Format
Sort by
Items per page

Send to

Choose Destination

Best matches for Scotch+M:

Evolution of 2014/15 H3N2 Influenza Viruses Circulating in US: Consequences for Vaccine Effectiveness and Possible New Pandemic. Veljkovic V et al. Front Microbiol. (2015)

Analysis of Viral Genetics for Estimating Diffusion of Influenza A H6N1. Scotch M et al. AMIA Jt Summits Transl Sci Proc. (2015)

Analyses of Merging Clinical and Viral Genetic Data for Influenza Surveillance. Magee D et al. AMIA Annu Symp Proc. (2015)

Search results

Items: 1 to 20 of 55

1.
Mol Biol Evol. 2018 Aug 7. doi: 10.1093/molbev/msy153. [Epub ahead of print]

Bayesian phylogeography and pathogenic characterisation of smallpox based on HA, ATI and CrmB genes.

Author information

1
Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
2
Biodesign Center for Environmental Health Engineering, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
3
Department of Biomedical Informatics, College of Health Solutions, Arizona State University, Tempe, AZ, USA.
4
College of Public Service & Community Solutions, Arizona State University, Tempe, AZ, USA.

Abstract

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 miniaturised, 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 characterisation 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 dataset currently available of whole genome (n = 51) and partially sequenced (n = 20) VARV isolates. We show that multi-locus 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 (CFR). 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 post-eradication era as recommended by the World Health Organisation (WHO).

2.
Infect Genet Evol. 2018 Jul 4;64:225-230. doi: 10.1016/j.meegid.2018.07.003. [Epub ahead of print]

The effects of random taxa sampling schemes in Bayesian virus phylogeography.

Author information

1
Department of Biomedical Informatics, Arizona State University, 13212 E. Shea Blvd., Scottsdale 85259, AZ, USA; Biodesign Center for Environmental Health Engineering, Arizona State University, 1001 S. McAllister Ave, Tempe 85281, AZ, USA.
2
Department of Biomedical Informatics, Arizona State University, 13212 E. Shea Blvd., Scottsdale 85259, AZ, USA; Biodesign Center for Environmental Health Engineering, Arizona State University, 1001 S. McAllister Ave, Tempe 85281, AZ, USA. Electronic address: matthew.scotch@asu.edu.

Abstract

Public health researchers are often tasked with accurately and quickly identifying the location and time when an epidemic originated from a representative sample of nucleotide sequences. In this paper, we investigate multiple approaches to subsampling the sequence set when employing a Bayesian phylogeographic generalized linear model. Our results indicate that near-categorical posterior MCC estimates on the root can be obtained with replicate runs using 25-50% of the sequence data, and that including 90% of sequences does not necessarily entail more accurate inferences. We present the first analysis of predictor signal suppression and show how the ability to detect the influence of predictor variables is limited when sample size predictors are included in the models.

KEYWORDS:

Phylogeography; Selection Bias; Viruses

PMID:
29991455
DOI:
10.1016/j.meegid.2018.07.003
Free full text
Icon for Elsevier Science
3.
Environ Int. 2018 Jul 3;119:241-249. doi: 10.1016/j.envint.2018.06.018. [Epub ahead of print]

Avian influenza virus ecology and evolution through a climatic lens.

Author information

1
Department of Global Health, University of Washington, Seattle, WA, United States. Electronic address: cmorin@email.arizona.edu.
2
Department of Emergency Medicine, University of Washington, Seattle, WA, United States.
3
Department of Biology & Wildlife and University of Alaska Museum, Fairbanks, AK, United States.
4
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, United States; Center for Environmental Health Engineering, Biodesign Institute, Arizona State University, Tempe, AZ, United States.
5
Department of Global Health, University of Washington, Seattle, WA, United States; Department of Emergency Medicine, University of Washington, Seattle, WA, United States; Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, United States.
6
Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, United States.
7
Department of Global Health, University of Washington, Seattle, WA, United States; Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, United States.
8
Department of Global Health, University of Washington, Seattle, WA, United States; Center for Environmental Health Engineering, Biodesign Institute, Arizona State University, Tempe, AZ, United States.

