Distance-based outliers method for detecting disease outbreaks using social media

2016 ◽  
Author(s):  
Xiangfeng Dai ◽  
Marwan Bikdash
Keyword(s):  
Social Media ◽  
Disease Outbreaks

scholarly journals Real-time clinician text feeds from electronic health records

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
James T. H. Teo ◽  
Vlad Dinu ◽  
William Bernal ◽  
Phil Davidson ◽  
Vitaliy Oliynyk ◽  
...  
Keyword(s):  
Social Media ◽  
Electronic Health Records ◽  
Real Time ◽  
Capacity Planning ◽  
Low Cost ◽  
Disease Outbreaks ◽  
Record System ◽  
Health Records ◽  
Keywords And Phrases ◽  
Electronic Health

AbstractAnalyses of search engine and social media feeds have been attempted for infectious disease outbreaks, but have been found to be susceptible to artefactual distortions from health scares or keyword spamming in social media or the public internet. We describe an approach using real-time aggregation of keywords and phrases of freetext from real-time clinician-generated documentation in electronic health records to produce a customisable real-time viral pneumonia signal providing up to 4 days warning for secondary care capacity planning. This low-cost approach is open-source, is locally customisable, is not dependent on any specific electronic health record system and can provide an ensemble of signals if deployed at multiple organisational scales.

scholarly journals Determinants of Infodemics During Disease Outbreaks: A Systematic Review

2021 ◽  
Vol 9 ◽  
Author(s):  
Javier Alvarez-Galvez ◽  
Victor Suarez-Lledo ◽  
Antonio Rojas-Garcia
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Health Promotion ◽  
Disease Outbreaks ◽  
Cochrane Library ◽  
Evidence Based ◽  
Healthy Habits ◽  
Health Related ◽  
Social Media Platforms ◽  
Use Of Social Media

Background: The widespread use of social media represents an unprecedented opportunity for health promotion. We have more information and evidence-based health related knowledge, for instance about healthy habits or possible risk behaviors. However, these tools also carry some disadvantages since they also open the door to new social and health risks, in particular during health emergencies. This systematic review aims to study the determinants of infodemics during disease outbreaks, drawing on both quantitative and qualitative methods.Methods: We searched research articles in PubMed, Scopus, Medline, Embase, CINAHL, Sociological abstracts, Cochrane Library, and Web of Science. Additional research works were included by searching bibliographies of electronically retrieved review articles.Results: Finally, 42 studies were included in the review. Five determinants of infodemics were identified: (1) information sources; (2) online communities' structure and consensus; (3) communication channels (i.e., mass media, social media, forums, and websites); (4) messages content (i.e., quality of information, sensationalism, etc.,); and (5) context (e.g., social consensus, health emergencies, public opinion, etc.). Studied selected in this systematic review identified different measures to combat misinformation during outbreaks.Conclusion: The clarity of the health promotion messages has been proven essential to prevent the spread of a particular disease and to avoid potential risks, but it is also fundamental to understand the network structure of social media platforms and the emergency context where misinformation might dynamically evolve. Therefore, in order to prevent future infodemics, special attention will need to be paid both to increase the visibility of evidence-based knowledge generated by health organizations and academia, and to detect the possible sources of mis/disinformation.

scholarly journals Using WeChat, a Chinese Social Media App, for Early Detection of the COVID-19 Outbreak in December 2019: Retrospective Study

10.2196/19589 ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. e19589
Author(s):  
Wenjun Wang ◽  
Yikai Wang ◽  
Xin Zhang ◽  
Xiaoli Jia ◽  
Yaping Li ◽  
...  
Keyword(s):  
Social Media ◽  
Early Detection ◽  
New Technologies ◽  
Disease Outbreaks ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Early Detection Of Disease ◽  
New Approach ◽  
Novel Coronavirus ◽  
Chinese Social Media

