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Variola virus is at risk of re-emergence either through accidental release, bioterrorism, or synthetic biology. The use of phylogenetics and phylogeography to support epidemic field response is expected to grow as sequencing technology becomes miniaturized, cheap, and ubiquitous. In this study, we aimed to explore the use of common VARV diagnostic targets hemagglutinin (HA), cytokine response modifier B (CrmB), and A-type inclusion protein (ATI) for phylogenetic characterization as well as the representativeness of modelling strategies in phylogeography to support epidemic response should smallpox re-emerge. We used Bayesian discrete-trait phylogeography using the most complete data set currently available of whole genome (n = 51) and partially sequenced (n = 20) VARV isolates. We show that multilocus models combining HA, ATI, and CrmB genes may represent a useful heuristic to differentiate between VARV Major and subclades of VARV Minor which have been associated with variable case-fatality rates. Where whole genome sequencing is unavailable, phylogeography models of HA, ATI, and CrmB may provide preliminary but uncertain estimates of transmission, while supplementing whole genome models with additional isolates sequenced only for HA can improve sample representativeness, maintaining similar support for transmission relative to whole genome models. We have also provided empirical evidence delineating historic international VARV transmission using phylogeography. Due to the persistent threat of re-emergence, our results provide important research for smallpox epidemic preparedness in the posteradication era as recommended by the World Health Organisation. [ABSTRACT FROM AUTHOR]

GeoBoost is a command-line software package developed to address sparse or incomplete metadata in GenBank sequence records that relate to the location of the infected host (LOIH) of viruses. Given a set of GenBank accession numbers corresponding to virus GenBank records, GeoBoost extracts, integrates and normalizes geographic information reflecting the LOIH of the viruses using integrated information from GenBank metadata and related full-text publications. In addition, to facilitate probabilistic geospatial modeling, GeoBoost assigns probability scores for each possible LOIH. [ABSTRACT FROM AUTHOR]

Background: Decision support systems for differential diagnosis have traditionally been evaluated on the basis of criteria how sensitively and specifically they are able to identify the correct diagnosis established by expert clinicians. Discussion: This article questions whether evaluation criteria pertaining to identifying the correct diagnosis are most appropriate or useful. Instead it advocates evaluation of decision support systems for differential diagnosis based on the criterion of maximizing value of information. Summary: This approach quantitatively and systematically integrates several important clinical management priorities, including avoiding serious diagnostic errors of omission and avoiding harmful or expensive tests. [ABSTRACT FROM AUTHOR]

Contraceptive use among women Veterans may not be adequately captured using administrative and pharmacy codes. Clinical progress notes may provide a useful alternative. The objectives of this study were to validate the use of administrative and pharmacy codes to identify contraceptive use in Veterans Health Administration data, and to determine the feasibility and validity of identifying contraceptive use in clinical progress notes. The study included women Veterans who participated in the Women Veterans Cohort Study, enrolled in the Veterans Affairs Connecticut Health Care System, completed a baseline survey, and had clinical progress notes from one year prior to survey completion. Contraceptive ICD-9-CM codes, V-codes, CPT codes, and pharmacy codes were identified. Progress notes were annotated to identify contraceptive use. Self-reported contraceptive use was identified from a baseline survey of health habits and healthcare practices and utilization. Sensitivity, specificity, and positive predictive value were calculated comparing administrative and pharmacy contraceptive codes and progress note-based contraceptive information to self-report survey data. Results showed that administrative and pharmacy codes were specific but not sensitive for identifying contraceptive use. For example, oral contraceptive pill codes were highly specific (1.00) but not sensitive (0.41). Data from clinical progress notes demonstrated greater sensitivity and comparable specificity. For example, for oral contraceptive pills, progress notes were both specific (0.85) and sensitive (0.73). Results suggest that the best approach for identifying contraceptive use, through either administrative codes or progress notes, depends on the research question. [ABSTRACT FROM AUTHOR]