Abstract

Avian influenza virus (AIV) is a major health threat to both avian and human populations. The ecology of the virus is driven by numerous factors, including climate and avian migration patterns, yet relatively little is known about these drivers. Long-distance transport of the virus is tied to inter- and intra-continental bird migration, while enhanced viral reassortment is linked to breeding habitats in Beringia shared by migrant species from North America and Asia. Furthermore, water temperature, pH, salinity, and co-existing biota all impact the viability and persistence of the virus in the environment. Changes in climate can potentially alter the ecology of AIV through multiple pathways. Warming temperatures can change the timing and patterns of bird migration, creating novel assemblages of species and new opportunities for viral transport and reassortment. Water temperature and chemistry may also be altered, resulting in changes in virus survival. In this review, we explain how these shifts have the potential to increase viral persistence, pathogenicity, and transmissibility and amplify the threat of pandemic disease in animal and human hosts. Better understanding of climatic influences on viral ecology is essential to developing strategies to limit adverse health effects in humans and animals.

Publication type

Publication type

4.
Bioinformatics. 2018 Jul 1;34(13):i565-i573. doi: 10.1093/bioinformatics/bty273.

Deep neural networks and distant supervision for geographic location mention extraction.

Author information

1
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.
2
Biodesign Center for Environmental Health Engineering, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
3
Department of Biostatistics, Epidemiology, and Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Abstract

Motivation:

Virus phylogeographers rely on DNA sequences of viruses and the locations of the infected hosts found in public sequence databases like GenBank for modeling virus spread. However, the locations in GenBank records are often only at the country or state level, and may require phylogeographers to scan the journal articles associated with the records to identify more localized geographic areas. To automate this process, we present a named entity recognizer (NER) for detecting locations in biomedical literature. We built the NER using a deep feedforward neural network to determine whether a given token is a toponym or not. To overcome the limited human annotated data available for training, we use distant supervision techniques to generate additional samples to train our NER.

Results:

Our NER achieves an F1-score of 0.910 and significantly outperforms the previous state-of-the-art system. Using the additional data generated through distant supervision further boosts the performance of the NER achieving an F1-score of 0.927. The NER presented in this research improves over previous systems significantly. Our experiments also demonstrate the NER's capability to embed external features to further boost the system's performance. We believe that the same methodology can be applied for recognizing similar biomedical entities in scientific literature.

5.
Sci Total Environ. 2018 Jun 23;643:460-467. doi: 10.1016/j.scitotenv.2018.06.206. [Epub ahead of print]

U.S. nationwide reconnaissance of ten infrequently monitored antibiotics in municipal biosolids.

Author information

1
Arizona State University, Biodesign Center for Environmental Health Engineering, Tempe, AZ, USA; Arizona State University, Department of Biomedical Informatics, College of Health Solutions, Tempe, AZ, USA.
2
Arizona State University, Biodesign Center for Environmental Health Engineering, Tempe, AZ, USA.
3
Arizona State University, School of Sustainable Engineering and the Built Environment, Tempe, AZ, USA; Stony Brook University, Center for Clean Water Technology, Department of Civil Engineering, Stony Brook, NY, USA.
4
Arizona State University, Biodesign Center for Environmental Health Engineering, Tempe, AZ, USA; Arizona State University, School of Sustainable Engineering and the Built Environment, Tempe, AZ, USA. Electronic address: halden@asu.edu.

Abstract

Ten infrequently monitored antibiotics in biosolids were examined in archived American sewage sludges (n = 79) collected as part of the 2006/2007 U.S. Environmental Protection Agency (EPA) Targeted National Sewage Sludge Survey. This study inspected the occurrence of amoxicillin, ampicillin, erythromycin, furazolidone [proxy metabolite: 3-(2-nitrobenzylidenamino)-2-oxazolidinone (NP-AOZ)], nalidixic acid, oxolinic acid, oxytetracycline, spiramycin, sulfadimidine, and sulfadimethoxine in sewage sludges after nearly a decade in frozen storage. Six antibiotics were detected at the following average concentrations (ng/g dry weight): amoxicillin (1.0), nalidixic acid (19.1), oxolinic acid (2.7), erythromycin (0.6), oxytetracycline (4.5), and ampicillin (14.8). The remaining four were not detected in any samples (<method detection limit, ng/g dry weight): sulfadimethoxine (<0.5), sulfadimidine (<1.0), spiramycin (<2.0), and NP-AOZ (<20.0). This study provides the first data on spiramycin, NP-AOZ, and nalidixic acid in U.S. sewage sludges. This study also provides new data on the losses of 5 antibiotics during long term frozen storage (-20 °C) in comparison to the 2006/2007 U.S. EPA Targeted National Sewage Sludge Survey.

