10,867 research outputs found
What's unusual in online disease outbreak news?
Background: Accurate and timely detection of public health events of
international concern is necessary to help support risk assessment and response
and save lives. Novel event-based methods that use the World Wide Web as a
signal source offer potential to extend health surveillance into areas where
traditional indicator networks are lacking. In this paper we address the issue
of systematically evaluating online health news to support automatic alerting
using daily disease-country counts text mined from real world data using
BioCaster. For 18 data sets produced by BioCaster, we compare 5 aberration
detection algorithms (EARS C2, C3, W2, F-statistic and EWMA) for performance
against expert moderated ProMED-mail postings. Results: We report sensitivity,
specificity, positive predictive value (PPV), negative predictive value (NPV),
mean alerts/100 days and F1, at 95% confidence interval (CI) for 287
ProMED-mail postings on 18 outbreaks across 14 countries over a 366 day period.
Results indicate that W2 had the best F1 with a slight benefit for day of week
effect over C2. In drill down analysis we indicate issues arising from the
granular choice of country-level modeling, sudden drops in reporting due to day
of week effects and reporting bias. Automatic alerting has been implemented in
BioCaster available from http://born.nii.ac.jp. Conclusions: Online health news
alerts have the potential to enhance manual analytical methods by increasing
throughput, timeliness and detection rates. Systematic evaluation of health
news aberrations is necessary to push forward our understanding of the complex
relationship between news report volumes and case numbers and to select the
best performing features and algorithms
Enhancing Twitter Data Analysis with Simple Semantic Filtering: Example in Tracking Influenza-Like Illnesses
Systems that exploit publicly available user generated content such as
Twitter messages have been successful in tracking seasonal influenza. We
developed a novel filtering method for Influenza-Like-Illnesses (ILI)-related
messages using 587 million messages from Twitter micro-blogs. We first filtered
messages based on syndrome keywords from the BioCaster Ontology, an extant
knowledge model of laymen's terms. We then filtered the messages according to
semantic features such as negation, hashtags, emoticons, humor and geography.
The data covered 36 weeks for the US 2009 influenza season from 30th August
2009 to 8th May 2010. Results showed that our system achieved the highest
Pearson correlation coefficient of 98.46% (p-value<2.2e-16), an improvement of
3.98% over the previous state-of-the-art method. The results indicate that
simple NLP-based enhancements to existing approaches to mine Twitter data can
increase the value of this inexpensive resource.Comment: 10 pages, 5 figures, IEEE HISB 2012 conference, Sept 27-28, 2012, La
Jolla, California, U
Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling
Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue
and Zika in Brasil and other tropical regions has long been a priority for
governments in affected areas. Streaming social media content, such as Twitter,
is increasingly being used for health vigilance applications such as flu
detection. However, previous work has not addressed the complexity of drastic
seasonal changes on Twitter content across multiple epidemic outbreaks. In
order to address this gap, this paper contrasts two complementary approaches to
detecting Twitter content that is relevant for Dengue outbreak detection,
namely supervised classification and unsupervised clustering using topic
modelling. Each approach has benefits and shortcomings. Our classifier achieves
a prediction accuracy of about 80\% based on a small training set of about
1,000 instances, but the need for manual annotation makes it hard to track
seasonal changes in the nature of the epidemics, such as the emergence of new
types of virus in certain geographical locations. In contrast, LDA-based topic
modelling scales well, generating cohesive and well-separated clusters from
larger samples. While clusters can be easily re-generated following changes in
epidemics, however, this approach makes it hard to clearly segregate relevant
tweets into well-defined clusters.Comment: Procs. SoWeMine - co-located with ICWE 2016. 2016, Lugano,
Switzerlan
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Clinical metagenomics.
