Genome characterization of influenza A and B viruses in New South Wales, Australia, in 2019: A retrospective study using high-throughput whole genome sequencing.

BACKGROUND: During the 2019 severe influenza season, New South Wales (NSW) experienced the highest number of cases in Australia. This study retrospectively investigated the genetic characteristics of influenza viruses circulating in NSW in 2019 and identified genetic markers related to antiviral resistance and potential virulence. METHODS: The complete genomes of influenza A and B viruses were amplified using reverse transcription-polymerase chain reaction (PCR) and sequenced with an Illumina MiSeq platform. RESULTS: When comparing the sequencing data with the vaccine strains and reference sequences, the phylogenetic analysis revealed that most NSW A/H3N2 viruses (n = 68; 94%) belonged to 3C.2a1b and a minority (n = 4; 6%) belonged to 3C.3a. These viruses all diverged from the vaccine strain A/Switzerland/8060/2017. All A/H1N1pdm09 viruses (n = 20) showed genetic dissimilarity from vaccine strain A/Michigan/45/2015, with subclades 6B.1A.5 and 6B.1A.2 identified. All B/Victoria-lineage viruses (n = 21) aligned with clade V1A.3, presenting triple amino acid deletions at positions 162-164 in the hemagglutinin protein, significantly diverging from the vaccine strain B/Colorado/06/2017. Multiple amino acid substitutions were also found in the internal proteins of influenza viruses, some of which have been previously reported in hospitalized influenza patients in Thailand. Notably, the oseltamivir-resistant marker H275Y was present in one immunocompromised patient infected with A/H1N1pdm09 and the resistance-related mutation I222V was detected in another A/H3N2-infected patient. CONCLUSIONS: Considering antigenic drift and the constant evolution of circulating A and B strains, we believe continuous monitoring of influenza viruses in NSW via the high-throughput sequencing approach provides timely and pivotal information for both public health surveillance and clinical treatment.

Direct detection of canine picornavirus complete coding sequence in wastewater using long-range reverse-transcriptase polymerase chain reaction and long-read sequencing.

We describe four complete coding sequence (cCDS) of canine picornavirus from wastewater in Arizona, USA detected by coupling cCDS single-contig (∼7.5 kb) reverse-transcriptase polymerase chain reaction (RT-PCR) and low-cost long-read high-throughput sequencing. For viruses of medical/veterinary importance, this workflow expands possibilities of wastewater based genomic epidemiology for exploring virus evolutionary dynamics especially in low-resource settings.

Patient-Related Metadata Reported in Sequencing Studies of SARS-CoV-2: Protocol for a Scoping Review and Bibliometric Analysis.

Background: There has been an unprecedented effort to sequence the SARS-CoV-2 virus and examine its molecular evolution. This has been facilitated by the availability of publicly accessible databases, the Global Initiative on Sharing All Influenza Data (GISAID) and GenBank, which collectively hold millions of SARS-CoV-2 sequence records. Genomic epidemiology, however, seeks to go beyond phylogenetic analysis by linking genetic information to patient characteristics and disease outcomes, enabling a comprehensive understanding of transmission dynamics and disease impact.While these repositories include fields reflecting patient-related metadata for a given sequence, inclusion of these demographic and clinical details is scarce. The extent to which patient-related metadata is reported in published sequencing studies and its quality remains largely unexplored. Methods: The NIH's LitCovid collection will be used for automated classification of articles reporting having deposited SARS-CoV-2 sequences in public repositories, while an independent search will be conducted in PubMed for validation. Data extraction will be conducted using Covidence. The extracted data will be synthesized and summarized to quantify the availability of patient metadata in the published literature of SARS-CoV-2 sequencing studies. For the bibliometric analysis, relevant data points, such as author affiliations and citation metrics will be extracted. Discussion: This scoping review will report on the extent and types of patient-related metadata reported in genomic viral sequencing studies of SARS-CoV-2, identify gaps in this reporting, and make recommendations for improving the quality and consistency of reporting in this area. The bibliometric analysis will uncover trends and patterns in the reporting of patient-related metadata, including differences in reporting based on study types or geographic regions. Co-occurrence networks of author keywords will also be presented. The insights gained from this study may help improve the quality and consistency of reporting patient metadata, enhancing the utility of sequence metadata and facilitating future research on infectious diseases.