KEYWORDS:

Antibiotics; Archived samples; Biosolids; Sewage sludge; Storage

6.
Transbound Emerg Dis. 2018 Apr 24. doi: 10.1111/tbed.12883. [Epub ahead of print]

Phylogeography of H5N1 avian influenza virus in Indonesia.

Author information

1
School of Public Health and Community Medicine, University of New South Wales Sydney, Sydney, NSW, Australia.
2
Center for Environmental Health Engineering, Biodesign Institute, Arizona State University, Tempe, Arizona.
3
College of Health Solutions, Arizona State University, Phoenix, Arizona.
4
College of Public Service and Community Solution, Arizona State University, Phoenix, Arizona.

Abstract

Highly pathogenic avian influenza (HPAI) viruses of the H5N1 subtype are a major concern to human and animal health in Indonesia. This study aimed to characterize transmission dynamics of H5N1 over time using novel Bayesian phylogeography methods to identify factors which have influenced the spread of H5N1 in Indonesia. We used publicly available hemagglutinin sequence data sampled between 2003 and 2016 to model ancestral state reconstruction of HPAI H5N1 evolution. We found strong support for H5N1 transmission routes between provinces in Java Island and inter-island transmissions, such as between Nusa Tenggara and Kalimantan Islands, not previously described. The spread is consistent with wild bird flyways and poultry trading routes. H5N1 migration was associated with the regions of high chicken densities and low human development indices. These results can be used to inform more targeted planning of H5N1 control and prevention activities in Indonesia.

KEYWORDS:

H5N1 Subtype; Indonesia; Influenza A Virus; One Health; Phylogeography

7.
Sci Rep. 2018 Apr 12;8(1):5905. doi: 10.1038/s41598-018-24264-8.

The Effects of Sampling Location and Predictor Point Estimate Certainty on Posterior Support in Bayesian Phylogeographic Generalized Linear Models.

Author information

1
Department of Biomedical Informatics, Arizona State University, Tempe, Arizona, USA.
2
Biodesign Center for Environmental Health Engineering, Arizona State University, Tempe, Arizona, USA.
3
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, USA.
4
School of Life Sciences, Arizona State University, Tempe, Arizona, USA.
5
Department of Biomedical Informatics, Arizona State University, Tempe, Arizona, USA. matthew.scotch@asu.edu.
6
Biodesign Center for Environmental Health Engineering, Arizona State University, Tempe, Arizona, USA. matthew.scotch@asu.edu.

Abstract

The use of generalized linear models in Bayesian phylogeography has enabled researchers to simultaneously reconstruct the spatiotemporal history of a virus and quantify the contribution of predictor variables to that process. However, little is known about the sensitivity of this method to the choice of the discrete state partition. Here we investigate this question by analyzing a data set containing 299 sequences of the West Nile virus envelope gene sampled in the United States and fifteen predictors aggregated at four spatial levels. We demonstrate that although the topology of the viral phylogenies was consistent across analyses, support for the predictors depended on the level of aggregation. In particular, we found that the variance of the predictor support metrics was minimized at the most precise level for several predictors and maximized at more sparse levels of aggregation. These results suggest that caution should be taken when partitioning a region into discrete locations to ensure that interpretable, reproducible posterior estimates are obtained. These results also demonstrate why researchers should use the most precise discrete states possible to minimize the posterior variance in such estimates and reveal what truly drives the diffusion of viruses.

8.
Bioinformatics. 2018 May 1;34(9):1606-1608. doi: 10.1093/bioinformatics/btx799.

GeoBoost: accelerating research involving the geospatial metadata of virus GenBank records.

Author information

1
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA.
2
Institute of Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
3
Biodesign Center for Environmental Health Engineering, Arizona State University, Tempe, AZ 85281, USA.

