An open-source digital contact tracing system tailored to haulage

Muwonge, Adrian; Muwonge, Adrian; Wee, Bryan A.; Mugerwa, Ibrahimm; Nabunya, Emma; Mpyangu, Christine M.; Bronsvoort, Barend M. de C.; Ssebaggala, Emmanuel Robert; Kiayias, Aggelos; Mwaka, Erisa; Joloba, Moses · 2023 · DOAJ

DOI: 10.3389/fdgth.2023.1199635

archive: archived pipeline: cataloged verified

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Summary

This paper introduces and evaluates THEA-GS, an open-source digital contact tracing (DCT) system specifically designed for long-distance haulage drivers. The research addresses the limitations of manual contact tracing (MCT), which is labor-intensive and often too slow to outpace rapid disease outbreaks like COVID-19. Given that haulage drivers maintain supply chains during lockdowns and travel across borders, they represent a high-risk group for disease transmission. The study aims to demonstrate the functionality of THEA-GS in a low- and middle-income country (LMIC) context, using Uganda as a case study, while also examining challenges related to user adoption and trust. THEA-GS consists of two main components: an Android mobile application and a backend server suite. The mobile app records timestamped GPS data exclusively when drivers are on the road, using geo-fencing within ±100 meters of the road network. This data is transmitted via encrypted connections to the server, which integrates with national result dispatch systems to receive diagnostic test results. The system employs a place-based contact tracing approach rather than peer-to-peer proximity detection. It uses a clustering algorithm to identify potential transmission hotspots based on duration of stops, occupancy, and distance, cross-referencing these clusters with "ground truth information" such as fuel stations and border checkpoints. The server also provides a web-based interface for public health officials to monitor risk assessments and system performance metrics, such as positive predictive value and specificity. The system was piloted in Uganda during the Omicron variant wave between October 2021 and October 2022. A total of 3,270 haulage drivers were enrolled at four border ports of entry, with approximately 75% utilizing the tool for about two months. The analysis incorporated 3,800 test results, including 48 positive cases, 125 identified contacts, and 40 million timestamped GPS points. Results indicated that THEA-GS was approximately 90 times faster than manual contact tracing. The average time from sample collection to notifying a confirmed case was 70 minutes, and notifying their contacts took 80 minutes. The system successfully mapped road network coverage and identified risk hotspots. However, the study noted that adoption faced challenges primarily due to drivers' lack of awareness regarding the tool's purpose and benefits for public health. The authors conclude that THEA-GS is a robust, functional tool capable of significantly accelerating contact tracing for the haulage industry, thereby reducing strain on supply chains and public health resources. While the technical performance was strong, the study highlights that social acceptance and sustained adoption are heavily dependent on establishing trust among users. The tool is presented as a scalable, open-source solution that can be integrated into national digital health infrastructure to manage infectious disease outbreaks involving high-risk mobile populations.

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• exposure measurement