Viral Sequence Data Research Articles

Optimized quantification of intra-host viral diversity in SARS-CoV-2 and influenza virus sequence data.

High error rates of viral RNA-dependent RNA polymerases lead to diverse intra-host viral populations during infection. Errors made during replication that are not strongly deleterious to the virus can lead to the generation of minority variants. However, accurate detection of minority variants in viral sequence data is complicated by errors introduced during sample preparation and data analysis. We used synthetic RNA controls and simulated data to test seven variant-calling tools across a range of allele frequencies and simulated coverages. We show that choice of variant caller and use of replicate sequencing have the most significant impact on single-nucleotide variant (SNV) discovery and demonstrate how both allele frequency and coverage thresholds impact both false discovery and false-negative rates. When replicates are not available, using a combination of multiple callers with more stringent cutoffs is recommended. We use these parameters to find minority variants in sequencing data from SARS-CoV-2 clinical specimens and provide guidance for studies of intra-host viral diversity using either single replicate data or data from technical replicates. Our study provides a framework for rigorous assessment of technical factors that impact SNV identification in viral samples and establishes heuristics that will inform and improve future studies of intra-host variation, viral diversity, and viral evolution. IMPORTANCE When viruses replicate inside a host cell, the virus replication machinery makes mistakes. Over time, these mistakes create mutations that result in a diverse population of viruses inside the host. Mutations that are neither lethal to the virus nor strongly beneficial can lead to minority variants that are minor members of the virus population. However, preparing samples for sequencing can also introduce errors that resemble minority variants, resulting in the inclusion of false-positive data if not filtered correctly. In this study, we aimed to determine the best methods for identification and quantification of these minority variants by testing the performance of seven commonly used variant-calling tools. We used simulated and synthetic data to test their performance against a true set of variants and then used these studies to inform variant identification in data from SARS-CoV-2 clinical specimens. Together, analyses of our data provide extensive guidance for future studies of viral diversity and evolution.

CoVnita, an end-to-end privacy-preserving framework for SARS-CoV-2 classification

Classification of viral strains is essential in monitoring and managing the COVID-19 pandemic, but patient privacy and data security concerns often limit the extent of the open sharing of full viral genome sequencing data. We propose a framework called CoVnita, that supports private training of a classification model and secure inference with the same model. Using genomic sequences from eight common SARS-CoV-2 strains, we simulated scenarios where the data was distributed across multiple data providers. Our framework produces a private federated model, over 8 parties, with a classification AUROC of 0.99, given a privacy budget of varepsilon =1. The roundtrip time, from encryption to decryption, took a total of 0.298 s, with an amortized time of 74.5 ms per sample.

Using Multiplex Amplicon PCR Technology to Efficiently and Timely Generate Rift Valley Fever Virus Sequence Data for Genomic Surveillance.

Rift Valley fever (RVF) is a febrile vector-borne disease endemic in Africa and continues to spread in new territories. It is a climate-sensitive disease mostly triggered by abnormal rainfall patterns. The disease is associated with high mortality and morbidity in both humans and livestock. RVF is caused by the Rift Valley fever virus (RVFV) of the genus Phlebovirus in the family Phenuiviridae. It is a tripartite RNA virus with three genomic segments: small (S), medium (M) and large (L). Pathogen genomic sequencing is becoming a routine procedure and a powerful tool for understanding the evolutionary dynamics of infectious organisms, including viruses. Inspired by the utility of amplicon-based sequencing demonstrated in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and Ebola, Zika and West Nile viruses, we report an RVFV sample preparation based on amplicon multiplex polymerase chain reaction (amPCR) for template enrichment and reduction of background host contamination. The technology can be implemented rapidly to characterize and genotype RVFV during outbreaks in a near-real-time manner. To achieve this, we designed 74 multiplex primer sets covering the entire RVFV genome to specifically amplify the nucleic acid of RVFV in clinical samples from an animal tissue. Using this approach, we demonstrate achieving complete RVFV genome coverage even from samples containing a relatively low viral load. We report the first primer scheme approach of generating multiplex primer sets for a tripartite virus which can be replicated for other segmented viruses.

