Analysis of syntactic and semantic features for fine-grained event-spatial understanding in outbreak news reports

<p>Abstract</p> <p>Background</p> <p>Previous studies have suggested that epidemiological reasoning needs a fine-grained modelling of events, especially their spatial and temporal attributes. While the temporal analysis of events has been intensively studied, far less attention has been paid to their spatial analysis. This article aims at filling the gap concerning automatic event-spatial attribute analysis in order to support health surveillance and epidemiological reasoning.</p> <p>Results</p> <p>In this work, we propose a methodology that provides a detailed analysis on each event reported in news articles to recover the most specific locations where it occurs. Various features for recognizing spatial attributes of the events were studied and incorporated into the models which were trained by several machine learning techniques. The best performance for spatial attribute recognition is very promising; 85.9% F-score (86.75% precision/85.1% recall).</p> <p>Conclusions</p> <p>We extended our work on event-spatial attribute recognition by focusing on machine learning techniques, which are CRF, SVM, and Decision tree. Our approach avoided the costly development of an external knowledge base by employing the feature sources that can be acquired locally from the analyzed document. The results showed that the CRF model performed the best. Our study indicated that the nearest location and previous event location are the most important features for the CRF and SVM model, while the location extracted from the verb's subject is the most important to the Decision tree model.</p

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

Efficient Text Classification with Linear Regression Using a Combination of Predictors for Flu Outbreak Detection

Early prediction of disease outbreaks and seasonal epidemics such as Influenza may reduce their impact on daily lives. Today, the web can be used for surveillance of diseases.Search engines and Social Networking Sites can be used to track trends of different diseases more quickly than government agencies such as Center of Disease Control and Prevention(CDC). Today, Social Networking Sites (SNS) are widely used by diverse demographic populations. Thus, SNS data can be used effectively to track disease outbreaks and provide necessary warnings. Although the generated data of microblogging sites is valuable for real time analysis and outbreak predictions, the volume is huge. Therefore, one of the main challenges in analyzing this huge volume of data is to find the best approach for accurate analysis in an efficient time. Regardless of the analysis time, many studies show only the accuracy of applying different machine learning approaches. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. The aim of this study is to propose an efficient and accurate framework that uses SNS data to track disease outbreaks and provide early warnings, even for newest outbreaks accurately. The presented framework of outbreak prediction consists of three main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module utilizes the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText and six conventional machine learning algorithms, are evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers have been trained and tested using a pre-labeled dataset of flu-related and unrelated Twitter postings. The selected text classifier is then used to classify over 8,400,000 tweet documents. The flu-related documents are then mapped ona weekly basis using a mapping module. Lastly, the mapped results are passed together with historical Center for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. The evaluation of flu tweet classification shows that FastText together with the extracted features, has achieved accurate results with anF-measure value of 89.9% in addition to its efficiency. Therefore, FastText has been chosen to be the classification module to work together with the other modules in the proposed framework, including the linear regression module, for flu trend predictions. The prediction results are compared with the available recent data from CDC as the ground truth and show a strong correlation of 96.2%

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117

Vulnerability and One Health assessment approaches for infectious threats from a social science perspective:a systematic scoping review

Vulnerability assessments identify vulnerable groups and can promote effective community engagement in responding to and mitigating destabilising events. This scoping review maps assessments for local-level vulnerabilities in the context of infectious threats. We searched various databases for articles written between 1978 and 2019. Eligible documents assessed local-level vulnerability, focusing on infectious threats and antimicrobial resistance. Since few studies provided this dual focus, we included tools from climate change and disaster risk reduction literature that engaged the community in the assessment. We considered studies using a One Health approach as essential for identifying vulnerability risk factors for zoonotic disease affecting humans. Of the 5390 records, we selected 36 articles for review. This scoping review fills a gap regarding vulnerability assessments by combining insights from various approaches: local-level understandings of vulnerability involving community perspectives; studies of social and ecological factors relevant to exposure; and integrated quantitative and qualitative methods that make generalisations based on direct observation. The findings inform the development of new tools to identify vulnerabilities and their relation to social and natural environments

Arboviral disease outbreaks in the Pacific Islands countries and areas, 2014 to 2020: a systematic literature and document review

Arthropod-borne diseases pose a significant public health threat, accounting for greater than 17% of infectious disease cases and 1 million deaths annually. Across Pacific Island countries and areas (PICs), outbreaks of dengue, chikungunya, and Zika are increasing in frequency and scale. Data about arbovirus outbreaks are incomplete, with reports sporadic, delayed, and often based solely on syndromic surveillance. We undertook a systematic review of published and grey literature and contacted relevant regional authorities to collect information about arboviral activity affecting PICs between October 2014 and June 2020. Our literature search identified 1176 unique peer-reviewed articles that were reduced to 25 relevant publications when screened. Our grey literature search identified 873 sources. Collectively, these data reported 104 unique outbreaks, including 72 dengue outbreaks affecting 19 (out of 22) PICs, 14 chikungunya outbreaks affecting 11 PICs, and 18 Zika outbreaks affecting 14 PICs. Our review is the most complete account of arboviral outbreaks to affect PICs since comparable work was published in 2014. It highlights the continued elevated level of arboviral activity across the Pacific and inconsistencies in how information about outbreaks is reported and recorded. It demonstrates the importance of a One-Health approach and the role that improved communication and reporting between different governments and sectors play in understanding the emergence, circulation, and transboundary risks posed by arboviral diseases

A Review of Influenza Detection and Prediction Through Social Networking Sites

Early prediction of seasonal epidemics such as influenza may reduce their impact in daily lives. Nowadays, the web can be used for surveillance of diseases. Search engines and social networking sites can be used to track trends of different diseases seven to ten days faster than government agencies such as Center of Disease Control and Prevention (CDC). CDC uses the Illness-Like Influenza Surveillance Network (ILINet), which is a program used to monitor Influenza-Like Illness (ILI) sent by thousands of health care providers in order to detect influenza outbreaks. It is a reliable tool, however, it is slow and expensive. For that reason, many studies aim to develop methods that do real time analysis to track ILI using social networking sites. Social media data such as Twitter can be used to predict the spread of flu in the population and can help in getting early warnings. Today, social networking sites (SNS) are used widely by many people to share thoughts and even health status. Therefore, SNS provides an efficient resource for disease surveillance and a good way to communicate to prevent disease outbreaks. The goal of this study is to review existing alternative solutions that track flu outbreak in real time using social networking sites and web blogs. Many studies have shown that social networking sites can be used to conduct real time analysis for better predictions.https://doi.org/10.1186/s12976-017-0074-

When Infodemic Meets Epidemic: a Systematic Literature Review

Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment

The power of immersive technologies: a sociopsychological analysis of the relationship between immersive environments, storytelling, sentiment, and the impact on user experience

This dissertation initially focused on exploring the potential of immersive technologies for the distant future. However, the emergence of the COVID-19 virus in late 2019 disrupted the world, causing a pause in many areas. Nevertheless, the butterfly effect of the pandemic spurred the development of immersive technologies, resulting in the rise of the metaverse, web3, non-fungible tokens (NFT), and avatars, which are gaining increasing popularity. The excitement for the metaverse is growing in both academia and industry, leading to new avenues of research, digital marketing, video games, tourism, and social media. This dissertation explores this rapidly emerging technological revolution and its effects on user experience (UX)