Untapped potential: Opportunities and challenges for self-led contact tracing during outbreaks of communicable diseases

Abstract

Outbreaks of communicable diseases such as COVID-19 can significantly threaten individual wellbeing and societal functioning. To contain such outbreaks, public health services (PHS) implement contact tracing (CT). In this process, a public health professional (PHP) interviews an infected individual (‘case’) to identify people they were in contact with during their infectious period (‘contacts’) and informs them about the necessary measures to prevent further transmission of the pathogen. However, CT can be challenging during large outbreaks due to the high numbers of cases and contacts, time and resource demands, and limited cooperation. In this thesis, we investigated whether and how CT can be improved by more actively involving cases and contacts in tasks typically performed by PHPs, supported by digital tools. We refer to this approach as ‘self-led’ CT. In part one of this thesis, we examined the use of web-based respondent-driven sampling (webRDS) in public health. A scoping review showed that webRDS has been successfully used to recruit participants for research on various topics, deliver health interventions, and support case finding. Peer recruitment can be stimulated by offering adequate incentives, conducting formative research into recruitment barriers, and thoroughly motivating ‘seeds’ to initiate recruitment. In part two, we explored PHPs’ perspectives on self-led CT through interviews and surveys. PHPs were generally positive, anticipating that self-led CT could make the process more efficient and enable more autonomous participation. However, they also expressed concerns about losing oversight and the ability to support cases and contacts. PHPs considered self-led CT appropriate when dealing with digitally skilled and motivated individuals, and during outbreaks involving relatively many cases and contacts. They considered self-led CT less suitable in high-risk or complex situations. PHPs emphasized that self-led CT should complement—not replace—provider-initiated CT and recommended maintaining options for personal support. In part three, we investigated citizens’ perspectives on self-led CT during the COVID-19 pandemic using interviews and surveys. Citizens generally indicated to be willing to participate in self-led CT. Their willingness depended on various factors, including previous experiences with CT, sense of responsibility, self-efficacy, perceived impact, trust in digital tools, and privacy concerns. Citizens recommended enabling early participation, minimizing data collection, and offering both autonomous and supported options within the CT process. In part four, we conducted an experimental online questionnaire study to compare different approaches to support citizens in recalling and reporting their contacts. We found that using context-specific recall cues and asking participants to list contacts separately for different situations increased the number of reported contacts, but also raised the risk of participant dropout. Furthermore, not all additionally elicited contacts may equally contribute to CT effectiveness. Our findings suggest that self-led CT may be less feasible for individuals with many or high-risk contacts. In the final part of the thesis, we discuss limitations, opportunities for future research, and propose a framework for integrating self-led and provider-initiated CT to maximize benefits while addressing potential challenges.