Rebels, vigilantes and mavericks: heterodox actors in global health governance

COVID-19 has exposed profound governance challenges that demand more diverse and creative approaches to global health governance moving forward. This article works towards such a pluralization of the field by foregrounding the vital role played by heterodox actors during the pandemic. Heterodox global health actors are backgrounded actors who improve health in different parts of the world, but who remain politically marginalized – and epistemically invisibilized – because they depart in crucial respects from the liberal orthodoxy pervading the field of global health governance. The article analytically foregrounds those heterodox actors through an architectural inversion – a relational approach to the study of global health governance that builds upon recent methodological insights from postcolonial studies, infrastructure studies, and science and technology studies. The article then harnesses that methodological approach to empirically investigate the COVID-19 activities of three different heterodox actors: rebel groups providing public health in the borderlands of Myanmar, a women’s vigilante movement stitching face masks in the Czech Republic, and a maverick scientific platform for the international sharing of viral sequence data. Performing that architectural inversion begins to loosen the dominance of the liberal episteme within the practice and study of global health governance. It further visibilizes how that field is continually co-produced by the background activities of many such heterodox actors. It also lays conceptual foundations for a more heterodox future research agenda on global health governance – and arguably global governance more broadly – in response to the numerous unresolved challenges revealed by COVID-19.

A systematic review of Hepatitis B virus (HBV) prevalence and genotypes in Kenya: Data to inform clinical care and health policy.

The aim of this systematic review and meta-analysis is to evaluate available prevalence and viral sequencing data representing chronic hepatitis B (CHB) infection in Kenya. More than 20% of the global disease burden from CHB is in Africa, however there is minimal high quality seroprevalence data from individual countries and little viral sequencing data available to represent the continent. We undertook a systematic review of the prevalence and genetic data available for hepatitis B virus (HBV) in Kenya using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2020 checklist. We identified 23 studies reporting HBV prevalence and 8 studies that included HBV genetic data published in English between January 2000 and December 2021. We assessed study quality using the Joanna Briggs Institute critical appraisal checklist. Due to study heterogeneity, we divided the studies to represent low, moderate, high and very high-risk for HBV infection, identifying 8, 7, 5 and 3 studies in these groups, respectively. We calculated pooled HBV prevalence within each group and evaluated available sequencing data. Pooled HBV prevalence was 3.4% (95% CI 2.7-4.2%), 6.1% (95% CI 5.1-7.4%), 6.2% (95% CI 4.64-8.2) and 29.2% (95% CI 12.2-55.1), respectively. Study quality was overall low; only three studies detailed sample size calculation and 17/23 studies were cross sectional. Eight studies included genetic information on HBV, with two undertaking whole genome sequencing. Genotype A accounted for 92% of infections. Other genotypes included genotype D (6%), D/E recombinants (1%) or mixed populations (1%). Drug resistance mutations were reported by two studies. There is an urgent need for more high quality seroprevalence and genetic data to represent HBV in Kenya to underpin improved HBV screening, treatment and prevention in order to support progress towards elimination targets.

Low Viral Diversity Limits the Effectiveness of Sequence-Based Transmission Inference for SARS-CoV-2.

Tracking the spread of infection amongst individuals within and between communities has been a major challenge during viral outbreaks. With the unprecedented scale of viral sequence data collection during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the possibility of using phylogenetics to reconstruct past transmission events has been explored as a more rigorous alternative to traditional contact tracing; however, the reliability of sequence-based inference of transmission networks has yet to be directly evaluated. E. E. Bendall, G. Paz-Bailey, G. A. Santiago, C. A. Porucznik, et al. (mSphere 7:e00400-22, 2022, https://doi.org/10.1128/mSphere.00400-22) evaluate the potential of this technique by applying best practices sequence comparison methods to three geographically distinct cohorts that include known transmission pairs and demonstrate that linked pairs are often indistinguishable from unrelated samples. This study clearly establishes how low viral diversity limits the utility of genomic methods of epidemiological inference for SARS-CoV-2.

A Phylogeographic Analysis of Porcine Parvovirus 1 in Africa.

