Cov2clusters: genomic clustering of SARS-CoV-2 sequences (preprint)

Background: The COVID-19 pandemic remains a global public health concern. Advances in rapid sequencing has led to an unprecedented level of SARS-CoV-2 whole genome sequence (WGS) data and sharing of sequences through global repositories that has enabled almost real-time genomic analysis of the pathogen. WGS data has been used previously to group genetically similar viral pathogens to reveal evidence of transmission, including methods that identify distinct clusters on a phylogenetic tree. Identifying clusters of linked cases can aid in the regional surveillance and management of the disease. In this study, we present a novel method for producing stable genomic clusters of SARS-CoV-2 cases, cov2clusters, and compare the sensitivity and stability of our approach to previous methods used for phylogenetic clustering using real-world SARS-CoV-2 sequence data obtained from British Columbia, Canada, Results: We found that cov2clusters produced more stable clusters than previously used phylogenetic clustering methods when adding sequence data through time, mimicking an increase in sequence data through the pandemic. Our method also showed high sensitivity when compared to epidemiologically informed clusters. These clusters often contained a high number of cases that were identical or near identical genetically. Conclusions: This new approach presented here allows for the identification of stable clusters of SARS-CoV-2 from WGS data. Producing high-resolution SARS-CoV-2 clusters from sequence data alone can a challenge and, where possible, both genomic and epidemiological data should be used in combination.

Genomic epidemiology offers high resolution estimates of serial intervals for COVID-19 (preprint)

Estimating key aspects of transmission is crucial in infectious disease control. Serial intervals - the time between symptom onset in an infector and infectee - are fundamental, and help to define rates of transmission, estimates of reproductive numbers, and vaccination levels needed to prevent transmission. However, estimating the serial interval requires knowledge of individuals' contacts and exposures (who infected whom), which is typically obtained through resource-intensive contact tracing efforts. We develop an alternate framework that uses virus sequences to inform who infected whom and thereby estimate serial intervals. The advantages are many-fold: virus sequences are often routinely collected to support epidemiological investigations and to monitor viral evolution. The genomic approach offers high resolution and cluster-specific estimates of the serial interval that are comparable with those obtained from contact tracing data. Our approach does not require contact tracing data, and can be used in large populations and over a range of time periods. We apply our techniques to SARS-CoV-2 sequence data from the first two waves of COVID-19 in Victoria, Australia. We find that serial interval estimates vary between clusters, supporting the need to monitor this key parameter and use updated estimates in onward applications. Compared to an early published serial interval estimate, using cluster-specific serial intervals can cause estimates of the effective reproduction number Rt to vary by a factor of up to 2-3. We also find that serial intervals estimated in settings such as schools and meat processing/packing plants tend to be shorter than those estimated in healthcare facilities.

Genomics-Informed Responses in the Elimination of COVID-19 in Australia (preprint)

BACKGROUND: A cornerstone of Australia’s ability to control COVID-19 has been effective border control, using an extensive supervised quarantine program. However, a rapid recrudescence in COVID-19 cases was observed in the state of Victoria in June 2020. Here, we describe the genomic findings that located the source of this second wave as a breach in supervised hotel quarantine and demonstrate the successful elimination of COVID-19 for a second time in Australia.METHODS: Genome sequencing was performed on all available SARS-CoV-2-positive samples in Victoria and integrated genomic and epidemiological investigation undertaken.RESULTS: At 31st January 2021, 20,451 COVID-19 cases were reported in Victoria; samples were sequenced from 75% of cases (15,431/20,451). Genomics revealed 98% (10,426/10,646) of locally-acquired cases during the second wave were derived from a single incursion from hotel quarantine, with the outbreak strain rapidly detected in other Australian states and territories. Phylodynamic analyses indicated an epidemic growth rate comparable to emerging variants, such as B.1.1.7 in the United Kingdom. Strict public health interventions resulted in the elimination of the outbreak strain by 29th October 2020. Subsequent cases represented independent international or interstate introductions, with limited local spread.CONCLUSIONS: Rapid escalation of clonal outbreaks can occur from even a single breach of control practices, as revealed through our genomic ‘enhanced outbreak-detection' system. The subsequent elimination and rapid control of new SARS-CoV-2 incursions reinforce that decisive public health responses to emergent cases are effective even with high epidemic growth rates, and “elimination” should be favored in settings where this is achievable.FUNDING STATEMENT: The Microbiological Diagnostic Unit Public Health Laboratory (MDU PHL) and the Victorian Infectious Diseases Reference Laboratory (VIDRL) at The Doherty Institute are funded by the Victorian Government. This work was supported by the National Health and Medical Research Council, Australia (NHMRC); Partnership Grant (APP1149991), Investigator Grant to BPH (APP1196103), Investigator Grant to DAW (APP1174555), Research Fellowship to TPS (APP1105525), MRFF COVID-19 Genomics Grant (MRF9200006).DECLARATION OF INTERESTS: None to declare. ETHICS APPROVAL STATEMENT: Data were collected in accordance with the Victorian Public Health and Wellbeing Act 2008. Ethical approval was received from the University of Melbourne Human Research Ethics Committee (study number 1954615.3).

