Characterization of SARS-CoV-2 infection clusters in an urban setting based on integrated real-time genomic surveillance, outbreak analysis, and contact tracing

Alexander Dilthey (Heinrich Heine University Düsseldorf, Germany) described how his team aims to track SARS-CoV-2 transmission chains in the population, providing information that is useful for public health. He explained that this data is important in designing effective intervention and containment strategies, on a local and global scale, as well as for research into the virus itself. Alex told how, due to the many modes of transmission of SARS-CoV-2 and multiple infection sources through community transmission, classical contract tracing, and epidemiological methods are ‘typically not sufficient to capture these transmission chains at high resolution.’ In fact, the source of infection was unknown in 40% of cases in the city of Düsseldorf.

Alex and his team tackled this issue using an integrated genomic surveillance approach, in which analysis of the mutations present in SARS-CoV-2 samples can help infer routes of transmission. They decided to apply the principal of genomic epidemiology at a whole-city scale across Düsseldorf, treating all ongoing transmission chains as one large outbreak. Describing the system’s key features, Alex explained how genomic surveillance was integrated with public health and contact tracing data, and noted that, though the workflow did provide information on variants of concern, this was not its main aim. Finally, they used a ‘sequencing first’ approach to identify clusters. The system has been in development since August 2020, and was fully rolled out in April 2021, with funding from the local state government. Their goal is to sequence all positive cases with a rapid turnaround time.

Alex briefly outlined the workflow of the genomic surveillance program. Each morning, samples that have tested positive for COVID-19 at commercial diagnostic labs are delivered to Alex and his team. These are then sequenced and analysed, mostly using an ARTIC-based approach. The results are displayed in a publicly accessible dashboard application (https://covgen.hhu.de), which also lists putative clusters. This cluster analysis information is then used by Düsseldorf Health Department and contact tracing teams, to aid their tracing efforts. The rapid turnaround times utilise optimised library preparation protocols and scalable IT set-up running analysis via the ARTIC pipeline, guppy, and Medaka; viral consensus genome sequences are then fed into a central compute cluster to identify putative infection clusters. As the samples are nanopore sequenced, data is available in real time: ‘samples will start appearing on this dashboard here as soon as they are loaded on to the MinION’.

Alex then discussed how he and his team investigated the use of genetic data to identify putative clusters. They applied a simple cluster identification algorithm to the data generated from August to December 2020 to look for groups of a minimum size containing genetically identical SARS-CoV-2 samples. Displaying a phylogenetic tree for the data, he showed the putative clusters identified by the algorithm. Integration of post-hoc contact tracing and case information showed that – ‘in each and every case’ – the clusters pointed them to individual transmission chains, for example, in a care home or through a school trip. This collection of information enabled further, in-depth investigation of the transmission chains.

Alex showed one example in which, thorough characterisation of previously unrecognised, complex transmission chains – comprising a home care service, different households, friendship groups, workplaces, and a supermarket – was enabled through the combination of genomic epidemiology and contact tracing data. He stressed how this example illustrated the importance of the data in informing containment and intervention strategies. For example, if home care services were identified as contributing to the spread of COVID-19, measures such as vaccination of employees could be required; if further analysis indicated supermarkets as a transmission source, this could impact their current classification in Germany as low-risk environments.

Alex concluded that ‘it is possible to track SARS-CoV-2 transmission chains through the population, even during periods of ongoing community transmission and very high incidences’ – and that combining sequencing data with contact tracing data is essential for this. More information on their findings using genomic surveillance is available in their preprint. Alex ended his presentation with an open question: as they have demonstrated that the cost of sequencing increases by only 30-40% when aiming to sequence all positive cases, should genomic surveillance through sequencing be implemented much more widely? ‘And I think the answer to that is probably a tentative ‘yes’’.