Abstract

Summary:

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.

Availability and implementation:

Binaries and resources required for running GeoBoost are packed into a single zipped file and freely available for download at https://tinyurl.com/geoboost. A video tutorial is included to help users quickly and easily install and run the software. The software is implemented in Java 1.8, and supported on MS Windows and Linux platforms.

Contact:

gragon@upenn.edu.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
29240889
PMCID:
PMC5925778
[Available on 2019-05-01]
DOI:
10.1093/bioinformatics/btx799
Icon for Silverchair Information Systems
9.
AMIA Jt Summits Transl Sci Proc. 2017 Jul 26;2017:114-122. eCollection 2017.

Extracting geographic locations from the literature for virus phylogeography using supervised and distant supervision methods.

Author information

1
Arizona State University, Tempe, Arizona, USA.
2
University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Abstract

The field of phylogeography allows researchers to model the spread and evolution of viral genetic sequences. Phylogeography plays a major role in infectious disease surveillance, viral epidemiology and vaccine design. When conducting viral phylogeographic studies, researchers require the location of the infected host of the virus, which is often present in public databases such as GenBank. However, the geographic metadata in most GenBank records is not precise enough for many phylogeographic studies; therefore, researchers often need to search the articles linked to the records for more information, which can be a tedious process. Here, we describe two approaches for automatically detecting geographic location mentions in articles pertaining to virus-related GenBank records: a supervised sequence labeling approach with innovative features and a distant-supervision approach with novel noise- reduction methods. Evaluated on a manually annotated gold standard, our supervised sequence labeling and distant supervision approaches attained F-scores of 0.81 and 0.66, respectively.

10.
Phys Rev E. 2017 May;95(5-1):052320. doi: 10.1103/PhysRevE.95.052320. Epub 2017 May 31.

Multiscale model for pedestrian and infection dynamics during air travel.

Author information

1
Department of Aerospace Engineering, Embry-Riddle Aeronautical University, Daytona Beach, Florida 32114, USA.
2
SAL Mathematical, Computational and Modeling Science Center, School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona 85287, USA.
3
Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona 85259, USA.
4
Biodesign Center for Environmental Security, Arizona State University, Tempe, Arizona 85257, USA.
5
Department of Computer Science, Florida State University, Tallahassee, Florida 32306, USA.

Abstract

In this paper we develop a multiscale model combining social-force-based pedestrian movement with a population level stochastic infection transmission dynamics framework. The model is then applied to study the infection transmission within airplanes and the transmission of the Ebola virus through casual contacts. Drastic limitations on air-travel during epidemics, such as during the 2014 Ebola outbreak in West Africa, carry considerable economic and human costs. We use the computational model to evaluate the effects of passenger movement within airplanes and air-travel policies on the geospatial spread of infectious diseases. We find that boarding policy by an airline is more critical for infection propagation compared to deplaning policy. Enplaning in two sections resulted in fewer infections than the currently followed strategy with multiple zones. In addition, we found that small commercial airplanes are better than larger ones at reducing the number of new infections in a flight. Aggregated results indicate that passenger movement strategies and airplane size predicted through these network models can have significant impact on an event like the 2014 Ebola epidemic. The methodology developed here is generic and can be readily modified to incorporate the impact from the outbreak of other directly transmitted infectious diseases.

11.
Influenza Other Respir Viruses. 2017 Jul;11(4):306-310. doi: 10.1111/irv.12458. Epub 2017 Jun 24.

Does influenza pandemic preparedness and mitigation require gain-of-function research?

Author information

1
School of Public Health and Community Medicine, UNSW, Sydney, NSW, Australia.
2
Biodesign Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
3
Department of Biomedical Informatics, College of Health Solutions, Arizona State University, Tempe, AZ, USA.
4
College of Public Service & Community Solutions, Arizona State University, Tempe, AZ, USA.

Abstract

The risk and benefits of gain-of-function studies on influenza A have been widely debated since 2012 when the methods to create two respiratory transmissible H5N1 mutant isolates were published. Opponents of gain-of-function studies argue the biosecurity risk is unacceptable, while proponents cite potential uses for pandemic surveillance, preparedness and mitigation. In this commentary, we provide an overview of the background and applications of gain-of-function research and argue that the anticipated benefits have yet to materialize while the significant risks remain.