'Mashup' was originally used to describe the mixing together of musical tracks to create a new piece of music. The term now refers to Web sites or services that weave data from different sources into a new data source or service. Using a musical metaphor that builds on the origin of the word 'mashup', this paper presents a demonstration "playlist" of four geo-mashup vignettes that make use of a range of Web 2.0, Semantic Web, and 3-D Internet methods, with outputs/end-user interfaces spanning the flat Web (two-dimensional - 2-D maps), a three-dimensional - 3-D mirror world (Google Earth) and a 3-D virtual world (Second Life ®). The four geo-mashup "songs" in this "playlist" are: 'Web 2.0 and GIS (Geographic Information Systems) for infectious disease surveillance', 'Web 2.0 and GIS for molecular epidemiology', 'Semantic Web for GIS mashup', and 'From Yahoo! Pipes to 3-D, avatar-inhabited geo-mashups'. It is hoped that this showcase of examples and ideas, and the pointers we are providing to the many online tools that are freely available today for creating, sharing and reusing geo-mashups with minimal or no coding, will ultimately spark the imagination of many public health practitioners and stimulate them to start exploring the use of these methods and tools in their day-to-day practice. The paper also discusses how today's Web is rapidly evolving into a much more intensely immersive, mixed-reality and ubiquitous socio-experiential Metaverse that is heavily interconnected through various kinds of user-created mashups. [ABSTRACT FROM AUTHOR]

Correction to: Int J Health Geogr (2018) 17:38 https://doi.org/10.1186/s12942-018-0157-5 Following publication of the original article [[1]], the authors realised that the reference "Iannetti 2014" mentioned in Table 3 was not in the list of References, where ref. #66 is wrongly pointing to a different article from "Iannetti 2011". Reference 1 Beard R, Wentz E, Scotch M. A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks. [Extracted from the article]

The SARS-CoV-2 pandemic resulted in a scale-up of viral genomic surveillance globally. However, the wet lab constraints (economic, infrastructural, and personnel) of translating novel virus variant sequence information to meaningful immunological and structural insights that are valuable for the development of broadly acting countermeasures (especially for emerging and re-emerging viruses) remain a challenge in many resource-limited settings. Here, we describe a workflow that couples wastewater surveillance, high-throughput sequencing, phylogenetics, immuno-informatics, and virus capsid structure modeling for the genotype-to-serotype characterization of uncultivated picornavirus sequences identified in wastewater. Specifically, we analyzed canine picornaviruses (CanPVs), which are uncultivated and yet-to-be-assigned members of the family Picornaviridae that cause systemic infections in canines. We analyzed 118 archived (stored at −20 °C) wastewater (WW) samples representing a population of ~700,000 persons in southwest USA between October 2019 to March 2020 and October 2020 to March 2021. Samples were pooled into 12 two-liter volumes by month, partitioned (into filter-trapped solids [FTSs] and filtrates) using 450 nm membrane filters, and subsequently concentrated to 2 mL (1000×) using 10,000 Da MW cutoff centrifugal filters. The 24 concentrates were subjected to RNA extraction, CanPV complete capsid single-contig RT-PCR, Illumina sequencing, phylogenetics, immuno-informatics, and structure prediction. We detected CanPVs in 58.3% (14/24) of the samples generated 13,824,046 trimmed Illumina reads and 27 CanPV contigs. Phylogenetic and pairwise identity analyses showed eight CanPV genotypes (intragenotype divergence <14%) belonging to four clusters, with intracluster divergence of <20%. Similarity analysis, immuno-informatics, and virus protomer and capsid structure prediction suggested that the four clusters were likely distinct serological types, with predicted cluster-distinguishing B-cell epitopes clustered in the northern and southern rims of the canyon surrounding the 5-fold axis of symmetry. Our approach allows forgenotype-to-serotype characterization of uncultivated picornavirus sequences by coupling phylogenetics, immuno-informatics, and virus capsid structure prediction. This consequently bypasses a major wet lab-associated bottleneck, thereby allowing resource-limited settings to leapfrog from wastewater-sourced genomic data to valuable immunological insights necessary for the development of prophylaxis and other mitigation measures. [ABSTRACT FROM AUTHOR]

