Online respondent-driven detection for enhanced contact tracing of COVID-19 Free

Contact tracing (CT) is a core intervention in the global COVID-19 response, but it is a laborious task for the public health professionals (PHPs) who execute CT. In many countries, the workload associated with CT of COVID-19 exceeded available resources for CT. Online respondent-driven detection (RDD) is an innovative method for case finding that can enhance CT efficiency, through active involvement of index cases and contacts in the execution of CT. This can accelerate CT and decrease PHPs' workload. However, it is unclear if and why (not) PHPs want to apply RDD for CT of COVID-19 and what they need to this purpose.

This mixed methods study is undertaken in the Netherlands and Germany. We developed an interview guide based on the ‘reasoned action approach' to elicit factors that influence PHPs' intention to apply RDD for CT of COVID-19 and their needs. We conducted 17 semi-structured interviews with Dutch PHPs - and will conduct 6-10 interviews with German PHPs involved in CT of COVID-19. Based on the interviews, an online questionnaire is developed to validate the qualitative findings in a larger population of PHPs.

Dutch PHPs believe that RDD facilitates autonomous participation of index cases and contacts and, conversely, less control of PHPs in CT. PHPs anticipated that this could impact the quality and efficiency of CT, and the delivery of - and compliance with - control measures. Several factors, e.g. the available CT-capacity and the anticipated skills and willingness of index cases and contacts to participate in RDD, influenced PHPs' intention to apply RDD for CT of COVID-19. PHPs expressed a need to retain opportunities to support and guide index cases and contacts in CT and to maintain oversight over the CT-process.

RDD can support PHPs and enhance CT during the ongoing COVID-19 pandemic and future large scale outbreaks of infectious diseases. In order to be properly applied, PHPs' needs should be addressed.