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A latent shared-component generative model for real-time disease surveillance using Twitter data
[article]
2015
arXiv
pre-print
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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
2018
Information Processing & Management
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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
2016
PLoS ONE
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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]
2011
Lecture Notes in Computer Science
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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
2018
Theoretical Biology and Medical Modelling
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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
2021
Sensors
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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
2019
Scientific Reports
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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]
2020
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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
2020
Big Data Mining and Analytics
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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
2018
Online Journal of Public Health Informatics
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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
2020
Stochastic environmental research and risk assessment (Print)
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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
2020
International Journal of Environmental Research and Public Health
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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]
2017
arXiv
pre-print
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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
2019
PLoS ONE
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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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