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

   https://doi.org/10.1145/3550332  

   Yi, Fan; Xie, Yaxiong; Jamieson, Kyle (September 2022, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   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. 

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2. REACT: real-time contact tracing and risk monitoring using privacy-enhanced mobile tracking

   https://doi.org/10.1145/3431843.3431845  

   Xiong, Li; Shahabi, Cyrus; Da, Yanan; Ahuja, Ritesh; Hertzberg, Vicki; Waller, Lance; Jiang, Xiaoqian; Franklin, Amy (October 2020, SIGSPATIAL Special)

   null (Ed.)

   Contact tracing is an essential public health tool for controlling epidemic disease outbreaks such as the COVID-19 pandemic. Digital contact tracing using real-time locations or proximity of individuals can be used to significantly speed up and scale up contact tracing. In this article, we present our project, REACT, for REAal-time Contact Tracing and risk monitoring via privacy-enhanced tracking of users' locations and symptoms. With privacy enhancement that allows users to control and refine the precision with which their information will be collected and used, REACT will enable: 1) contact tracing of individuals who are exposed to infected cases and identification of hot-spot locations, 2) individual risk monitoring based on the locations they visit and their contact with others; and 3) community risk monitoring and detection of early signals of community spread. We will briefly describe our ongoing work and the approaches we are taking as well as some challenges we encountered in deploying the app. 

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3. Health Status Discovery for Online Bidirectional Contact Tracing and Disease Aware Navigation

   https://doi.org/10.1109/CAI64502.2025.00084  

   Hurtado, Sofia; Marculescu, Radu (May 2025, IEEE)

   The effectiveness of digital contact tracing during extended outbreaks of airborne infectious diseases, such as COVID-19, influenza, or RSV, can be hindered by limited social compliance and delays in real-world testing. Prior work has shown the utility of graph learning for bidirectional contact tracing and multi-agent reinforcement learning (MARL) for disease mitigation; however, they rely on post-hoc analysis and full testing compliance, thus limiting real-time applicability. To address these limitations, we propose a new framework for online automated bidirectional contact tracing and disease-aware navigation. Our framework iteratively identifies infectious culprits, infers individual health statuses, and deploys agents to minimize infectious exposure without requiring Oracle health information. Our proposed framework achieves an average online backwards tracing F1-score of 92% and estimates the total case counts within 5% accuracy, even under conditions of probabilistic testing with significant social hesitancy. Additionally, our proposed agent-based navigation system can reduce the disease spread by 29%. These results demonstrate the framework’s potential to address critical gaps in traditional disease surveillance and mitigation models and improve real-time public health interventions. 

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4. Comparing Perspectives Around Human and Technology Support for Contact Tracing

   https://doi.org/10.1145/3411764.3445669  

   Lu, Xi; L. Reynolds, Tera; Jo, Eunkyung; Hong, Hwajung; Page, Xinru; Chen, Yunan; A. Epstein, Daniel (May 2021, Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems)

   null (Ed.)

   Various contact tracing approaches have been applied to help contain the spread of COVID-19, with technology-based tracing and human tracing among the most widely adopted. However, governments and communities worldwide vary in their adoption of digital contact tracing, with many instead choosing the human approach. We investigate how people perceive the respective benefits and risks of human and digital contact tracing through a mixed-methods survey with 291 respondents from the United States. Participants perceived digital contact tracing as more beneficial for protecting privacy, providing convenience, and ensuring data accuracy, and felt that human contact tracing could help provide security, emotional reassurance, advice, and accessibility. We explore the role of self-tracking technologies in public health crisis situations, highlighting how designs must adapt to promote societal benefit rather than just self-understanding. We discuss how future digital contact tracing can better balance the benefits of human tracers and technology amidst the complex contact tracing process and context. 

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5. Toward Accurate Spatiotemporal COVID-19 Risk Scores Using High-Resolution Real-World Mobility Data

   https://doi.org/10.1145/3481044  

   Rambhatla, Sirisha; Zeighami, Sepanta; Shahabi, Kameron; Shahabi, Cyrus; Liu, Yan (June 2022, ACM Transactions on Spatial Algorithms and Systems)

   As countries look toward re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging. While contact tracing only aims to track past activities of infected users, one path to safe reopening is to develop reliable spatiotemporal risk scores to indicate the propensity of the disease. Existing works which aim at developing risk scores either rely on compartmental model-based reproduction numbers (which assume uniform population mixing) or develop coarse-grain spatial scores based on reproduction number (R0) and macro-level density-based mobility statistics. Instead, in this article, we develop a Hawkes process-based technique to assign relatively fine-grain spatial and temporal risk scores by leveraging high-resolution mobility data based on cell-phone originated location signals. While COVID-19 risk scores also depend on a number of factors specific to an individual, including demography and existing medical conditions, the primary mode of disease transmission is via physical proximity and contact. Therefore, we focus on developing risk scores based on location density and mobility behaviour. We demonstrate the efficacy of the developed risk scores via simulation based on real-world mobility data. Our results show that fine-grain spatiotemporal risk scores based on high-resolution mobility data can provide useful insights and facilitate safe re-opening. 

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