• A clinically matched dataset of rhinovirus (RV) isolates from children in Sydney, Australia • There were significantly more RV-C isolates from paediatric cases aged < 2 years • There were phylogenetic-trait associations between young age and VP4/VP2 capsid protein • There are possible age-specific variations in infectivity among subtypes Rhinoviruses (RV) represent the most common aetiological agent of all acute respiratory tract infections across all age groups and a significant burden of disease among children. Recent studies have shown that RV-A and RV-C species are associated with increased disease severity. In order to better understand the potential associations between RV species and clinical features among paediatric cases, this study aimed to integrate genetic and epidemiological data using Bayesian phylogenetic methods. Potential associations between RV species and subtypes, and clinical disease severity using a matched dataset of 52 RV isolates sampled from children (< 18 years) in Sydney, Australia, between 2006 and 2009 were uncovered using epidemiological and phylogenetic methods. It was found that RV-C was significantly more likely to be isolated from paediatric cases aged < 2 years compared with RV-A, although no significant differences in recorded symptoms were observed. Significant phylogenetic-trait associations between age and the VP4/VP2 capsid protein phylogeny suggest that age-specific variations in infectivity among subtypes may may be possible. This study adds to the growing body of epidemiological evidence concerning RV. Improving surveillance and testing for RV, including routine whole genome sequencing, may improve understanding of the varied disease outcomes of RV species and subtypes. Future studies could aim to identify specific genetic markers associated with age-specific infectivity of RV, which could inform treatment practices and public health surveillance of RV. [ABSTRACT FROM AUTHOR]

Discrete phylogeography using software such as BEAST considers the sampling location of each taxon as fixed; often to a single location without uncertainty. When studying viruses, this implies that there is no possibility that the location of the infected host for that taxa is somewhere else. Here, we relaxed this strong assumption and allowed for analytic integration of uncertainty for discrete virus phylogeography. We used automatic language processing methods to find and assign uncertainty to alternative potential locations. We considered two influenza case studies: H5N1 in Egypt; H1N1 pdm09 in North America. For each, we implemented scenarios in which 25 per cent of the taxa had different amounts of sampling uncertainty including 10, 30, and 50 per cent uncertainty and varied how it was distributed for each taxon. This includes scenarios that: (i) placed a specific amount of uncertainty on one location while uniformly distributing the remaining amount across all other candidate locations (correspondingly labeled 10 , 30 , and 50); (ii) assigned the remaining uncertainty to just one other location; thus 'splitting' the uncertainty among two locations (i.e. 10/90, 30/70 , and 50/50); and (iii) eliminated uncertainty via two predefined heuristic approaches: assignment to a centroid location (CNTR) or the largest population in the country (POP). We compared all scenarios to a reference standard (RS) in which all taxa had known (absolutely certain) locations. From this, we implemented five random selections of 25 per cent of the taxa and used these for specifying uncertainty. We performed posterior analyses for each scenario, including: (a) virus persistence, (b) migration rates, (c) trunk rewards, and (d) the posterior probability of the root state. The scenarios with sampling uncertainty were closer to the RS than CNTR and POP. For H5N1, the absolute error of virus persistence had a median range of 0.005–0.047 for scenarios with sampling uncertainty—(i) and (ii) above—versus a range of 0.063–0.075 for CNTR and POP. Persistence for the pdm09 case study followed a similar trend as did our analyses of migration rates across scenarios (i) and (ii). When considering the posterior probability of the root state, we found all but one of the H5N1 scenarios with sampling uncertainty had agreement with the RS on the origin of the outbreak whereas both CNTR and POP disagreed. Our results suggest that assigning geospatial uncertainty to taxa benefits estimation of virus phylogeography as compared to ad-hoc heuristics. We also found that, in general, there was limited difference in results regardless of how the sampling uncertainty was assigned; uniform distribution or split between two locations did not greatly impact posterior results. This framework is available in BEAST v.1.10. In future work, we will explore viruses beyond influenza. We will also develop a web interface for researchers to use our language processing methods to find and assign uncertainty to alternative potential locations for virus phylogeography. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Canine parvoviruses (CPVs) are a major cause of morbidity and mortality in dogs. However, surveillance has been largely limited to clinically manifest cases, resulting in a dearth of CPV genomic information on virus type, abundance, and diversity, limiting our understanding of its evolutionary dynamics. We tested the feasibility of using dog feces in poop bags collected from outdoor waste bins as a source for environmental surveillance of CPV. After polymerase chain reaction, long-read sequencing, and bioinformatics, we identified that CPV-2c was present in Arizona, USA, in June 2022 and documented variants with amino acid substitutions 530E and 101K in NS1 and NS2, respectively. Based on publicly available sequence data in GenBank as of January 2023, the CPV genome described here represents the only CPV genome described in the USA from the 2022 season, despite news of CPV outbreak-associated fatalities in dogs in the USA. This highlights the need for more studies that document CPV complete or near complete genomes, as well as experimental studies, to further our understanding of its evolutionary process. [ABSTRACT FROM AUTHOR]