KEYWORDS:

influenza; pandemics; public health surveillance

PMID:
28502086
PMCID:
PMC5485867
DOI:
10.1111/irv.12458
[Indexed for MEDLINE]
Free PMC Article
Icon for Wiley Icon for PubMed Central
12.
PLoS Comput Biol. 2017 Feb 7;13(2):e1005389. doi: 10.1371/journal.pcbi.1005389. eCollection 2017 Feb.

Bayesian phylogeography of influenza A/H3N2 for the 2014-15 season in the United States using three frameworks of ancestral state reconstruction.

Author information

1
Department of Biomedical Informatics, Arizona State University, Tempe, Arizona, United States of America.
2
Biodesign Center for Environmental Security, Arizona State University, Tempe, Arizona, United States of America.
3
Departments of Biomathematics and Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America.
4
Department of Biostatistics, School of Public Health, University of California, Los Angeles, California, United States of America.

Abstract

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.

PMID:
28170397
PMCID:
PMC5321473
DOI:
10.1371/journal.pcbi.1005389
[Indexed for MEDLINE]
Free PMC Article
Icon for Public Library of Science Icon for PubMed Central
13.
J Am Med Inform Assoc. 2016 Sep;23(5):934-41. doi: 10.1093/jamia/ocv172. Epub 2016 Jan 17.

A high-precision rule-based extraction system for expanding geospatial metadata in GenBank records.

Author information

1
Department of Biomedical Informatics, Arizona State University, 13212 E Shea Blvd, Scottsdale, AZ 85259, USA ttahsin@asu.edu.
2
Department of Biomedical Informatics, Arizona State University, 13212 E Shea Blvd, Scottsdale, AZ 85259, USA.

Abstract

OBJECTIVE:

The metadata reflecting the location of the infected host (LOIH) of virus sequences in GenBank often lacks specificity. This work seeks to enhance this metadata by extracting more specific geographic information from related full-text articles and mapping them to their latitude/longitudes using knowledge derived from external geographical databases.

MATERIALS AND METHODS:

We developed a rule-based information extraction framework for linking GenBank records to the latitude/longitudes of the LOIH. Our system first extracts existing geospatial metadata from GenBank records and attempts to improve it by seeking additional, relevant geographic information from text and tables in related full-text PubMed Central articles. The final extracted locations of the records, based on data assimilated from these sources, are then disambiguated and mapped to their respective geo-coordinates. We evaluated our approach on a manually annotated dataset comprising of 5728 GenBank records for the influenza A virus.

RESULTS:

We found the precision, recall, and f-measure of our system for linking GenBank records to the latitude/longitudes of their LOIH to be 0.832, 0.967, and 0.894, respectively.

DISCUSSION:

Our system had a high level of accuracy for linking GenBank records to the geo-coordinates of the LOIH. However, it can be further improved by expanding our database of geospatial data, incorporating spell correction, and enhancing the rules used for extraction.

CONCLUSION:

Our system performs reasonably well for linking GenBank records for the influenza A virus to the geo-coordinates of their LOIH based on record metadata and information extracted from related full-text articles.

KEYWORDS:

information extraction; natural language processing; phylogeography

PMID:
26911818
PMCID:
PMC4997033
DOI:
10.1093/jamia/ocv172
[Indexed for MEDLINE]
Free PMC Article
Icon for Silverchair Information Systems Icon for PubMed Central
14.
Drug Saf. 2016 Mar;39(3):231-40. doi: 10.1007/s40264-015-0379-4.

Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter.

Author information

1
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA. abeed.sarker@asu.edu.
2
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.
3
Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
4
Rueckert-Hartman College for Health Professions, Regis University, Denver, CO, USA.
5
Department of Pharmacy Practice and Science, University of Arizona, Tucson, AZ, USA.

Abstract

INTRODUCTION:

Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications.

OBJECTIVES:

Our primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts.

METHODS:

We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall(®), oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time.

RESULTS:

Our analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall(®): 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41, F measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time.

CONCLUSION:

Our study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks.

