preserved contact tracing system that features good dependability and usability. CoAvoid leverages the Google/Apple Exposure Notification (GAEN) API to achieve decent device compatibility and operating efficiency. It utilizes GPS along with Bluetooth Low Energy (BLE) to dependably verify user information. In addition, to enhance privacy protection, CoAvoid applies fuzzification and obfuscation measures to shelter sensitive data, making both servers and users agnostic to information of both low and high-risk populations. The evaluation demonstrates good efficacy and security of CoAvoid. Compared with four state-of-art contact tracing applications, CoAvoid can reduce upload data by at least 90% and simultaneously resist wormhole and replay attacks in various scenarios.

Confidentiality-Preserving Edge-Based Wireless Communications for Contact Tracing Systems

This paper addresses two issues of a contact-tracing system with edge-based wireless communication techniques: to locate close contacts and to provide confidentiality-preserving communications. In this paper, we propose the Confidentiality-Preserving Contact-Tracing (CPCT) system with multiple wireless edge-based nodes. The CPCT system consists of three stages: registration, authentication, and confidentiality-preserving communication stages. We propose two Physical-Layer Authentication (PLA) schemes for the authentication stage of the CPCT system: the Coordinate-based Location PLA (CLP) scheme using the estimated coordinates and the Time-of-Arrival (ToA) based Location PLA (TLP) scheme using the estimated ToAs. We propose a distributed encryption scheme for the confidentiality-preserving communication stage of the CPCT system named the Distributed Channel Impulse Response (CIR)-based Encryption (DCE) scheme. We provide the theoretical analysis of the proposed schemes and derive their closed-form expressions. We implement the proposed schemes and conduct extensive performance comparisons through simulations. We observe that the theoretical results perfectly match the corresponding simulation results. Moreover, the proposed PLA schemes provide higher authentication performance than the prior PLA scheme, while the proposed encryption scheme provides higher confidentiality performance than the prior encryption scheme.

Decentralized multi-client functional encryption for set intersection with improved efficiency

Functional encryption (FE) is a new paradigm of public key encryption that can control the exposed information of plaintexts by supporting computation on encrypted data. In this paper, we propose efficient multi-client FE (MCFE) schemes that compute the set intersection of ciphertexts generated by two clients. First, we propose an MCFE scheme that calculates the set intersection cardinality (MCFE-SIC) and prove its static security under dynamic assumptions. Next, we extend our MCFE-SIC scheme to an MCFE scheme for set intersection (MCFE-SI) and prove its static security under dynamic assumptions. The decryption algorithm of our MCFE-SI scheme is more efficient than the existing MCFE-SI scheme because it requires fewer pairing operations to calculate the intersection of two clients. Finally, we propose a decentralized MCFE scheme for set intersection (DMCFE-SI) that decentralizes the generation of function keys. Our MCFE schemes can be effectively applied to a privacy-preserving contact tracing system to prevent the spread of recent infectious diseases.

Network analysis of a COVID-19 outbreak in the west of Ireland in February 2021

Abstract Notification and contact tracing systems of COVID-19 hold a vast amount of information on transmission chains of the virus. It can be hard to gain an understanding of these due to the number of individuals involved. Network analysis can be used as a method of visualising these systems, gaining understanding of transmission chains and as a potential tool for monitoring outbreaks. It can link cases together and show how far reaching initial infections were. Here, a system developed in the programming language R links the Irish infectious disease notification system and contact tracing system together and creates a network representation of the result. The system finds any cases or close contacts linked in any manner to the known cases. The result is a network from the earliest found case to the latest found case or contact related to the outbreak. A large outbreak of COVID-19 occurred in the student population in the West of Ireland in February 2021 with 449 cases linked to it by the Department of Public Health at the time. Using the system, 192 further positive cases were found to be linked to the outbreak. A total of 1,431 individuals were linked in some manner to it with 68% in the 19-24 age group and less than 1% in the 65+ age group. This takes a matter of seconds to run and highlights clusters within the outbreak, the largest of which had 96 cases and 121 not detected close contacts. Visualising the transmission chains also showed that there were no other large clusters outside of the outbreak at the time. A system such as this can link cases to outbreaks not previously linked, dramatically reduce the time taken to link cases together and visualise transmission chains to gain a deeper understanding of what is happening. This automated system frees up resources to allow for deeper investigation into cases and situations of concern. It also has the potential to link outbreaks together and spot previously unnoticed situations of concern. Key messages • Network analysis is beneficial for the monitoring of the spread of an infectious disease like COVID-19. • Here, 192 cases were found to be linked to an outbreak that was thought to have 449 cases in it.