Porcine parvovirus 1 (PPV1) is recognized as a major cause of reproductive failure in pigs, leading to several clinical outcomes globally known as SMEDI. Despite being known since the late 1960s its circulation is still of relevance to swine producers. Additionally, the emergence of variants such as the virulent 27a strain, for which lower protection induced by vaccines has been demonstrated, is of increasing concern. Even though constant monitoring of PPV1 using molecular epidemiological approaches is of pivotal importance, viral sequence data are scarce especially in low-income countries. To fill this gap, a collection of 71 partial VP2 sequences originating from eight African countries (Burkina Faso, Côte d'Ivoire, Kenya, Mozambique, Namibia, Nigeria, Senegal, and Tanzania) during the period 2011-2021 were analyzed within the context of global PPV1 variability. The observed pattern largely reflected what has been observed in high-income regions, i.e., 27a-like strains were more frequently detected than less virulent NADL-8-like strains. A phylogeographic analysis supported this observation, highlighting that the African scenario has been largely shaped by multiple PPV1 importation events from other continents, especially Europe and Asia. The existence of such an international movement coupled with the circulation of potential vaccine-escape variants requires the careful evaluation of the control strategies to prevent new strain introduction and persistence.

Comparative Epidemiology of Rabbit Haemorrhagic Disease Virus Strains from Viral Sequence Data.

Since their introduction in 1859, European rabbits (Oryctolagus cuniculus) have had a devastating impact on agricultural production and biodiversity in Australia, with competition and land degradation by rabbits being one of the key threats to agricultural and biodiversity values in Australia. Biocontrol agents, with the most important being the rabbit haemorrhagic disease virus 1 (RHDV1), constitute the most important landscape-scale control strategies for rabbits in Australia. Monitoring field strain dynamics is complex and labour-intensive. Here, using phylodynamic models to analyse the available RHDV molecular data, we aimed to: investigate the epidemiology of various strains, use molecular data to date the emergence of new variants and evaluate whether different strains are outcompeting one another. We determined that the two main pathogenic lagoviruses variants in Australia (RHDV1 and RHDV2) have had similar dynamics since their release, although over different timeframes (substantially shorter for RHDV2). We also found a strong geographic difference in their activities and evidence of overall competition between the two viruses.

1130. Ensovibep antiviral activity in ambulatory patients with COVID-19 is independent of baseline anti-SARS-CoV-2 antibodies and exhibits minimal selective pressure – Results from the placebo-controlled EMPATHY trial