Ancestral state reconstruction in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS) framework. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM) of genetic, geographic, demographic, and environmental predictors of interest to the virus and incorporating BSSVS to estimate the posterior inclusion probabilities of each predictor. Although the latter appears to enhance the biological validity of ancestral state reconstruction, there has yet to be a comparison of phylogenies created by the two methods. In this paper, we compare these two methods, while also using a primitive method without BSSVS, and highlight the differences in phylogenies created by each. We test six coalescent priors and six random sequence samples of H3N2 influenza during the 2014–15 flu season in the U.S. We show that the GLMs yield significantly greater root state posterior probabilities than the two alternative methods under five of the six priors, and significantly greater Kullback-Leibler divergence values than the two alternative methods under all priors. Furthermore, the GLMs strongly implicate temperature and precipitation as driving forces of this flu season and nearly unanimously identified a single root state, which exhibits the most tropical climate during a typical flu season in the U.S. The GLM, however, appears to be highly susceptible to sampling bias compared with the other methods, which casts doubt on whether its reconstructions should be favored over those created by alternate methods. We report that a BSSVS approach with a Poisson prior demonstrates less bias toward sample size under certain conditions than the GLMs or primitive models, and believe that the connection between reconstruction method and sampling bias warrants further investigation. [ABSTRACT FROM AUTHOR]

Introduction: Web logs ("blogs") have become a popular mechanism for people to express their daily thoughts, feelings, and emotions. Many of these expressions contain health care-related themes, both physical and mental, similar to information discussed during a clinical interview or medical consultation. Thus, some of the information contained in blogs might be important for health care research, especially in mental health where stress-related conditions may be difficult and expensive to diagnose and where early recognition is often key to successful treatment. In the field of biomedical informatics, techniques such as information retrieval (IR) and natural language processing (NLP) are often used to unlock information contained in free-text notes. These methods might assist the clinical research community to better understand feelings and emotions post deployment and the burden of symptoms of stress among US military service members. Methods: In total, 90 military blog posts describing deployment situations and 60 control posts of Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) were collected. After "stop" word exclusion and stemming, a "bag-of-words" representation and term weighting was performed, and the most relevant words were manually selected out of the high-weight words. A pilot ontology was created using Collaborative Protégé, a knowledge management application. The word lists and the ontology were then used within General Architecture for Text Engineering (GATE), an NLP framework, to create an automated pipeline for recognition and analysis of blogs related to combat exposure. An independent expert opinion was used to create a reference standard and evaluate the results of the GATE pipeline. Results: The 2 dimensions of combat exposure descriptors identified were: words dealing with physical exposure and the soldiers' emotional reactions to it. GATE pipeline was able to retrieve blog texts describing combat exposure with precision 0.9, recall 0.75, and F-score 0.82. Discussion: Natural language processing and automated information retrieval might potentially provide valuable tools for retrieving and analyzing military blog posts and uncovering military service members' emotions and experiences of combat exposure. [ABSTRACT FROM AUTHOR]