Background A novel coronavirus, SARS-CoV-2, was identified in December 2019, when the first cases were reported in Wuhan, China. The once-localized outbreak has since been declared a pandemic. As of April 24, 2020, there have been 2.7 million confirmed cases and nearly 200,000 deaths. Early warning systems using new technologies should be established to prevent or mitigate such events in the future. Objective This study aimed to explore the possibility of detecting the SARS-CoV-2 outbreak in 2019 using social media. Methods WeChat Index is a data service that shows how frequently a specific keyword appears in posts, subscriptions, and search over the last 90 days on WeChat, the most popular Chinese social media app. We plotted daily WeChat Index results for keywords related to SARS-CoV-2 from November 17, 2019, to February 14, 2020. Results WeChat Index hits for “Feidian” (which means severe acute respiratory syndrome in Chinese) stayed at low levels until 16 days ahead of the local authority’s outbreak announcement on December 31, 2019, when the index increased significantly. The WeChat Index values persisted at relatively high levels from December 15 to 29, 2019, and rose rapidly on December 30, 2019, the day before the announcement. The WeChat Index hits also spiked for the keywords “SARS,” “coronavirus,” “novel coronavirus,” “shortness of breath,” “dyspnea,” and “diarrhea,” but these terms were not as meaningful for the early detection of the outbreak as the term “Feidian”. Conclusions By using retrospective infoveillance data from the WeChat Index, the SARS-CoV-2 outbreak in December 2019 could have been detected about two weeks before the outbreak announcement. WeChat may offer a new approach for the early detection of disease outbreaks.

scholarly journals Social Media Use, Self-Efficacy, Perceived Threat, and Preventive Behavior in Times of COVID-19: Results of a Cross-Sectional Study in Pakistan

2021 ◽  
Vol 12 ◽  
Author(s):  
Qaisar Khalid Mahmood ◽  
Sara Rizvi Jafree ◽  
Sahifa Mukhtar ◽  
Florian Fischer
Keyword(s):  
Social Media ◽  
Self Efficacy ◽  
Media Use ◽  
Disease Outbreaks ◽  
Cross Sectional Study ◽  
Perceived Threat ◽  
Preventive Behavior ◽  
Cross Sectional Survey ◽  
Social Media Use ◽  
Cross Sectional

Although the role of social media in infectious disease outbreaks is receiving increasing attention, little is known about the mechanisms by which social media use affects risk perception and preventive behaviors during such outbreaks. This study aims to determine whether there are any relationships between social media use, preventive behavior, perceived threat of coronavirus, self-efficacy, and socio-demographic characteristics. The data were collected from 310 respondents across Pakistan using an online cross-sectional survey. Reliability analyses were performed for all scales and structural equational modeling was used to identify the relationships between study variables. We found that: (i) social media use predicts self-efficacy (β = 0.25, p < 0.05) and perceived threat of coronavirus (β = 0.54, p < 0.05, R2 = 0.06), and (ii) preventive behavior is predicted by self-efficacy and perceived threat of coronavirus (R = 0.10, p < 0.05). Therefore, these results indicate the importance of social media's influence on health-related behaviors. These findings are valuable for health administrators, governments, policymakers, and social scientists, specifically for individuals whose situations are similar to those in Pakistan.

scholarly journals A Framework for Forecasting Outbreak of Infectious Diseases Based on Climate Variability and Social Media Content

2021 ◽  
Vol 9 (5) ◽  
pp. 118-124
Author(s):  
Juliet Johny ◽  
Linda Sara Mathew
Keyword(s):  
Social Media ◽  
Infectious Diseases ◽  
Disease Incidence ◽  
Disease Outbreaks ◽  
Media Content ◽  
Data Generation ◽  
Real Time Analysis ◽  
Weather Observations ◽  
Proposed Model ◽  
Media Data

The amount of data has risen significantly over the last few years, due to the popularity of some of the data generation sources like social media, electronic health records, sensors and online shopping sites. Analyzing, processing and storing this data is very prominent since it helps to uncover hidden patterns and unknown correlations. A big data analysis and prediction System is proposed in this context, which combines weather observations, health data and social media content in order to forecast the outbreaks of infectious diseases in a locality. Finding information about the determinants of disease outbreaks are required to reduce its effects on populations. An In-mapper combiner based MapReduce algorithm is used to calculate the mean of daily measurements of various climate parameters like temperature, atmospheric pressure, relative humidity, solar and wind. The climatic parameter that may leads to the outbreak of a disease is identified by finding the correlation between the parameters and disease incidence count. To evaluate how user’s tweeting patterns and sentiments matched with the outbreak of diseases, all tweets containing keywords related to diseases are collected using twitter streaming APIs and are analyzed and processed using Spark framework. The performance of proposed model is improved due to the presence of tweet processing. This indicates that the real-time analysis of social media data can provide more effective result rather than working on the historical data.