Abstract Background Ensovibep is a multi-specific DARPin (designed ankyrin repeat protein) antiviral in clinical development for treatment of COVID-19. In the Phase 2 EMPATHY study, ensovibep demonstrated greater viral load decline versus placebo. Here we report (1) the efficacy of ensovibep in patients with and without anti-SARS-CoV-2 antibodies at baseline and (2) SARS-CoV-2 mutation emergence data with treatment. Methods Eligible ambulatory patients with ≥2 COVID-19 symptoms (onset within 7 days) and positive SARS-CoV-2 rapid antigen test on day of dosing, were randomized (1:1:1:1) to ensovibep (600, 225 or 75 mg) or placebo as single, IV infusion. Chemiluminescent immunoassays were used for antibody detection (SARS-CoV-2 S1/S2 IgG and SARS-CoV-2 IgM). A pre-specified subgroup analysis was performed based on baseline anti-SARS-CoV-2 antibody status. Analysis of changes in viral genome from baseline to post baseline was performed to evaluate treatment-emergent mutations. Results Of the patients analyzed, 48.5% had anti-SARS-CoV-2 antibodies at baseline. Baseline log10 SARS-CoV-2 viral load (mean ±SD) was similar across groups [ensovibep (all doses) 6.5 ±1.5, placebo 6.2 ±1.5]; > 90% were infected with the Delta (B.1.617.2) variant. SARS-CoV-2 viral load reduction up to Day 8 showed similar effects in favor of ensovibep compared with placebo regardless of the presence of anti-SARS-CoV-2 antibodies (Figure 1). Patients in ensovibep 75 mg, 600 mg, and placebo groups had comparable incidences of emergent mutations, with a higher incidence in the 225 mg group. Based on analysis of 70% of the expected viral sequencing data, two mutations in the key binding residues of ensovibep were observed (Y489H and F486L) in a total of three patients treated with ensovibep. These patients either cleared virus by Day 8 or mutations were transient (occurred at a single time point but not later in the course of infection). Figure 1Forest plot of estimated treatment differences and associated 95% confidence intervals in time-weighted change from baseline in log10 SARS-CoV-2 viral load through Day 8 by subgroups for the presence of anti-SARS-CoV-2 antibodies (SARS-CoV-2 S1/S2 IgG and/or SARS-CoV-2 IgM) at baseline. Conclusion Ensovibep effectively reduces SARS-CoV-2 viral load regardless of the presence of anti-SARS-CoV-2 antibodies at baseline. Furthermore, there were no emerging mutations of concern, indicating that a single dose administration of ensovibep is associated with minimal selective pressure. Disclosures Marc Bonten, MD, PhD, Astra-Zeneca: Advisor/Consultant|Janssen: Advisor/Consultant|Merck: Advisor/Consultant|Novartis: Advisor/Consultant Richa Chandra, MD, Novartis Pharmaceuticals Corporation: Employee Damodaran Solai Elango, MD, Novartis Healthcare Pvt Ltd: Employee Pierre Fustier, PhD, Molecular Partners AG: Employee Kinfemichael Gedif, PhD, Novartis Pharmaceuticals Corporation: Employee Susana Goncalves, MD, Novartis Pharma AG: Employee Awawu Igbinadolor, MD, Novartis: Awawu Igbinadolor reports financial support from different pharmaceutical companies and organizations Jeff Kingsley, DO, MBA, CPI, FACRP, Centricity Research: Other Charles G. Knutson, PhD, Novartis Institutes for BioMedical Research: Employee Petra Kukkaro, PhD, Novartis Pharma AG: Employee Nagalingeswaran Kumarasamy, MD, Novartis: Nagalingeswaran Kumarasamy reports financial support from different pharmaceutical companies and organizations Philippe Legenne, MD, Molecular Partners AG: Employee Martha Mekebeb-Reuter, MD, Novartis: Martha Mekebeb-Reuter reports financial support from different pharmaceutical companies and organizations Krishnan Ramanathan, MD, Novartis Pharma AG: Employee Evgeniya Reshetnyak, PhD, Novartis Pharmaceuticals Corporation: Employee Michael Robinson, PhD, Novartis Institute for Tropical Disease: Employee Jennifer Rosa, MD, Novartis: Jennifer Rosa reports financial support from different pharmaceutical companies and organizations Marianne Soergel, MD, Molecular Partners AG: Employee Vaia Stavropoulou, PhD, Molecular Partners AG: Employee Nina Stojcheva, PhD, Molecular Partners AG: Employee Michael T. Stumpp, PhD, Molecular Partners AG: Employee Andreas Tietz, MD, Novartis Pharma AG: Employee Xiaojun Zhao, PhD, Novartis Institutes for BioMedical Research: Employee Zhaojie Zhang, PhD, 8. Novartis Institutes for BioMedical Research: Employee.

Impact of molecular sequence data completeness on HIV cluster detection and a network science approach to enhance detection

Detection of viral transmission clusters using molecular epidemiology is critical to the response pillar of the Ending the HIV Epidemic initiative. Here, we studied whether inference with an incomplete dataset would influence the accuracy of the reconstructed molecular transmission network. We analyzed viral sequence data available from ~ 13,000 individuals with diagnosed HIV (2012–2019) from Houston Health Department surveillance data with 53% completeness (n = 6852 individuals with sequences). We extracted random subsamples and compared the resulting reconstructed networks versus the full-size network. Increasing simulated completeness was associated with an increase in the number of detected clusters. We also subsampled based on the network node influence in the transmission of the virus where we measured Expected Force (ExF) for each node in the network. We simulated the removal of nodes with the highest and then lowest ExF from the full dataset and discovered that 4.7% and 60% of priority clusters were detected respectively. These results highlight the non-uniform impact of capturing high influence nodes in identifying transmission clusters. Although increasing sequence reporting completeness is the way to fully detect HIV transmission patterns, reaching high completeness has remained challenging in the real world. Hence, we suggest taking a network science approach to enhance performance of molecular cluster detection, augmented by node influence information.

Analysis of Zika Virus Sequence Data Associated with a School Cohort in Haiti.