Genomic epidemiology reveals the dominance of Hennepin County in the transmission of SARS-CoV-2 in Minnesota from 2020 to 2022.

IMPORTANCE: We analyzed over 22,000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes of patient samples tested at Mayo Clinic Laboratories during a 2-year period in the COVID-19 pandemic, which included Alpha, Delta, and Omicron variants of concern to examine the roles and relationships of Minnesota virus transmission. We found that Hennepin County, the most populous county, drove the transmission of SARS-CoV-2 viruses in the state after including the formation of earlier clades including 20A, 20C, and 20G, as well as variants of concern Alpha and Delta. We also found that Hennepin County was the source for most of the county-to-county introductions after an initial predicted introduction with the virus in early 2020 from an international source, while other counties acted as transmission "sinks." In addition, major policies, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, did not appear to have an impact on virus diversity across individual counties.

Text mining biomedical literature to identify extremely unbalanced data for digital epidemiology and systematic reviews: dataset and methods for a SARS-CoV-2 genomic epidemiology study.

There are many studies that require researchers to extract specific information from the published literature, such as details about sequence records or about a randomized control trial. While manual extraction is cost efficient for small studies, larger studies such as systematic reviews are much more costly and time-consuming. To avoid exhaustive manual searches and extraction, and their related cost and effort, natural language processing (NLP) methods can be tailored for the more subtle extraction and decision tasks that typically only humans have performed. The need for such studies that use the published literature as a data source became even more evident as the COVID-19 pandemic raged through the world and millions of sequenced samples were deposited in public repositories such as GISAID and GenBank, promising large genomic epidemiology studies, but more often than not lacked many important details that prevented large-scale studies. Thus, granular geographic location or the most basic patient-relevant data such as demographic information, or clinical outcomes were not noted in the sequence record. However, some of these data was indeed published, but in the text, tables, or supplementary material of a corresponding published article. We present here methods to identify relevant journal articles that report having produced and made available in GenBank or GISAID, new SARS-CoV-2 sequences, as those that initially produced and made available the sequences are the most likely articles to include the high-level details about the patients from whom the sequences were obtained. Human annotators validated the approach, creating a gold standard set for training and validation of a machine learning classifier. Identifying these articles is a crucial step to enable future automated informatics pipelines that will apply Machine Learning and Natural Language Processing to identify patient characteristics such as co-morbidities, outcomes, age, gender, and race, enriching SARS-CoV-2 sequence databases with actionable information for defining large genomic epidemiology studies. Thus, enriched patient metadata can enable secondary data analysis, at scale, to uncover associations between the viral genome (including variants of concern and their sublineages), transmission risk, and health outcomes. However, for such enrichment to happen, the right papers need to be found and very detailed data needs to be extracted from them. Further, finding the very specific articles needed for inclusion is a task that also facilitates scoping and systematic reviews, greatly reducing the time needed for full-text analysis and extraction.

Foundational domains and competencies for baccalaureate health informatics education.

BACKGROUND: Foundational domains are the building blocks of educational programs. The lack of foundational domains in undergraduate health informatics (HI) education can adversely affect the development of rigorous curricula and may impede the attainment of CAHIIM accreditation of academic programs. OBJECTIVE: This White Paper presents foundational domains developed by AMIA's Academic Forum Baccalaureate Education Committee (BEC) which include corresponding competencies (knowledge, skills, and attitudes) that are intended for curriculum development and CAHIIM accreditation quality assessment for undergraduate education in applied health informatics. METHODS: The AMIA BEC used the previously published master's foundational domains as a guide to creating a set of competencies for health informatics at the undergraduate level to assess graduates from undergraduate health informatics programs for competence at graduation. A consensus method was used to adapt the domains for undergraduate level course work and harmonize the foundational domains with the currently adapted domains for HI master's education. RESULTS: Ten foundational domains were developed to support the development and evaluation of baccalaureate health informatics education. DISCUSSION: This article will inform future work towards building CAHIIM accreditation standards to ensure that higher education institutions meet acceptable levels of quality for undergraduate health informatics education.