Clinical metagenomic next-generation sequencing (mNGS), the comprehensive analysis of microbial and host genetic material (DNA and RNA) in samples from patients, is rapidly moving from research to clinical laboratories. This emerging approach is changing how physicians diagnose and treat infectious disease, with applications spanning a wide range of areas, including antimicrobial resistance, the microbiome, human host gene expression (transcriptomics) and oncology. Here, we focus on the challenges of implementing mNGS in the clinical laboratory and address potential solutions for maximizing its impact on patient care and public health
Dynamical Patterns of Cattle Trade Movements
Despite their importance for the spread of zoonotic diseases, our
understanding of the dynamical aspects characterizing the movements of farmed
animal populations remains limited as these systems are traditionally studied
as static objects and through simplified approximations. By leveraging on the
network science approach, here we are able for the first time to fully analyze
the longitudinal dataset of Italian cattle movements that reports the mobility
of individual animals among farms on a daily basis. The complexity and
inter-relations between topology, function and dynamical nature of the system
are characterized at different spatial and time resolutions, in order to
uncover patterns and vulnerabilities fundamental for the definition of targeted
prevention and control measures for zoonotic diseases. Results show how the
stationarity of statistical distributions coexists with a strong and
non-trivial evolutionary dynamics at the node and link levels, on all
timescales. Traditional static views of the displacement network hide important
patterns of structural changes affecting nodes' centrality and farms' spreading
potential, thus limiting the efficiency of interventions based on partial
longitudinal information. By fully taking into account the longitudinal
dimension, we propose a novel definition of dynamical motifs that is able to
uncover the presence of a temporal arrow describing the evolution of the system
and the causality patterns of its displacements, shedding light on mechanisms
that may play a crucial role in the definition of preventive actions
Dynamical Patterns of Cattle Trade Movements
Despite their importance for the spread of zoonotic diseases, our
understanding of the dynamical aspects characterizing the movements of farmed
animal populations remains limited as these systems are traditionally studied
as static objects and through simplified approximations. By leveraging on the
network science approach, here we are able for the first time to fully analyze
the longitudinal dataset of Italian cattle movements that reports the mobility
of individual animals among farms on a daily basis. The complexity and
inter-relations between topology, function and dynamical nature of the system
are characterized at different spatial and time resolutions, in order to
uncover patterns and vulnerabilities fundamental for the definition of targeted
prevention and control measures for zoonotic diseases. Results show how the
stationarity of statistical distributions coexists with a strong and
non-trivial evolutionary dynamics at the node and link levels, on all
timescales. Traditional static views of the displacement network hide important
patterns of structural changes affecting nodes' centrality and farms' spreading
potential, thus limiting the efficiency of interventions based on partial
longitudinal information. By fully taking into account the longitudinal
dimension, we propose a novel definition of dynamical motifs that is able to
uncover the presence of a temporal arrow describing the evolution of the system
and the causality patterns of its displacements, shedding light on mechanisms
that may play a crucial role in the definition of preventive actions
Retailers' press release activity: market signals for stakeholder engagement?
PURPOSE – To increase understanding of the role, content and effectiveness of press releases. DESIGN/METHODOLOGY/APPROACH – Qualitative and quantitative analyses of UK supermarkets' press releases in 2001/2002, a medium previously little researched in marketing, are undertaken. FINDINGS – Supermarkets seek stakeholder engagement on diverse issues with different mixes of groups. Treating releases as market signals demonstrates the important role of the press as filters and interpreters. Intended messages frequently fail to reach target audiences, and when they do can be significantly reinterpreted, so that positive claims are reported critically and negatively. Larger chains apparently produce more releases and gain more newspaper coverage, but generally volume of releases does not improve likelihood of press coverage. RESEARCH LIMITATIONS/IMPLICATIONS – The nature of qualitative data, the limited time frame, and possible omissions from source archives. PRACTICAL IMPLICATIONS – As level of coverage varied independently of turnover, higher release activity of larger chains was questioned. Seeking differentiation through press releases becomes problematic with the press as filters. ORIGINALITY/VALUE – This paper increases knowledge of press releases as market communications, and contributes to the literature of market signalling, notably emphasising the press as important signal intermediaries
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