Zika virus (ZIKV) infections occurred in epidemic form in the Americas in 2014-2016, with some of the earliest isolates in the region coming from Haiti. We isolated ZIKV from 20 children with acute undifferentiated febrile illness who were part of a cohort of children seen at a school clinic in the Gressier region of Haiti. The virus was also isolated from three pools of Aedes aegypti mosquitoes collected at the same location. On phylogenetic analysis, three distinct ZIKV clades were identified. Strains from all three clades were present in Haiti in 2014, making them among the earliest isolates identified in the Western Hemisphere. Strains from all three clades were also isolated in 2016, indicative of their persistence across the time period of the epidemic. Mosquito isolates were collected in 2016 and included representatives from two of the three clades; in one instance, ZIKV was isolated from a pool of male mosquitoes, suggestive of vertical transmission of the virus. The identification of multiple ZIKV clades in Haiti at the beginning of the epidemic suggests that Haiti served as a nidus for transmission within the Caribbean.

Haplotype assignment of longitudinal viral deep sequencing data using covariation of variant frequencies.

Longitudinal deep sequencing of viruses can provide detailed information about intra-host evolutionary dynamics including how viruses interact with and transmit between hosts. Many analyses require haplotype reconstruction, identifying which variants are co-located on the same genomic element. Most current methods to perform this reconstruction are based on a high density of variants and cannot perform this reconstruction for slowly evolving viruses. We present a new approach, HaROLD (HAplotype Reconstruction Of Longitudinal Deep sequencing data), which performs this reconstruction based on identifying co-varying variant frequencies using a probabilistic framework. We illustrate HaROLD on both RNA and DNA viruses with synthetic Illumina paired read data created from mixed human cytomegalovirus (HCMV) and norovirus genomes, and clinical datasets of HCMV and norovirus samples, demonstrating high accuracy, especially when longitudinal samples are available.

Application of Statistical Learning to Identify Omicron Mutations in SARS-CoV-2 Viral Genome Sequence Data From Populations in Africa and the United States

With timely collection of SARS-CoV-2 viral genome sequences, it is important to apply efficient data analytics to detect emerging variants at the earliest time. To evaluate the application of a statistical learning strategy (SLS) to improve early detection of novel SARS-CoV-2 variants using viral sequence data from global surveillance. This case series applied an SLS to viral genomic sequence data collected from 63 686 individuals in Africa and 531 827 individuals in the United States with SARS-CoV-2. Data were collected from January 1, 2020, to December 28, 2021. The outcome was an indicator of Omicron variant derived from viral sequences. Centering on a temporally collected outcome, the SLS used the generalized additive model to estimate locally averaged Omicron caseload percentages (OCPs) over time to characterize Omicron expansion and to estimate when OCP exceeded 10%, 25%, 50%, and 75% of the caseload. Additionally, an unsupervised learning technique was applied to visualize Omicron expansions, and temporal and spatial distributions of Omicron cases were investigated. In total, there were 2698 cases of Omicron in Africa and 12 141 in the United States. The SLS found that Omicron was detectable in South Africa as early as December 31, 2020. With 10% OCP as a threshold, it may have been possible to declare Omicron a variant of concern as early as November 4, 2021, in South Africa. In the United States, the application of SLS suggested that the first case was detectable on November 21, 2021. The application of SLS demonstrates how the Omicron variant may have emerged and expanded in Africa and the United States. Earlier detection could help the global effort in disease prevention and control. To optimize early detection, efficient data analytics, such as SLS, could assist in the rapid identification of new variants as soon as they emerge, with or without lineages designated, using viral sequence data from global surveillance.

TreeKnit: Inferring ancestral reassortment graphs of influenza viruses.

When two influenza viruses co-infect the same cell, they can exchange genome segments in a process known as reassortment. Reassortment is an important source of genetic diversity and is known to have been involved in the emergence of most pandemic influenza strains. However, because of the difficulty in identifying reassortment events from viral sequence data, little is known about their role in the evolution of the seasonal influenza viruses. Here we introduce TreeKnit, a method that infers ancestral reassortment graphs (ARG) from two segment trees. It is based on topological differences between trees, and proceeds in a greedy fashion by finding regions that are compatible in the two trees. Using simulated genealogies with reassortments, we show that TreeKnit performs well in a wide range of settings and that it is as accurate as a more principled bayesian method, while being orders of magnitude faster. Finally, we show that it is possible to use the inferred ARG to better resolve segment trees and to construct more informative visualizations of reassortments.

Comparative Dynamics of Delta and Omicron SARS-CoV-2 Variants across and between California and Mexico.