Contact Tracing Strategies for COVID-19 Prevention and Containment: A Scoping Review

Coronavirus Disease 2019 (COVID-19) spreads rapidly and is easily contracted by individuals who come near infected persons. With this nature and rapid spread of the contagion, different types of research have been conducted to investigate how non-pharmaceutical interventions can be employed to contain and prevent COVID-19. In this review, we analyzed the key elements of digital contact tracing strategies developed for the prevention and containment of the dreaded epidemic since its outbreak. We carried out a scoping review through relevant studies indexed in three databases, namely Google Scholar, PubMed, and ACM Digital Library. Using some carefully defined search terms, a total of 768 articles were identified. The review shows that 86.32% (n = 101) of the works focusing on contact tracing were published in 2020, suggesting there was an increased awareness that year, increased research efforts, and the fact that the pandemic was given a very high priority by most journals. We observed that many (47.86%, n = 56) of the studies were focused on design and implementation issues in the development of COVID-19 contact tracing systems. In addition, has been established that most of the studies were conducted in 41 countries and that contract tracing app development are characterized by some sensitive issues, including privacy-preserving and case-based referral characteristics.

Using next generation matrices to estimate the proportion of infections that are not detected in an outbreak

Contact tracing, where exposed individuals are followed up to break ongoing transmission chains, is a key pillar of outbreak response for infectious disease outbreaks. Unfortunately, these systems are not fully effective, and infections can still go undetected as people may not remember all their contacts or contacts may not be traced successfully. A large proportion of undetected infections suggests poor contact tracing and surveillance systems, which could be a potential area of improvement for a disease response. In this paper, we present a method for estimating the proportion of infections that are not detected during an outbreak. Our method uses next generation matrices that are parameterized by linked contact tracing data and case line-lists. We validate the method using simulated data from an individual-based model and then investigate two case studies: the proportion of undetected infections in the SARS-CoV-2 outbreak in New Zealand during 2020 and the Ebola epidemic in Guinea during 2014. We estimate that only 5.26% of SARS-CoV-2 infections were not detected in New Zealand during 2020 (95% credible interval: 0.243 – 16.0%) if 80% of contacts were under active surveillance but depending on assumptions about the ratio of contacts not under active surveillance versus contacts under active surveillance 39.0% or 37.7% of Ebola infections were not detected in Guinea (95% credible intervals: 1.69 – 87.0% or 1.70 – 80.9%).

Strengthening government's response to COVID-19 in Indonesia: A modified Delphi study of medical and health academics.

The Indonesian government has issued various policies to control COVID-19. However, COVID-19 new cases continued to increase, and there remain uncertainties as to the future trajectory. We aimed to investigate how medical and health academics view the Indonesian government's handling of COVID-19 and which areas of health systems need to be prioritized to improve the government's response to COVID-19. We conducted a modified Delphi study adapting the COVID-19 assessment scorecard (COVID-SCORE) as the measurement criteria. We invited medical and health academics from ten universities across Indonesia to take part in the two-round Delphi study. In the first round, participants were presented with 20 statements of COVID-SCORE and asked to rate their agreement with each statement using a five-point Likert scale. All participants who completed the first cycle were invited to participate in the second cycle. They had the opportunity to revise their answers based on the previous cycle's results and ranked a list of actions to improve government response. We achieved a moderate consensus level for five statements, a low consensus level for 13 statements and no consensus for two statements. The prioritization suggested that top priorities for improving the government's response to COVID-19 in Indonesia encompass: (1) strengthening capacity to ensure consistent, credible and targeted communication while adopting a more inclusive and empathic communication style to address public concerns; (2) ensuring universal access to reliable COVID-19 testing by expanding lab infrastructure, facilitating operational readiness, and scaling up implementation of proven alternative/complementary tests to RT-PCR; and (3) boosting contact tracing implementation capacity and facilitating contact tracing for all positive cases, involving key stakeholders in further development of the existing contact tracing system (i.e. PeduliLindungi) as well as its evaluation and quality assurance. Ultimately, our study highlights the importance of strengthening health system functions during the pandemic and improving health system resilience for dealing with future public health emergencies.

Internet of things (IoT) imbedded point-of-care SARS-CoV-2 testing in the pandemic and post-pandemic era

The outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant in China have revealed a high rate of asymptomatic cases, making isolation and quarantine measures exceedingly difficult. Public health surveillance and intervention measures will require rapid and accurate testing preferably on-site using point-of-care tests (POCTs) technology for SARS-CoV-2 variants. However, delayed and/or inaccurate surveillance data is a major obstacle blocking the large-scale implementation of POCTs in curbing spread of infectious pathogens and reducing mortality during an outbreak. To determine levels of community transmission and timely strategies accordingly, highly sensitive and specific POCT embedded with the internet of things (IoT) technology could enable on-site screening and real-time data collection. A new Rapid Amplification with Sensitivity And Portability point-of-care test (RASAP-POCT) system based on thermal convection PCR is the first IoT-based isothermal nucleic acid amplification POCT, which can provide test results within 20–30 min using saliva and/or nasopharyngeal swab samples without nucleic acid extraction. With the IoT-imbedded feature, the RASAP-POCT system can be integrated easily and smoothly with China’s existing mobile-phone-based contact tracing system, which has previously proved to be highly effective in maintaining the dynamic zero-COVID policy. Current regulatory guidelines and rules should be modified to accelerate the adoption of new technologies under an emergency use authorization (EUA).

