Multimodal surveillance of SARS-CoV-2 at a university enables development of a robust outbreak response framework

Universities are particularly vulnerable to infectious disease outbreaks and are also ideal environments to study transmission dynamics and evaluate mitigation and surveillance measures when outbreaks occur. Here, we introduce a SARS-CoV-2 surveillance and response framework based on high-resolution, multimodal data collected during the 2020-2021 academic year at Colorado Mesa University. We analyzed epidemiological and sociobehavioral data (demographics, contact tracing, and wifi-based co-location data) alongside pathogen surveillance data (wastewater, random, and reflexive diagnostic testing; and viral genomic sequencing of wastewater and clinical specimens) to characterize outbreak dynamics and inform policy decisions. We quantified group attributes that increased disease risk, and highlighted parallels between traditional and wifi-based contact tracing. We additionally used clinical and environmental viral sequencing to identify cryptic transmission, cluster overdispersion, and novel lineages or mutations. Ultimately, we used distinct data types to identify information that may help shape institutional policy and to develop a model of pathogen surveillance suitable for the future of outbreak preparedness.

DNA spike-ins enable confident interpretation of SARS-CoV-2 genomic data from amplicon-based sequencing

The rapid global spread and continued evolution of SARS-CoV-2 has highlighted an unprecedented need for viral genomic surveillance and clinical viral sequencing. Amplicon-based sequencing methods provide a sensitive, low-cost and rapid approach but suffer a high potential for contamination, which can undermine lab processes and results. This challenge will only increase with expanding global production of sequences by diverse research groups for epidemiological and clinical interpretation. We present an approach which uses synthetic DNA spike-ins (SDSIs) to track samples and detect inter-sample contamination through a sequencing workflow. Applying this approach to the ARTIC Consortiums amplicon design, we define a series of best practices for Illumina-based sequencing and provide a detailed characterization of approaches to increase sensitivity for low-viral load samples incorporating the SDSIs. We demonstrate the utility and efficiency of the SDSI method amidst a real-time investigation of a suspected hospital cluster of SARS-CoV-2 cases.