Evolutionary analysis using viral sequence data can elucidate the epidemiology of transmission. Using publicly available SARS-CoV-2 sequence and epidemiological data, we developed discrete phylogeographic models to interrogate the emergence and dispersal of the Delta and Omicron variants in 2021 between and across California and Mexico. External introductions of Delta and Omicron in the region peaked in early July (2021-07-10 [95% CI: 2021-04-20, 2021-11-01]) and mid-December (2021-12-15 [95% CI: 2021-11-14, 2022-01-09]), respectively, 3 months and 2 weeks after first detection. These repeated introductions coincided with domestic migration events with no evidence of a unique transmission hub. The spread of Omicron was most consistent with gravity centric patterns within Mexico. While cross-border events accounted for only 5.1% [95% CI: 4.3–6] of all Delta migration events, they accounted for 20.6% [95% CI: 12.4–29] of Omicron movements, paralleling the increase in international travel observed in late 2021. Our investigations of the Delta and Omicron epidemics in the California/Mexico region illustrate the complex interplay and the multiplicity of viral and structural factors that need to be considered to limit viral spread, even as vaccination is reducing disease burden. Understanding viral transmission patterns may help intra-governmental responses to viral epidemics.

Benchmarking and Assessment of Eight De Novo Genome Assemblers on Viral Next-Generation Sequencing Data, Including the SARS-CoV-2.

Viral genomics has become crucial in clinical diagnostics and ecology, not to mention to stem the COVID-19 pandemic. Whole-genome sequencing (WGS) is pivotal in gaining an improved understanding of viral evolution, genomic epidemiology, infectious outbreaks, pathobiology, clinical management, and vaccine development. Genome assembly is one of the crucial steps in WGS data analyses. A series of different assemblers has been developed with the advent of high-throughput next-generation sequencing (NGS). Various studies have reported the evaluation of these assembly tools on distinct datasets; however, these lack data from viral origin. In this study, we performed a comparative evaluation and benchmarking of eight de novo assemblers: SOAPdenovo, Velvet, assembly by short sequences (ABySS), iterative De Bruijn graph assembler (IDBA), SPAdes, Edena, iterative virus assembler, and VICUNA on the viral NGS data from distinct Illumina (GAIIx, Hiseq, Miseq, and Nextseq) platforms. WGS data of diverse viruses, that is, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), dengue virus 3, human immunodeficiency virus 1, hepatitis B virus, human herpesvirus 8, human papillomavirus 16, rhinovirus A, and West Nile virus, were utilized to assess these assemblers. Performance metrics such as genome fraction recovery, assembly lengths, NG50, N50, contig length, contig numbers, mismatches, and misassemblies were analyzed. Overall, three assemblers, that is, SPAdes, IDBA, and ABySS, performed consistently well, including for genome assembly of SARS-CoV-2. These assembly methods should be considered and recommended for future studies of viruses. The study also suggests that implementing two or more assembly approaches should be considered in viral NGS studies, especially in clinical settings. Taken together, the benchmarking of eight de novo genome assemblers reported in this study can inform future public health and ecology research concerning the viruses, the COVID-19 pandemic, and viral outbreaks.

Making genomic surveillance deliver: A lineage classification and nomenclature system to inform rabies elimination.

The availability of pathogen sequence data and use of genomic surveillance is rapidly increasing. Genomic tools and classification systems need updating to reflect this. Here, rabies virus is used as an example to showcase the potential value of updated genomic tools to enhance surveillance to better understand epidemiological dynamics and improve disease control. Previous studies have described the evolutionary history of rabies virus, however the resulting taxonomy lacks the definition necessary to identify incursions, lineage turnover and transmission routes at high resolution. Here we propose a lineage classification system based on the dynamic nomenclature used for SARS-CoV-2, defining a lineage by phylogenetic methods for tracking virus spread and comparing sequences across geographic areas. We demonstrate this system through application to the globally distributed Cosmopolitan clade of rabies virus, defining 96 total lineages within the clade, beyond the 22 previously reported. We further show how integration of this tool with a new rabies virus sequence data resource (RABV-GLUE) enables rapid application, for example, highlighting lineage dynamics relevant to control and elimination programmes, such as identifying importations and their sources, as well as areas of persistence and routes of virus movement, including transboundary incursions. This system and the tools developed should be useful for coordinating and targeting control programmes and monitoring progress as countries work towards eliminating dog-mediated rabies, as well as having potential for broader application to the surveillance of other viruses.