PMID:
26748505
PMCID:
PMC4749656
DOI:
10.1007/s40264-015-0379-4
[Indexed for MEDLINE]
Free PMC Article
Icon for Springer Icon for PubMed Central

Conflict of interest statement

Compliance with Ethical Standards Funding This work was supported by National Institutes of Health (NIH) National Library of Medicine (NLM) grant number NIH NLM 5R01LM011176. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NLM or NIH. Conflict of interest Abeed Sarker, Rachel Ginn, Karen O’Connor, Matthew Scotch, Karen Smith, Dan Malone, and Graciela Gonzalez have no conflicts of interest that are directly relevant to the content of this study. Ethical approval Not applicable. Informed consent Not applicable.

15.
Front Microbiol. 2015 Dec 22;6:1456. doi: 10.3389/fmicb.2015.01456. eCollection 2015.

Evolution of 2014/15 H3N2 Influenza Viruses Circulating in US: Consequences for Vaccine Effectiveness and Possible New Pandemic.

Author information

1
Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade Belgrade, Serbia.
2
Galveston National Laboratory, Department of Pathology, University of Texas Medical Branch Galveston, TX, USA.
3
Department of Biomedical Informatics, Arizona State UniversityScottsdale, AZ, USA; Center for Environmental Security, Biodesign Institute and Global Security Initiative, Arizona State UniversityTempe, AZ, USA.

Abstract

A key factor in the effectiveness of the seasonal influenza vaccine is its immunological compatibility with the circulating viruses during the season. Here we propose a new bioinformatics approach for analysis of influenza viruses which could be used as an efficient tool for selection of vaccine viruses, assessment of the effectiveness of seasonal influenza vaccines, and prediction of the epidemic/pandemic potential of novel influenza viruses.

KEYWORDS:

H3N2; influenza virus; pandemic potential; phylogenetic analysis; seasonal influenza vaccine effectiveness

16.
AMIA Jt Summits Transl Sci Proc. 2015 Mar 23;2015:212-6. eCollection 2015.

Conceptualizing a Novel Quasi-Continuous Bayesian Phylogeographic Framework for Spatiotemporal Hypothesis Testing.

Author information

1
Arizona State University, Tempe, AZ, USA.

Abstract

Continuous phylogeography is a growing approach to studying the spatiotemporal origins of RNA viruses because of its realistic spatial reconstruction advantages over discrete phylogeography. While the generalized linear model has been demonstrated as an effective tool for simultaneously assessing the drivers impacting viral diffusion in discrete phylogeography, there is no similar testing method in the continuous phylogeographic framework. In this paper, we take a step toward bridging that gap by conceptualizing a novel quasi-continuous approach which enables the addition of discrete locations beyond the known sampling locations of the virus. Our model, when fully developed into phylogeographic software, will enable spatiotemporal hypothesis testing of viral diffusion without being strictly limited to observed sampling locations. This model can still assess the impact of local epidemiological variables on virus spread and could provide public health agencies with more realistic estimates of key predictors and locations by utilizing a more continuous landscape.

17.
AMIA Jt Summits Transl Sci Proc. 2015 Mar 23;2015:36-40. eCollection 2015.

Analysis of Viral Genetics for Estimating Diffusion of Influenza A H6N1.

Author information

1
Arizona State University, Tempe, AZ, USA.
2
University of California, Los Angeles, Los Angeles, CA.
3
University of Washington, Seattle, WA.

Abstract

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.

18.
Bioinformatics. 2015 Jun 15;31(12):i348-56. doi: 10.1093/bioinformatics/btv259.

Knowledge-driven geospatial location resolution for phylogeographic models of virus migration.

Author information

1
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA.
2
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA.

Abstract

Diseases caused by zoonotic viruses (viruses transmittable between humans and animals) are a major threat to public health throughout the world. By studying virus migration and mutation patterns, the field of phylogeography provides a valuable tool for improving their surveillance. A key component in phylogeographic analysis of zoonotic viruses involves identifying the specific locations of relevant viral sequences. This is usually accomplished by querying public databases such as GenBank and examining the geospatial metadata in the record. When sufficient detail is not available, a logical next step is for the researcher to conduct a manual survey of the corresponding published articles.