scholarly journals Disease and Social Media in Post-Natural Disaster Recovery Philippines

2021 ◽  
Author(s):  
Lauren E. Charles ◽  
Courtney D. Corley
Keyword(s):  
Social Media ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Disease Surveillance ◽  
Disaster Recovery ◽  
Recovery Period ◽  
Disease Outbreaks ◽  
The Philippines ◽  
Surveillance Program ◽  
Multilinear Regression

AbstractIntroductionThe Philippines is plagued with natural disasters and resulting precipitating factors for disease outbreaks. The developing country has a strong disease surveillance program during and post-disaster phases; however, latent disease contracted during these emergency situations emerges once the Filipinos return to their homes. Coined the social media capital of the world, the Philippines provides an opportunity to evaluate the potential of social media use in disease surveillance during the post-recovery period. By developing and defining a non-traditional method for enhancing detection of infectious diseases post-natural disaster recovery in the Philippines, this research aims to increase the resilience of affected developing countries through advanced passive disease surveillance with minimal cost and high impact.MethodsWe collected 50 million geo-tagged tweets, weekly case counts for six diseases, and all natural disasters from the Philippines between 2012 and 2013. We compared the predictive capability of various disease lexicon-based time series models (e.g., Twitter’s BreakoutDetection, Autoregressive Integrated Moving Average with Explanatory Variable [ARIMAX], Multilinear regression, and Logistic regression) and document embeddings (Gensim’s Doc2Vec).ResultsThe analyses show that the use of only tweets to predict disease outbreaks in the Philippines has varying results depending on which technique is applied, the disease type, and location. Overall, the most consistent predictive results were from the ARIMAX model which showed the significance in tweet value for prediction and a role of disaster in specific instances.DiscussionOverall, the use of disease/sick lexicon-filtered tweets as a predictor of disease in the Philippines appears promising. Due to the consistent and large increase use of Twitter within the country, it would be informative to repeat analysis on more recent years to confirm the top method for prediction. In addition, we suggest that a combination disease-specific model would produce the best results. The model would be one where the case counts of a disease are updated periodically along with the continuous monitoring of lexicon-based tweets plus or minus the time from disaster.

scholarly journals Health Information Needs and Health Seeking Behavior during the 2014-2016 Ebola Outbreak: A Twitter Content Analysis

2017 ◽  
Author(s):  
Michelle L. Odlum ◽  
Sunmoo Yoon
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Health Information ◽  
Language Processing ◽  
Information Seeking ◽  
Information Needs ◽  
Disease Outbreaks ◽  
Time Data ◽  
Health Seeking ◽  
Over Time

AbstractIntroductionFor effective public communication during major disease outbreaks like the 2014-2016 Ebola epidemic, health information needs of the population must be adequately assessed. Through content analysis of social media data, like tweets, public health information needs can be effectively assessed and in turn provide appropriate health information to effectively address such needs. The aim of the current study was to assess health information needs about Ebola, at distinct epidemic time points, through longitudinal tracking.MethodsNatural language processing was applied to explore public response to Ebola over time from the beginning of the outbreak (July 2014) to six month post outbreak (March 2015). A total 155,647 tweets (unique 68,736, retweet 86,911) mentioning Ebola were analyzed and visualized with infographics.ResultsPublic fear, frustration, and health information seeking regarding Ebola-related global priorities were observed across time. Our longitudinal content analysis revealed that due to ongoing health information deficiencies, resulting in fear and frustration, social media was at times an impediment and not a vehicle to support health information needs.DiscussionContent analysis of tweets effectively assessed Ebola information needs. Our study also demonstrates the use of Twitter as a method for capturing real-time data to assess ongoing information needs, fear, and frustration over time.All authors have seen and approved the manuscript.

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