Enhanced detection of mpox virus in wastewater using a pre-amplification approach: A pilot study informing population-level monitoring of low-titer pathogens.

A recent outbreak of the mpox virus (MPXV) occurred in non-endemic regions of the world beginning in May 2022. Pathogen surveillance systems faced pressure to quickly establish response protocols, offering an opportunity to employ wastewater-based epidemiology (WBE) for population-level monitoring. The pilot study reported herein aimed to: (i) develop a reliable protocol for MPXV DNA detection in wastewater which would reduce false negative reporting, (ii) test this protocol on wastewater from various regions across the United States, and (iii) conduct a state of the science review of the current literature reporting on experimental methods for MPXV detection using WBE. Twenty-four-hour composite samples of untreated municipal wastewater were collected from the states of New Jersey, Georgia, Illinois, Texas, Arizona, and Washington beginning July 3rd, 2022 through October 16th, 2022 (n = 60). Samples underwent vacuum filtration, DNA extraction from captured solids, MPXV DNA pre-amplification, and qPCR analysis. Of the 60 samples analyzed, a total of eight (13%) tested positive for MPXV in the states of Washington, Texas, New Jersey, and Illinois. The presence of clade IIb MPXV DNA in these samples was confirmed via Sanger sequencing and integration of pre-amplification prior to qPCR decreased the rate of false negative detections by 87% as compared to qPCR analysis alone. Wastewater-derived detections of MPXV were compared to clinical datasets, with 50% of detections occurring as clinical cases were increasing/peaking and 50% occurring as clinical cases waned. Results from the literature review (n = 9 studies) revealed successful strategies for the detection of MPXV DNA in wastewater, however also emphasized a need for further method optimization and standardization. Overall, this work highlights the use of pre-amplification prior to qPCR detection as a means to capture the presence of MPXV DNA in community wastewater and offers guidance for monitoring low-titer pathogens via WBE.

Rhizobium Phage-Like Microvirus Genome Sequence Identified in Wastewater in Arizona, USA, in November 2020 Encodes an Endolysin and a Putative Multiheme Cytochrome c-like Protein.

We describe the genome (4,696 nucleotides [GC content, 56%; coverage, 3,641×) of MAZ-Nov-2020, a microvirus identified from municipal wastewater in Maricopa County, Arizona, USA, in November 2020. The MAZ-Nov-2020 genome encodes major capsid protein, endolysin, replication initiator protein, and two hypothetical proteins, one of which was predicted to likely be a membrane-associated multiheme cytochrome c.

Wastewater surveillance uncovers regional diversity and dynamics of SARS-CoV-2 variants across nine states in the USA.

Wastewater-based epidemiology (WBE) is a non-invasive and cost-effective approach for monitoring the spread of a pathogen within a community. WBE has been adopted as one of the methods to monitor the spread and population dynamics of the SARS-CoV-2 virus, but significant challenges remain in the bioinformatic analysis of WBE-derived data. Here, we have developed a new distance metric, CoVdist, and an associated analysis tool that facilitates the application of ordination analysis to WBE data and the identification of viral population changes based on nucleotide variants. We applied these new approaches to a large-scale dataset from 18 cities in nine states of the USA using wastewater collected from July 2021 to June 2022. We found that the trends in the shift between the Delta and Omicron SARS-CoV-2 lineages were largely consistent with what was seen in clinical data, but that wastewater analysis offered the added benefit of revealing significant differences in viral population dynamics at the state, city, and even neighborhood scales. We also were able to observe the early spread of variants of concern and the presence of recombinant lineages during the transitions between variants, both of which are challenging to analyze based on clinically-derived viral genomes. The methods outlined here will be beneficial for future applications of WBE to monitor SARS-CoV-2, particularly as clinical monitoring becomes less prevalent. Additionally, these approaches are generalizable, allowing them to be applied for the monitoring and analysis of future viral outbreaks.

