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A latent shared-component generative model for real-time disease surveillance using Twitter data [article]

Roberto C.S.N.P. Souza, Denise E.F de Brito, Renato M. Assunção, Wagner Meira Jr
2015 arXiv   pre-print
We develop a generative simple yet effective model to connect the fluctuations of disease cases and disease-related Twitter posts.  ...  Using data from a significant number of large Brazilian towns, we demonstrate empirically that our model is able to predict well the next weeks of the disease counts using the tweets and disease cases  ...  The authors would like to thank INWeb, CNPq, CAPES and Fapemig for financial support.  ... 
arXiv:1510.05981v1 fatcat:mhook6x3xzbpffhmlhjzh4kvxu

Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification

Ovidiu Șerban, Nicholas Thapen, Brendan Maginnis, Chris Hankin, Virginia Foot
2018 Information Processing & Management  
Interest in real-time syndromic surveillance based on social media data has greatly increased in recent years.  ...  The Nowcasting module shows that using social media data can improve prediction for multiple diseases over simply using traditional data sources.  ...  Train a LASSO state-specific model using this data. • Now use this model to predict the CDC count for t n using the Twitter counts for t n as regressors.  ... 
doi:10.1016/j.ipm.2018.04.011 fatcat:seorwli2ovd2bj5v4eoadkxcpa

Avian Influenza Risk Surveillance in North America with Online Media

Colin Robertson, Lauren Yee, Dena L. Schanzer
2016 PLoS ONE  
In this paper we investigated the use of one social media outlet, Twitter, for surveillance of avian influenza risk in North America.  ...  The use of Internet-based sources of information for health surveillance applications has increased in recent years, as a greater share of social and media activity happens through online channels.  ...  Utilizing new technologies such as Twitter along with current surveillance systems may allow for near real-time risk surveillance of the information landscape associated with real-world outbreak events  ... 
doi:10.1371/journal.pone.0165688 pmid:27880777 pmcid:PMC5120807 fatcat:vps4znxlezbbna32xdptaih2na

Identifying Health-Related Topics on Twitter [chapter]

Kyle W. Prier, Matthew S. Smith, Christophe Giraud-Carrier, Carl L. Hanson
2011 Lecture Notes in Computer Science  
The methods used in this paper provide a possible toolset for public health researchers and practitioners to better understand public health problems through large datasets of conversational data.  ...  Tobacco use is chosen as a test case to demonstrate the effectiveness of topic modeling via LDA across a large, representational dataset from the United States, as well as across a smaller subset that  ...  The most likely topic components for each of the five topics generated by LDA.  ... 
doi:10.1007/978-3-642-19656-0_4 fatcat:juk7cpl2l5eytci5tooou326jy

A review of influenza detection and prediction through social networking sites

Ali Alessa, Miad Faezipour
2018 Theoretical Biology and Medical Modelling  
Nowadays, the web can be used for surveillance of diseases.  ...  Therefore, SNS provides an efficient resource for disease surveillance and a good way to communicate to prevent disease outbreaks.  ...  Availability of data and materials Not applicable.  ... 
doi:10.1186/s12976-017-0074-5 pmid:29386017 pmcid:PMC5793414 fatcat:ldmhvmhhy5ehjhkhlq3kcqvk4u

Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme

Venkatachalam Kandasamy, Pavel Trojovský, Fadi Al Machot, Kyandoghere Kyamakya, Nebojsa Bacanin, Sameh Askar, Mohamed Abouhawwash
2021 Sensors  
Our experimental results have been obtained from a comprehensive evaluation involving a dataset extracted from open-source data available from Twitter that were filtered by using the keywords "covid",  ...  The basic training data have been collected from Twitter posts.  ...  Exhibiting a Model in Online Mode The model in online sentiment and latent semantic pipeline component prediction aims for tweet prediction related to coronavirus in real time and implement the model to  ... 
doi:10.3390/s21227582 pmid:34833656 pmcid:PMC8623208 fatcat:b6nydytehfh2jgkp6t35bcqh5i

The Assessment of Twitter's Potential for Outbreak Detection: Avian Influenza Case Study

Samira Yousefinaghani, Rozita Dara, Zvonimir Poljak, Theresa M. Bernardo, Shayan Sharif
2019 Scientific Reports  
In this study, a Twitter-based data analysis framework was developed to automatically monitor avian influenza outbreaks in a real-time manner.  ...  Social media services such as Twitter are valuable sources of information for surveillance systems.  ...  Social media posts: The Twitter data-collector component used a crawler, i.e. a PHP script, to visit Twitter every minute.  ... 
doi:10.1038/s41598-019-54388-4 pmid:31796768 pmcid:PMC6890696 fatcat:poqiovzkc5bvveifbh3zkfwvue

An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time [article]

