Contact Tracing refers to the process of tracking persons who may have come into spatial contact with an infected person and subsequently collecting further information about these contacts. The feature-rich interaction, processing, and localization/communication modalities of smartphone devices have brought these to the technological forefront to battle and curb the fast spread of pandemics, e.g., COVID-19, introducing the notion of “digital vaccines” to custodians aiming at fast prevention rather than slow intervention.

The community has to this date proposed a wide range of approaches, ranging from opportunistic to participatory approaches, privacy-sensitive to no-privacy approaches, handheld-based (distributed) to cloud-based (centralized) approaches, and proximity-based (e.g., BLE, sound) to location-based approaches (e.g., Wi-Fi, GPS) for only outdoor settings to indoor settings, using closed-source to open-source counterparts. However, a wide range of challenges remain unanswered, including methodologies to improve the penetration and adoption rates, alleviating privacy or expectation skepticism, the ubiquitous availability on low-end terminals as well as technological/psychological adoption barriers, achieving cross-country interoperability with standard formations beyond recommendations, scalability/reliability and accuracy verification of engaged spatial technologies, and lessons about the effectiveness from real large-scale deployments. This special issue brings together transdisciplinary researchers and practitioners working in topics relevant to the special issue.

This issue of ACM TSAS includes the five accepted papers to the Special Issue on Contact Tracing. The articles are not listed in any particular order of preference.

In the first article, titled “A Survey on Contact Tracing: The Latest Advancements and Challenges,” Jiang et al. provide a comprehensive survey on contact tracing, covering both the traditional and the recent digital contact tracing technologies. The fundamental approach and models of contact tracing are covered, and then the authors survey the latest digital contact tracing technologies, detailing a variety of contact tracing mobile applications currently being used in many different countries together with the associated positioning technologies. At the end of the survey, the authors reflect on the current challenges and highlight several future trends. The given work provides a good introduction to readers before moving on to the subsequent technical articles.

In the second article, titled “Towards Accurate Spatiotemporal COVID-19 Risk Scores Using High-Resolution Real-world Mobility Data,” Rambhatla et al. present risk scores based on location density and mobility behavior that model the propensity of the disease, going beyond contact tracing that aims to track past activities of infected users. The article presents a Hawkes process-based technique to assign fine-grained spatial and temporal risk scores by leveraging high-resolution mobility data based on cell-phone-originated location signals. The authors demonstrate the efficacy of the developed risk scores via simulated contacts and disease spread based on real-world mobility data, and verify that the risk scores can provide useful insights and facilitate safe reopening.

In the third article, titled “ASTRO: Reducing COVID-19 Exposure through Contact Prediction and Avoidance,” Anastasiou et al. deal with path recommendations to reduce COVID-19 exposure in indoor/outdoor navigation scenarios. Particularly, the authors propose Accessible Spatio-temporal Route Optimization (ASTRO), a graph-based path discovering algorithm that can reduce the risk of COVID-19 exposure by taking into consideration the congestion in indoor spaces. ASTRO uses an A*-based algorithm to find the most promising path for safe movement within and across multiple buildings without constructing the full graph. For its path finding, ASTRO requires predicting congestion in corridors and hallways. For this purpose, the authors propose the CM-Structure, a grid-based partitioning scheme that is combined with a hash-based structure to store congestion models. The effectiveness of the proposition is verified with a variety of realistic datasets showing that it can reduce COVID-19 exposure by an order of magnitude.

In the fourth article, titled “Another Look at Privacy-preserving Automated Contact Tracing,” Tang points to the inevitable privacy leakages in existing BLE-based contact tracing solutions and proposes a venue-based contact tracing concept. The main idea is to only monitor users’ contacting history in virus-spreading-prone venues, which would also incorporate BLE and Wi-Fi location tracking technologies. The article demonstrates that the venue-based contact tracing concept can mitigate most of the issues that have been identified in existing solutions.

Last but not the least, in the fifth article, titled “PCT-TEE: Trajectory-based Private Contact Tracing System with Trusted Execution Environment,” Kato et al. deal with indirect contact tracing (e.g., people using the same elevator at successive times but without direct contact). Particularly, the authors propose an efficient contact tracing system exploiting the trajectories of users for private contact tracing (i.e., trajectories of user and the infected patients). The authors start out by formalizing their problem as the Spatio-temporal Private Set Intersection problem and solve it by means of a Trusted Execution Environment (TEE), part of the Intel SGX extension to the Intel x86 instruction set. Particularly, the authors design algorithms for spatiotemporal private set intersection under limited secure memory of TEE (usually limited to 128MB). The proposed TEE-based system provides flexible trajectory data encoding algorithms and is evaluated using real-world data with some very encouraging results for secure contact tracing.

We hope that this special issue has brought forward an interesting collection of articles on a timely topic that has impacted the life of the whole planet during the pandemic. We hope that the readers enjoy reading these articles and find them interesting. We are convinced that this collection of articles will facilitate further research in the exciting areas of spatial algorithms and systems that have opened up.