Leveraging an established neighbourhood-level, open access wastewater monitoring network to address public health priorities: a population-based study.

BACKGROUND: Before the COVID-19 pandemic, the US opioid epidemic triggered a collaborative municipal and academic effort in Tempe, Arizona, which resulted in the world's first open access dashboard featuring neighbourhood-level trends informed by wastewater-based epidemiology (WBE). This study aimed to showcase how wastewater monitoring, once established and accepted by a community, could readily be adapted to respond to newly emerging public health priorities. METHODS: In this population-based study in Greater Tempe, Arizona, an existing opioid monitoring WBE network was modified to track SARS-CoV-2 transmission through the analysis of 11 contiguous wastewater catchments. Flow-weighted and time-weighted 24 h composite samples of untreated wastewater were collected at each sampling location within the wastewater collection system for 3 days each week (Tuesday, Thursday, and Saturday) from April 1, 2020, to March 31, 2021 (Area 7 and Tempe St Luke's Hospital were added in July, 2020). Reverse transcription quantitative PCR targeting the E gene of SARS-CoV-2 isolated from the wastewater samples was used to determine the number of genome copies in each catchment. Newly detected clinical cases of COVID-19 by zip code within the City of Tempe, Arizona were reported daily by the Arizona Department of Health Services from May 23, 2020. Maricopa County-level new positive cases, COVID-19-related hospitalisations, deaths, and long-term care facility deaths per day are publicly available and were collected from the Maricopa County Epidemic Curve Dashboard. Viral loads of SARS-CoV-2 (genome copies per day) measured in wastewater from each catchment were aggregated at the zip code level and city level and compared with the clinically reported data using root mean square error to investigate early warning capability of WBE. FINDINGS: Between April 1, 2020, and March 31, 2021, 1556 wastewater samples were analysed. Most locations showed two waves in viral levels peaking in June, 2020, and December, 2020-January, 2021. An additional wave of viral load was seen in catchments close to Arizona State University (Areas 6 and 7) at the beginning of the fall (autumn) semester in late August, 2020. Additionally, an early infection hotspot was detected in the Town of Guadalupe, Arizona, starting the week of May 4, 2020, that was successfully mitigated through targeted interventions. A shift in early warning potential of WBE was seen, from a leading (mean of 8·5 days [SD 2·1], June, 2020) to a lagging (-2·0 days [1·4], January, 2021) indicator compared with newly reported clinical cases. INTERPRETATION: Lessons learned from leveraging an existing neighbourhood-level WBE reporting dashboard include: (1) community buy-in is key, (2) public data sharing is effective, and (3) sub-ZIP-code (postal code) data can help to pinpoint populations at risk, track intervention success in real time, and reveal the effect of local clinical testing capacity on WBE's early warning capability. This successful demonstration of transitioning WBE efforts from opioids to COVID-19 encourages an expansion of WBE to tackle newly emerging and re-emerging threats (eg, mpox and polio). FUNDING: National Institutes of Health's RADx-rad initiative, National Science Foundation, Virginia G Piper Charitable Trust, J M Kaplan Fund, and The Flinn Foundation.

Genomic epidemiology reveals the dominance of Hennepin County in transmission of SARS-CoV-2 in Minnesota from 2020-2022.