MOTIVATION:

In this article, we present a system for detection and disambiguation of locations (toponym resolution) in full-text articles to automate the retrieval of sufficient metadata. Our system has been tested on a manually annotated corpus of journal articles related to phylogeography using integrated heuristics for location disambiguation including a distance heuristic, a population heuristic and a novel heuristic utilizing knowledge obtained from GenBank metadata (i.e. a 'metadata heuristic').

RESULTS:

For detecting and disambiguating locations, our system performed best using the metadata heuristic (0.54 Precision, 0.89 Recall and 0.68 F-score). Precision reaches 0.88 when examining only the disambiguation of location names. Our error analysis showed that a noticeable increase in the accuracy of toponym resolution is possible by improving the geospatial location detection. By improving these fundamental automated tasks, our system can be a useful resource to phylogeographers that rely on geospatial metadata of GenBank sequences. .

PMID:
26072502
PMCID:
PMC4542781
DOI:
10.1093/bioinformatics/btv259
[Indexed for MEDLINE]
Free PMC Article
Icon for Silverchair Information Systems Icon for PubMed Central
19.
Front Microbiol. 2015 Feb 19;6:135. doi: 10.3389/fmicb.2015.00135. eCollection 2015.

In silico analysis suggests interaction between Ebola virus and the extracellular matrix.

Author information

1
Center for Multidisciplinary Research, Institute of Nuclear Sciences VINCA, University of Belgrade Belgrade, Serbia.
2
Luxembourg Institute of Health (former Centre de Recherche Public de la Santé)/Laboratoire National de Santé Luxembourg, Luxembourg.
3
Department of Biomedical Informatics, Arizona State University Scottsdale, AZ, USA ; Center for Environmental Security, Biodesign Institute and Security and Defense Systems Initiative, Arizona State University Tempe, AZ, USA.
4
Canadian Blood Services, Center for Innovation Toronto, ON, Canada.
5
Innovation Center of the Faculty of Chemistry, University of Belgrade Belgrade, Serbia.
6
Divisione di Oncologia Sperimentale, Centro di Riferimento Oncologico CRO-IRCCS Aviano, Italy.

Abstract

The worst Ebola virus (EV) outbreak in history has hit Liberia, Sierra Leone and Guinea hardest and the trend lines in this crisis are grave, and now represents a global public health threat concern. Limited therapeutic and/or prophylactic options are available for people suffering from Ebola virus disease (EVD) and further complicate the situation. Previous studies suggested that the EV glycoprotein (GP) is the main determinant causing structural damage of endothelial cells that triggers the hemorrhagic diathesis, but molecular mechanisms underlying this phenomenon remains elusive. Using the informational spectrum method (ISM), a virtual spectroscopy method for analysis of the protein-protein interactions, the interaction of GP with endothelial extracellular matrix (ECM) was investigated. Presented results of this in silico study suggest that Elastin Microfibril Interface Located Proteins (EMILINs) are involved in interaction between GP and ECM. This finding could contribute to a better understanding of EV/endothelium interaction and its role in pathogenesis, prevention and therapy of EVD.

KEYWORDS:

EMILINs; Ebola virus; endothelial extracellular matrix; glycoprotein GP; in silico analysis

20.
AMIA Jt Summits Transl Sci Proc. 2014 Apr 7;2014:102-11. eCollection 2014.

Natural language processing methods for enhancing geographic metadata for phylogeography of zoonotic viruses.

Author information

1
Arizona State University, Tempe, AZ, USA.

Abstract

Zoonotic viruses represent emerging or re-emerging pathogens that pose significant public health threats throughout the world. It is therefore crucial to advance current surveillance mechanisms for these viruses through outlets such as phylogeography. Despite the abundance of zoonotic viral sequence data in publicly available databases such as GenBank, phylogeographic analysis of these viruses is often limited by the lack of adequate geographic metadata. However, many GenBank records include references to articles with more detailed information and automated systems may help extract this information efficiently and effectively. In this paper, we describe our efforts to determine the proportion of GenBank records with "insufficient" geographic metadata for seven well-studied viruses. We also evaluate the performance of four different Named Entity Recognition (NER) systems for automatically extracting related entities using a manually created gold-standard.

Supplemental Content

Loading ...
Support Center