4,727 research outputs found
Towards cross-lingual alerting for bursty epidemic events
Background: Online news reports are increasingly becoming a source for event
based early warning systems that detect natural disasters. Harnessing the
massive volume of information available from multilingual newswire presents as
many challenges as opportunities due to the patterns of reporting complex
spatiotemporal events. Results: In this article we study the problem of
utilising correlated event reports across languages. We track the evolution of
16 disease outbreaks using 5 temporal aberration detection algorithms on
text-mined events classified according to disease and outbreak country. Using
ProMED reports as a silver standard, comparative analysis of news data for 13
languages over a 129 day trial period showed improved sensitivity, F1 and
timeliness across most models using cross-lingual events. We report a detailed
case study analysis for Cholera in Angola 2010 which highlights the challenges
faced in correlating news events with the silver standard. Conclusions: The
results show that automated health surveillance using multilingual text mining
has the potential to turn low value news into high value alerts if informed
choices are used to govern the selection of models and data sources. An
implementation of the C2 alerting algorithm using multilingual news is
available at the BioCaster portal http://born.nii.ac.jp/?page=globalroundup
Political and Economic Patterns in COVID-19 News: From Lockdown to Vaccination
The purpose of this study is to analyse COVID-19 related news published
across different geographical places, in order to gain insights in reporting
differences. The COVID-19 pandemic had a major outbreak in January 2020 and was
followed by different preventive measures, lockdown, and finally by the process
of vaccination. To date, more comprehensive analysis of news related to
COVID-19 pandemic are missing, especially those which explain what aspects of
this pandemic are being reported by newspapers inserted in different economies
and belonging to different political alignments. Since LDA is often less
coherent when there are news articles published across the world about an event
and you look answers for specific queries. It is because of having semantically
different content. To address this challenge, we performed pooling of news
articles based on information retrieval using TF-IDF score in a data processing
step and topic modeling using LDA with combination of 1 to 6 ngrams. We used
VADER sentiment analyzer to analyze the differences in sentiments in news
articles reported across different geographical places. The novelty of this
study is to look at how COVID-19 pandemic was reported by the media, providing
a comparison among countries in different political and economic contexts. Our
findings suggest that the news reporting by newspapers with different political
alignment support the reported content. Also, economic issues reported by
newspapers depend on economy of the place where a newspaper resides
Real-time processing of social media with SENTINEL: a syndromic surveillance system incorporating deep learning for health classification
Interest in real-time syndromic surveillance based on social media data has greatly increased in recent years. The ability to detect disease outbreaks earlier than traditional methods would be highly useful for public health officials. This paper describes a software system which is built upon recent developments in machine learning and data processing to achieve this goal. The system is built from reusable modules integrated into data processing pipelines that are easily deployable and configurable. It applies deep learning to the problem of classifying health-related tweets and is able to do so with high accuracy. It has the capability to detect illness outbreaks from Twitter data and then to build up and display information about these outbreaks, including relevant news articles, to provide situational awareness. It also provides nowcasting functionality of current disease levels from previous clinical data combined with Twitter data. The preliminary results are promising, with the system being able to detect outbreaks of influenza-like illness symptoms which could then be confirmed by existing official sources. The Nowcasting module shows that using social media data can improve prediction for multiple diseases over simply using traditional data sources
Global disease monitoring and forecasting with Wikipedia
Infectious disease is a leading threat to public health, economic stability,
and other key social structures. Efforts to mitigate these impacts depend on
accurate and timely monitoring to measure the risk and progress of disease.
Traditional, biologically-focused monitoring techniques are accurate but costly
and slow; in response, new techniques based on social internet data such as
social media and search queries are emerging. These efforts are promising, but
important challenges in the areas of scientific peer review, breadth of
diseases and countries, and forecasting hamper their operational usefulness.
We examine a freely available, open data source for this use: access logs
from the online encyclopedia Wikipedia. Using linear models, language as a
proxy for location, and a systematic yet simple article selection procedure, we
tested 14 location-disease combinations and demonstrate that these data
feasibly support an approach that overcomes these challenges. Specifically, our
proof-of-concept yields models with up to 0.92, forecasting value up to
the 28 days tested, and several pairs of models similar enough to suggest that
transferring models from one location to another without re-training is
feasible.
Based on these preliminary results, we close with a research agenda designed
to overcome these challenges and produce a disease monitoring and forecasting
system that is significantly more effective, robust, and globally comprehensive
than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein
and adjust novelty claims accordingly; revise title; various revisions for
clarit
Delving into Geospatial Data Services: Monitoring Earth for Covid-19 Impact Measure and Decision Making
Geospatial technologies are crucial for many applications and can facilitate decision-making to benefit society. When the Covid-19 pandemic restricted most of the services, geospatial technologies like satellite remote sensing, geographical information systems, and other allied technologies were found essential. They speed up many critical decision-making processes in the fight against the pandemic. This paper explores the significant contributions from the geospatial aspects throughout the pandemic in various research domains. The potential applications of geospatial technology to assist humanity during the pandemic are thoroughly examined. We categorized the entire study into i) environmental monitoring services, ii) disease control and management services, and iii) forecasting and decision-making services. Many valuable findings are derived based on the systematic review of some remarkable works. The outcome helps us understand how decision-making and forecasting are essential in the fight against the pandemic, with profound implications for future multidisciplinary research using geospatial technology
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