SARS-CoV-2 has had an unprecedented impact on human health and highlights the need for genomic epidemiology studies to increase our understanding of virus evolution and spread, and to inform policy decisions. We sequenced viral genomes from over 22,000 patient samples tested at Mayo Clinic Laboratories between 2020-2022 and use Bayesian phylodynamics to describe county and regional spread in Minnesota. The earliest introduction into Minnesota was to Hennepin County from a domestic source around January 22, 2020; six weeks before the first confirmed case in the state. This led to the virus spreading to Northern Minnesota, and eventually, the rest of the state. International introductions were most abundant in Hennepin (home to the Minneapolis/St. Paul International (MSP) airport) totaling 45 (out of 107) over the two-year period. Southern Minnesota counties were most common for domestic introductions with 19 (out of 64), potentially driven by bordering states such as Iowa and Wisconsin as well as Illinois which is nearby. Hennepin also was, by far, the most dominant source of in-state transmissions to other Minnesota locations (n=772) over the two-year period. We also analyzed the diversity of the location source of SARS-CoV-2 viruses in each county and noted the timing of state-wide policies as well as trends in clinical cases. Neither the number of clinical cases or major policy decisions, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, appeared to have impact on virus diversity across each individual county.

Surveillance of rhinovirus diversity among a university community identifies multiple types from all three species including an unassigned rhinovirus A genotype.

We determine the presence and diversity of rhinoviruses in nasopharyngeal swab samples from 248 individuals who presented with influenza-like illness (ILI) at a university clinic in the Southwest United States between October 1, 2020 and March 31, 2021. We identify at least 13 rhinovirus genotypes (A11, A22, A23, A25, A67, A101, B6, B79, C1, C17, C36, and C56, as well a new genotype [AZ88**]) and 16 variants that contributed to the burden of ILI in the community. We also describe the complete capsid protein gene of a member (AZ88**) of an unassigned rhinovirus A genotype.

Impact of sample clarification by size exclusion on virus detection and diversity in wastewater-based epidemiology.

The use of wastewater-based epidemiology (WBE) for early detection of virus circulation and response during the SARS-CoV-2 pandemic increased interest in and use of virus concentration protocols that are quick, scalable, and efficient. One such protocol involves sample clarification by size fractionation using either low-speed centrifugation to produce a clarified supernatant or membrane filtration to produce an initial filtrate depleted of solids, eukaryotes and bacterial present in wastewater (WW), followed by concentration of virus particles by ultrafiltration of the above. While this approach has been successful in identifying viruses from WW, it assumes that majority of the viruses of interest should be present in the fraction obtained by ultrafiltration of the initial filtrate, with negligible loss of viral particles and viral diversity. We used WW samples collected in a population of ~700,000 in southwest USA between October 2019 and March 2021, targeting three non-enveloped viruses (enteroviruses [EV], canine picornaviruses [CanPV], and human adenovirus 41 [Ad41]), to evaluate whether size fractionation of WW prior to ultrafiltration leads to appreciable differences in the virus presence and diversity determined. We showed that virus presence or absence in WW samples in both portions (filter trapped solids [FTS] and filtrate) are not consistent with each other. We also found that in cases where virus was detected in both fractions, virus diversity (or types) captured either in FTS or filtrate were not consistent with each other. Hence, preferring one fraction of WW over the other can undermine the capacity of WBE to function as an early warning system and negatively impact the accurate representation of virus presence and diversity in a population.

Complete Coding Sequences of Rhinovirus Types A46, A39, C56, and C48.

We report the coding-complete sequences of rhinovirus types C48, A46, A39, and C56, determined from nasopharyngeal swabs from three individuals with influenza-like symptoms in the United States. One sample showed a coinfection of rhinovirus types A46 and C48.

Genome Sequence of a Microvirus Recovered from Wastewater in Arizona, USA, in October 2020, Encodes a Previously Undescribed DNA-Binding Protein.

We describe the genome of Microvirus-AZ-2020, which was identified from wastewater in Arizona, USA, in October 2020. Microvirus-AZ-2020 belongs to subfamily Gokushovirinae and contains six (five known and one hypothetical) open reading frames (ORFs), each with >40 codons. HHPred analysis and Colabfold structure prediction suggest that the hypothetical ORF encodes a previously undescribed putative DNA-binding protein.