An Evaluation of Phylogenetic Workflows in Viral Molecular Epidemiology.

The use of viral sequence data to inform public health intervention has become increasingly common in the realm of epidemiology. Such methods typically utilize multiple sequence alignments and phylogenies estimated from the sequence data. Like all estimation techniques, they are error prone, yet the impacts of such imperfections on downstream epidemiological inferences are poorly understood. To address this, we executed multiple commonly used viral phylogenetic analysis workflows on simulated viral sequence data, modeling Human Immunodeficiency Virus (HIV), Hepatitis C Virus (HCV), and Ebolavirus, and we computed multiple methods of accuracy, motivated by transmission-clustering techniques. For multiple sequence alignment, MAFFT consistently outperformed MUSCLE and Clustal Omega, in both accuracy and runtime. For phylogenetic inference, FastTree 2, IQ-TREE, RAxML-NG, and PhyML had similar topological accuracies, but branch lengths and pairwise distances were consistently most accurate in phylogenies inferred by RAxML-NG. However, FastTree 2 was the fastest, by orders of magnitude, and when the other tools were used to optimize branch lengths along a fixed FastTree 2 topology, the resulting phylogenies had accuracies that were indistinguishable from their original counterparts, but with a fraction of the runtime.

The need for linked genomic surveillance of SARS-CoV-2.

Genomic surveillance during the coronavirus disease 2019 (COVID-19) pandemic has been key to the timely identification of virus variants with important public health consequences, such as variants that can transmit among and cause severe disease in both vaccinated or recovered individuals. The rapid emergence of the Omicron variant highlighted the speed with which the extent of a threat must be assessed. Rapid sequencing and public health institutions' openness to sharing sequence data internationally give an unprecedented opportunity to do this; however, assessing the epidemiological and clinical properties of any new variant remains challenging. Here we highlight a "band of four" key data sources that can help to detect viral variants that threaten COVID-19 management: 1) genetic (virus sequence) data; 2) epidemiological and geographic data; 3) clinical and demographic data; and 4) immunization data. We emphasize the benefits that can be achieved by linking data from these sources and by combining data from these sources with virus sequence data. The considerable challenges of making genomic data available and linked with virus and patient attributes must be balanced against major consequences of not doing so, especially if new variants of concern emerge and spread without timely detection and action.

Addressing Privacy Concerns in Sharing Viral Sequences and Minimum Contextual Data in a Public Repository During the COVID-19 Pandemic.

COVID-19 was declared to be a pandemic in March 2020 by the World Health Organization. Timely sharing of viral genomic sequencing data accompanied by a minimal set of contextual data is essential for informing regional, national, and international public health responses. Such contextual data is also necessary for developing, and improving clinical therapies and vaccines, and enhancing the scientific community’s understanding of the SARS-CoV-2 virus. The Canadian COVID-19 Genomics Network (CanCOGeN) was launched in April 2020 to coordinate and upscale existing genomics-based COVID-19 research and surveillance efforts. CanCOGeN is performing large-scale sequencing of both the genomes of SARS-CoV-2 virus samples (VirusSeq) and affected Canadians (HostSeq). This paper addresses the privacy concerns associated with sharing the viral sequence data with a pre-defined set of contextual data describing the sample source and case attribute of the sequence data in the Canadian context. Currently, the viral genome sequences are shared by provincial public health laboratories and their healthcare and academic partners, with the Canadian National Microbiology Laboratory and with publicly accessible databases. However, data sharing delays and the provision of incomplete contextual data often occur because publicly releasing such data triggers privacy and data governance concerns. The CanCOGeN Ethics and Governance Expert Working Group thus has investigated several privacy issues cited by CanCOGeN data providers/stewards. This paper addresses these privacy concerns and offers insights primarily in the Canadian context, although similar privacy considerations also exist in other jurisdictions. We maintain that sharing viral sequencing data and its limited associated contextual data in the public domain generally does not pose insurmountable privacy challenges. However, privacy risks associated with reidentification should be actively monitored due to advancements in reidentification methods and the evolving pandemic landscape. We also argue that during a global health emergency such as COVID-19, privacy should not be used as a blanket measure to prevent such genomic data sharing due to the significant benefits it provides towards public health responses and ongoing research activities.