The Pathogen Genomics in Public HeAlth Surveillance Evaluation (PG-PHASE) Framework: An Implementation Science Approach to Evaluating Pathogen Whole Genome Sequencing in Public Health (preprint)

Background Pathogen whole genome sequencing (WGS) is being incorporated into public health surveillance and disease control systems worldwide and has the potential to make significant contributions to infectious disease surveillance, outbreak investigation and infection prevention and control. However, to date, there are limited data regarding: (i) the optimal models for integration of genomic data into epidemiological investigations, and (ii) how to quantify and evaluate public health impacts resulting from genomic epidemiological investigations. Methods We developed the Pathogen Genomics in Public HeAlth Surveillance Evaluation (PG-PHASE) Framework to guide examination of the use of WGS in public health surveillance and disease control. We illustrate the use of this framework with three pathogens as case studies: Listeria monocytogenes, Mycobacterium tuberculosis and SARS-CoV-2. Results The framework utilises an adaptable whole-of-system approach towards understanding how interconnected elements in the public health application of pathogen genomics contribute to public health processes and outcomes. The three phases of the PG-PHASE Framework are designed to support understanding of WGS laboratory processes, analysis, reporting and data sharing, and how genomic data are utilised in public health practice across all stages, from the decision to send an isolate or sample for sequencing to the use of sequence data in public health surveillance, investigation and decision-making. Importantly, the phases can be used separately or in conjunction, depending on the need of the evaluator. Subsequent to conducting evaluation underpinned by the framework, avenues may be developed for strategic investment or interventions to improve utilisation of whole genome sequencing. Conclusions Comprehensive evaluation is critical to support health departments, public health laboratories and other stakeholders to successfully incorporate microbial genomics into public health practice. The PG-PHASE Framework aims to assist public health laboratories, health departments and authorities who are either considering transitioning to whole genome sequencing or intending to assess the integration of WGS in public health practice, including the capacity to detect and respond to outbreaks and associated costs, challenges and facilitators in the utilisation of microbial genomics and public health impacts. 

The PHA4GE SARS-CoV-2 Contextual Data Specification for Open Genomic Epidemiology (preprint)

The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatic tools and resources, and advocate for greater openness, interoperability, accessibility and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a clear and present need for a fit-for-purpose, open source SARS-CoV-2 contextual data standard. As such, we have developed an extension to the INSDC pathogen package, providing a SARS-CoV-2 contextual data specification based on harmonisable, publicly available, community standards. The specification is implementable via a collection template, as well as an array of protocols and tools to support the harmonisation and submission of sequence data and contextual information to public repositories. Well-structured, rich contextual data adds value, promotes reuse, and enables aggregation and integration of disparate data sets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19.

Tracking the COVID-19 pandemic in Australia using genomics (preprint)

BACKGROUND: Whole-genome sequencing of pathogens can improve resolution of outbreak clusters and define possible transmission networks. We applied high-throughput genome sequencing of SARS-CoV-2 to 75% of cases in the State of Victoria (population 6.24 million) in Australia. METHODS: Cases of SARS-CoV-2 infection were detected through active case finding and contact tracing. A dedicated SARS-CoV-2 multidisciplinary genomic response team was formed to enable rapid integration of epidemiological and genomic data. Phylodynamic analysis was performed to assess the putative impact of social restrictions. RESULTS: Between 25 January and 14 April 2020, 1,333 COVID-19 cases were reported in Victoria, with a peak in late March. After applying internal quality control parameters, 903 samples were included in genomic analyses. Sequenced samples from Australia were representative of the global diversity of SARS-CoV-2, consistent with epidemiological findings of multiple importations and limited onward transmission. In total, 76 distinct genomic clusters were identified; these included large clusters associated with social venues, healthcare facilities and cruise ships. Sequencing of sequential samples from 98 patients revealed minimal intra-patient SARS-CoV-2 genomic diversity. Phylodynamic modelling indicated a significant reduction in the effective viral reproductive number (Re) from 1.63 to 0.48 after the implementation of travel restrictions and population-level physical distancing. CONCLUSIONS: Our data provide a comprehensive framework for the use of SARS-CoV-2 genomics in public health responses. The application of genomics to rapidly identify SARS-CoV-2 transmission chains will become critically important as social restrictions ease globally. Public health responses to emergent cases must be swift, highly focused and effective.