Genome Sequences of Anelloviruses, Genomovirus, and Papillomavirus Isolated from Nasal Pharyngeal Swabs.

The genome sequences of three anelloviruses (genus Alphatorquevirus), a genomovirus (genus Gemykolovirus), and an unclassified papillomavirus were identified in four human nasopharyngeal swabs, and one was positive for influenza A and one for influenza B virus. The influenza B virus-positive sample had a coinfection with an anellovirus and a papillomavirus.

Detection of respiratory viruses directly from clinical samples using next-generation sequencing: A literature review of recent advances and potential for routine clinical use.

Acute respiratory infection is the third most frequent cause of mortality worldwide, causing over 4.25 million deaths annually. Although most diagnosed acute respiratory infections are thought to be of viral origin, the aetiology often remains unclear. The advent of next-generation sequencing (NGS) has revolutionised the field of virus discovery and identification, particularly in the detection of unknown respiratory viruses. We systematically reviewed the application of NGS technologies for detecting respiratory viruses from clinical samples and outline potential barriers to the routine clinical introduction of NGS. The five databases searched for studies published in English from 01 January 2010 to 01 February 2021, which led to the inclusion of 52 studies. A total of 14 different models of NGS platforms were summarised from included studies. Among these models, second-generation sequencing platforms (e.g., Illumina sequencers) were used in the majority of studies (41/52, 79%). Moreover, NGS platforms have proven successful in detecting a variety of respiratory viruses, including influenza A/B viruses (9/52, 17%), SARS-CoV-2 (21/52, 40%), parainfluenza virus (3/52, 6%), respiratory syncytial virus (1/52, 2%), human metapneumovirus (2/52, 4%), or a viral panel including other respiratory viruses (16/52, 31%). The review of NGS technologies used in previous studies indicates the advantages of NGS technologies in novel virus detection, virus typing, mutation identification, and infection cluster assessment. Although there remain some technical and ethical challenges associated with NGS use in clinical laboratories, NGS is a promising future tool to improve understanding of respiratory viruses and provide a more accurate diagnosis with simultaneous virus characterisation.

Canine picornaviruses detected in wastewater in Arizona, USA 2019 and 2021.

Virus surveillance by wastewater-based epidemiology (WBE) in two Arizona municipalities in Maricopa County, USA (~700,000 people), revealed the presence of six canine picornavirus (CanPV) variants: five in 2019 and one in 2021. Phylogenetic analysis suggests these viruses might be from domestic dog breeds living within or around the area. Phylogenetic and pairwise identity analyses suggest over 15 years of likely enzootic circulation of multiple lineages of CanPV in the USA and possibly globally. Considering <10 CanPV sequences are publicly available in GenBank as of June 2, 2022, the results provided here constitute an increase of current knowledge on CanPV diversity and highlight the need for increased surveillance.

Detection of human, porcine and canine picornaviruses in municipal sewage sludge using pan-enterovirus amplicon-based long-read Illumina sequencing.

We describe the successful detection of human, porcine and canine picornaviruses (CanPV) in sewage sludge (at each stage of treatment) from Louisville, Kentucky, USA, using Pan-enterovirus amplicon-based long-read Illumina sequencing. Based on publicly available sequence data in GenBank, this is the first detection of CanPV in the USA and the first detection globally using wastewater-based epidemiology. Our findings also suggest there might be clusters of endemic porcine enterovirus (which have been shown capable of causing systemic infection in porcine) circulation in the USA that have not been sampled for around two decades. Our findings highlight the value of WBE coupled with amplicon based long-read Illumina sequencing for virus surveillance and demonstrates this approach can provide an avenue that supports a "One Health" model to virus surveillance. Finally, we describe a new CanPV assay targeting the capsid protein gene region that can be used globally, especially in resource limited settings for its detection and molecular epidemiology.