Nicole E. Kogan, Leonardo Clemente, Parker Liautaud, Justin Kaashoek, Nicholas B. Link, Andre T. Nguyen, Fred S. Lu, Peter Huybers, Bernd Resch, Clemens Havas, Andreas Petutschnig, Jessica Davis (+5 others)
2020 arXiv   pre-print
We estimate the timing of sharp changes in each data stream using a simple Bayesian model that calculates in near real-time the probability of exponential growth or decay.  ...  Finally, we propose a combined indicator for exponential growth in multiple data streams that may aid in developing an early warning system for future COVID-19 outbreaks.  ...  Figure 74 : Downtrend region, in red, for ILI  ... 
arXiv:2007.00756v2 fatcat:wi5a2sq3u5djhmi3tzboyx7r4u

Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective [article]

Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cecile Paris, C Raina MacIntyre
2019 arXiv   pre-print
Epidemic intelligence deals with the detection of disease outbreaks using formal (such as hospital records) and informal sources (such as user-generated text on the web) of information.  ...  In this survey, we discuss approaches for epidemic intelligence that use textual datasets, referring to it as 'text-based epidemic intelligence'.  ...  The model includes a latent label each for: (a) switching between general or health-related words; (b) identifying background words; and (c) an ailment.  ... 
arXiv:1903.05801v1 fatcat:ga75672fcfdtzggj6gqhvc6ule

Survey on data analysis in social media: A practical application aspect

Qixuan Hou, Meng Han, Zhipeng Cai
2020 Big Data Mining and Analytics  
We outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques, such as topic analysis, time series analysis, sentiment analysis,  ...  It serves as a critical information source with large volumes, high velocity, and a wide variety of data.  ...  It is not a challenge to collect a large number of data from social media platforms. With real-time streaming service, it is also achievable to stream live data for real-time event detection.  ... 
doi:10.26599/bdma.2020.9020006 fatcat:msf6yz7tozbdne2mutwepo2ujy

Design Choices for Automated Disease Surveillance in the Social Web

Mark Abraham Magumba, Peter Nabende, Ernest Mwebaze
2018 Online Journal of Public Health Informatics  
The utility of these developments to public health use cases like disease surveillance, information dissemination, outbreak prediction and so forth has been widely investigated and variously demonstrated  ...  surveillance.  ...  by automated data acquisition and generation of statistical alerts, monitor disease indicators in real-time or near real-time to detect outbreaks of disease earlier than would otherwise be possible with  ... 
doi:10.5210/ojphi.v10i2.9312 pmid:30349632 pmcid:PMC6194101 fatcat:l4zzy3yrqffftebpc4pcuq7szu

A spatio-temporal hierarchical Markov switching model for the early detection of influenza outbreaks

Rubén Amorós, David Conesa, Antonio López-Quílez, Miguel-Angel Martinez-Beneito
2020 Stochastic environmental research and risk assessment (Print)  
The use of Hidden Markov chains in temporal models has shown to be great tools for classifying the epidemic or endemic state of influenza data, though their use in spatio-temporal models for outbreak detection  ...  In this work, we present a spatio-temporal Bayesian Markov switching model over the differentiated incidence rates for the rapid detection of influenza outbreaks.  ...  (2012) extended this to the spatio-temporal surveillance of several diseases by means of a shared component model.  ... 
doi:10.1007/s00477-020-01773-5 fatcat:ejfjcwn7bfejje7geqbfu6zipq

Topic Modeling and User Network Analysis on Twitter during World Lupus Awareness Day

Salvatore Pirri, Valentina Lorenzoni, Gianni Andreozzi, Marta Mosca, Giuseppe Turchetti
2020 International Journal of Environmental Research and Public Health  
When supporting patients with such a complex disease, sharing information through social media can play an important role in creating better healthcare services.  ...  Therefore, this study identifies hidden information for healthcare decision-makers and provides a detailed model of the implications for healthcare organizations to detect, understand, and define hidden  ...  Twitter account name.  ... 
doi:10.3390/ijerph17155440 pmid:32731600 pmcid:PMC7432829 fatcat:2xn5jdsjfnb6hgs5ibwhnpp2ky

Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise [article]

George Shaw Jr., Amir Karami
2017 arXiv   pre-print
This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-health related concerns in  ...  Our proposed framework uses two text mining methods, sentiment analysis and topic modeling, to discover negative topics.  ...  This research has two specific limitations: time and space. The time limitation is that the data were collected during a one-month time period, June 1 -June 30 2016.  ... 
arXiv:1709.07915v1 fatcat:oqbtnqtjbjhrna7hbpkmgpxqky

Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance

Oduwa Edo-Osagie, Gillian Smith, Iain Lake, Obaghe Edeghere, Beatriz De La Iglesia, Olalekan Uthman
2019 PLoS ONE  
We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of  ...  Finally, we found some correlation (r = 0.414, p = 0.0004) between the Twitter signal generated with the semi-supervised system and data from consultations for related health conditions.  ...  Acknowledgments We acknowledge support from NHS 111 and NHS Digital for their assistance with the NHS 111 system; Out-of-Hours providers submitting data to the GPOOH syndromic surveillance and Advanced  ... 
doi:10.1371/journal.pone.0210689 pmid:31318885 pmcid:PMC6638773 fatcat:2kqtjnjrjvcdxcfqaur6765imu
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