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BackgroundZoonotic viruses are infectious organisms transmittable between animals and humans. Agencies of public health, agriculture, and wildlife conduct surveillance of zoonotic viruses and often report data on their websites. However, the format and characteristics of these data are not known. ObjectiveTo describe and compare the format and characteristics of statistics of zoonotic viruses on state public health, agriculture, and wildlife agency websites. MethodsFor each state, we considered the websites of that state’s public health, agriculture, and wildlife agency. For each website, we noted the presence of any statistics for zoonotic viruses from 2000-2012. We analyzed the data using numerous categories including type of statistic, temporal and geographic level of detail, and format. We prioritized our analysis within each category based on assumptions of individuals’ preferences for extracting and analyzing data from websites. Thus, if two types of data (such as city and state-level) were present for a given virus in a given year, we counted the one with higher priority (city). External links from agency sites to other websites were not considered. ResultsFrom 2000-2012, state health departments had the most extensive virus data, followed by agriculture, and then wildlife. We focused on the seven viruses that were common across the three agencies. These included rabies, West Nile virus, eastern equine encephalitis, St. Louis encephalitis, western equine encephalitis, influenza, and dengue fever. Simple numerical totals were most often used to report the data (89% for public health, 81% for agriculture, and 82% for wildlife), and proportions were not different (chi-square P=.15). Public health data were most often presented yearly (66%), while agriculture and wildlife agencies often described cases as they occurred (Fisher’s Exact test P

IntroductionWeb 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. MethodsIn 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. ResultsThe 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. DiscussionNatural 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.

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.

Ancestral state reconstructions 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.

H6N1 influenza A is an avian virus but in 2013 infected a human in Taiwan. We studied the phylogeography of avian origin H6N1 viruses in the Influenza Research Database and the Global Initiative on Sharing Avian Influenza Data EpiFlu Database in order to characterize their recent evolutionary spread. Our results suggest that the H6N1 virus that infected a human in Taiwan is derived from a diversity of avian strains of H6N1 that have circulated for at least seven years in this region. Understanding how geography impacts the evolution of avian influenza could allow disease control efforts to focus on areas that pose the greatest risk to humans. The serious human infection with a known avian influenza virus underscores the zoonotic potential of diverse avian strains of influenza, and the need for comprehensive influenza surveillance in animals and the value of public sequence databases including GISAID and the IRD.

Emerging and re-emerging infectious diseases of zoonotic origin like highly pathogenic avian influenza pose a significant threat to human and animal health due to their elevated transmissibility. Identifying the drivers of such viruses is challenging, and estimation of spatial diffusion is complicated by the fact that the variability of viral spread from locations could be caused by a complex array of unknown factors. Several techniques exist to help identify these drivers, including bioinformatics, phylogeography, and spatial epidemiology, but these methods are generally evaluated separately and do not consider the complementary nature of each other. Here, we studied an approach that integrates these techniques and identifies the most important drivers of viral spread by focusing on H5N1 influenza A virus in Egypt because of its recent emergence as an epicenter for the disease. We used a Bayesian phylogeographic generalized linear model (GLM) to reconstruct spatiotemporal patterns of viral diffusion while simultaneously assessing the impact of factors contributing to transmission. We also calculated the cross-species transmission rates among hosts in order to identify the species driving transmission. The densities of both human and avian species were supported contributors, along with latitude, longitude, elevation, and several meteorological variables. Also supported was the presence of a genetic motif found near the hemagglutinin cleavage site. Various genetic, geographic, demographic, and environmental predictors each play a role in H1N1 diffusion. Further development and expansion of phylogeographic GLMs such as this will enable health agencies to identify variables that can curb virus diffusion and reduce morbidity and mortality.

Bioinformatics and phylogeography models use viral sequence data to analyze spread of epidemics and pandemics. However, few of these models have included analytical methods for testing whether certain predictors such as population density, rates of disease migration, and climate are drivers of spatial spread. Understanding the specific factors that drive spatial diffusion of viruses is critical for targeting public health interventions and curbing spread. In this paper we describe the application and evaluation of a model that integrates demographic and environmental predictors with molecular sequence data. The approach parameterizes evolutionary spread of RNA viruses as a generalized linear model (GLM) within a Bayesian inference framework using Markov chain Monte Carlo (MCMC). We evaluate this approach by reconstructing the spread of H5N1 in Egypt while assessing the impact of individual predictors on evolutionary diffusion of the virus.

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]

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. [ABSTRACT FROM AUTHOR]