The Necessity of Close Contact Tracing in Combating COVID-19 Infection – A Systemic Study

Many contact tracing solutions developed by countries around the globe in containing the Covid-19 pandemic are in the area of location-based tracing, which does not enable them to identify close contacts accurately. As location-based tracing implementations continuous on, the results have not been as effective as intended. Thus, in providing some closure, this study will dissect the need for close contact tracing solutions for the pandemic by providing a comprehensive contact tracing characteristic framework (CCTCF) for Covid-19, which will help authorities toward better pandemic management. In this study, CCTCF for Covid-19 was constructed by applying several methods. Using Problem, Intervention, Comparison, Outcome (PICO) as the framework, methods conducted were: (1) Case study to analyze the contact tracing systems in 30 countries; (2) Systematic literature review (n=2056) regarding solutions’ elements, (3) Thematic analysis for characteristics framework development. A total of 25 items were obtained for CCTCF, along with valuable insights that necessitate close contact tracing for the pandemic. Results from CCTCF have also shown that the best contact tracing solution for Covid-19 is bi-directional human-to-human close contact tracing, which uses a retrospective approach and is able to identify the source as well as groups of infection using a personal area network (PAN). Doi: 10.28991/esj-2022-SPER-019 Full Text: PDF

Cellular-Assisted, Deep Learning Based COVID-19 Contact Tracing

The Coronavirus disease (COVID-19) pandemic has caused social and economic crisis to the globe. Contact tracing is a proven effective way of containing the spread of COVID-19. In this paper, we propose CAPER, a Cellular-Assisted deeP lEaRning based COVID-19 contact tracing system based on cellular network channel state information (CSI) measurements. CAPER leverages a deep neural network based feature extractor to map cellular CSI to a neural network feature space, within which the Euclidean distance between points strongly correlates with the proximity of devices. By doing so, we maintain user privacy by ensuring that CAPER never propagates one client's CSI data to its server or to other clients. We implement a CAPER prototype using a software defined radio platform, and evaluate its performance in a variety of real-world situations including indoor and outdoor scenarios, crowded and sparse environments, and with differing data traffic patterns and cellular configurations in common use. Microbenchmarks show that our neural network model runs in 12.1 microseconds on the OnePlus 8 smartphone. End-to-end results demonstrate that CAPER achieves an overall accuracy of 93.39%, outperforming the accuracy of BLE based approach by 14.96%, in determining whether two devices are within six feet or not, and only misses 1.21% of close contacts. CAPER is also robust to environment dynamics, maintaining an accuracy of 92.35% after running for ten days.

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.

Effective Contact Tracing System Minimizes COVID-19 Related Infections and Deaths: Policy Lessons to Reduce the Impact of Future Pandemic Diseases

One of the fundamental questions in the presence of Coronavirus Diseases 2019 (COVID-19) pandemic crisis and in general of new pandemic diseases is to design effective policy responses to reduce the impact in the initial phase of diffusion, when appropriate therapies and drugs lack. This study analyses a main case study given by Italy, one of the first European countries to be damaged of the impact of COVID-19 pandemic. In particular, this study focuses on health policy responses to the pandemic crisis across selected Italian regions that were the first areas to experience a rapid increase in confirmed cases and deaths of COVID-19. The analysis of early regional health policies, from January to July 2020 (during the first wave of the COVID-19 pandemic), reveals that some regions have managed pandemic crisis with appropriate health policy responses based on: a) a timely and widespread testing of individuals, b) effective units of epidemiological investigation in a pervasive contact-tracing system to detect and isolate all infected people. This health policy response has reduced total deaths and negative effects of COVID-19 on health of people during the first pandemic wave, when are not available pharmaceutical interventions, such as vaccines and other antiviral drugs. This evidence in the first wave of COVID-19 pandemic provides important lessons to design an effective public health policy to constraint future pandemic waves driven by new variants and new viral agents, when appropriate drugs are not ready.

Developing A Conceptual Passive Contact Tracing System for Commercial Buildings Using WiFi Indoor Positioning

Contact tracing is one of the critical tools for fighting against pandemic disease outbreaks, such as the fast-growing SARS-CoV-2 virus and its different variants. At present, automated contact tracing systems face two main challenges: (1) requiring application installation on smart devices and (2) protecting the users’ privacy. This study introduces a conceptual passive contact tracing system using indoor WiFi positioning to address these challenges and investigate the role of such a system in commercial buildings. In this regard, this study uses a simulated small-office layout in a case study to demonstrate the applicability of the proposed system. The special use of the proposed contact tracing system could be academic facilities and office buildings, where (1) the WiFi infrastructure already exists and therefore implementing such a system could be cost-effective, and (2) the same users use the facility regularly, enabling the system to notify the users upon a confirmed case once they are back in the building and connected to the WiFi system. Such technology can not only enhance the current automated contact tracing system in commercial buildings by illuminating the need to use smartphone applications while protecting users’ privacy, but could also reduce the risk of infection in indoor environments. The developed system can benefit facility managers, business owners, policy makers, and authorities in assisting to find occupants’ high-risk contacts and control the spread of SARS-CoV-2 or similar infectious diseases in commercial buildings, particularly university campuses and office buildings.