Coverage of state-initiated contact-tracing during COVID-19 and factors influencing it: evidence from real-world data

Contact tracing has been one of the central non-pharmaceutical interventions implemented worldwide to try to control the spread of Sars-CoV-2, but its effectiveness strongly depends on its ability to detect contacts. To investigate this issue, we analysed an extensive operational database of SARS-CoV-2 tests in Geneva and used permutations statistics to estimate the number of secondary infectious contacts occurring at the same address. Results show that manual contact tracing captured on average 41% of the secondary infections occurring at the address, with variation in time from 23% during epidemic peaks to 60% during low epidemic activity. The under-reporting of contacts is influenced by both socio-economic and structural factors. People living in wealthy neighbourhoods are less likely to report contacts (adjusted odds ratio (aOR): 1.6) People living in buildings are also less likely to report contacts, with an aOR of 1.08 to 3.14 depending on the variant of concern, the size of the building and if the building had shops. This under-reporting of contacts in buildings decreased during periods of mandatory mask wearing and restriction of private gathering, highlighting the importance of public measures in reducing unnoticed infections in shared spaces. More effective contact tracing strategy should be partly digitalized to avoid saturation of contact tracing capacity during high activity of the pandemics. Public message and outreach should communicate on avoiding unnoticed infectious contacts in large building and may benefit from targeting specific population, such as those in wealthy areas. Evidence before this study We searched MEDLINE via Pubmed and the WHO-COVID-19 research database from the database inception until February 27, 2023 for relevant studies on the effect of contact tracing to curb COVID-19 transmission or on contact tracing coverage, with no language restriction, using the following terms: (COVID-19) AND (Contact tracing) AND (Efficacy or effectiveness or digital or transmission or coverage). Similarly to what has been reported by a recent systematic review evaluating the effect of contact tracing in controlling the spread of infectious disease, we mostly found mathematical modelling studies, and 13 observational studies. Observational studies have contrasted results about contact tracing effectiveness. Only 2 of them report global contact tracing coverage, estimated as the percentage of cases that were identified as contacts, which are below 11%. No study identify the factors affecting the contact tracing coverage. Modeling studies identified the adherence to quarantine, the notification delay and the contact tracing coverage (for manual contact tracing) or the app adherence (for mobile app based contact tracing) as the key factors for contact tracing efficiency. Added value of this study An operational COVID-19 infections database register and the use of permutation tests allowed a precise quantification of the coverage of the manual contact tracing system of the state of Geneva, Switzerland, and the factor affecting it. On average, 41% of the infected persons residing at the same address were reported. The rich information available in the register was used to identify the factors associated with under-reporting, which were wealthy neighborhoods, large buildings, and non-vaccination of the index-contact pair. Implications of all the available evidence Implementing more efficient contact tracing for future covid-19 resurgence or other pandemics is crucial. A multi-modal approach, consisting of manual and digital contact tracing and prevention of unnoticed infection, with a particular focus on populations with high contact under-reporting, could help reduce the transmission of infectious disease. However, at least for similar highly contagious aerosol-transmitted diseases, contact tracing will not be sufficient, and systemic policies such as masking, air filtration or gathering restrictions may be necessary. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was supported by the research project SELFISH, financed by the Swiss National Science Foundation, grant number 51NF40-160590 (LIVES Center international research project call) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Research received the agreement of the Cantonal Ethic Committee of Geneva (CCER protocol 2020-01273) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The de-identified database underlying this article will be shared on reasonable request using the form (). The code used for the analysis is available at the following repository: [https://gitlab.com/dmongin/scientific\_articles/-/tree/main/contact\_tracing][1]. [1]: https://gitlab.com/dmongin/scientific_articles/-/tree/main/contact_tracing