Twitter: a complementary tool to monitor seasonal influenza epidemic in France?

Social media as Twitter are used today by people to disseminate health information but also to share or exchange on their health. Based on this observation, recent studies showed that Twitter data can be used to monitor trends of infectious diseases such as influenza. These studies were mainly carried out in United States where Twitter is very popular1-4. In our knowledge, no research has been implemented in France to know whether Twitter data can be a complementary data source to monitor seasonal influenza epidemic.

June 18, 2019

Understanding Socio-Cultural Factors related to Obesity: Sentiment Analysis on related Tweets

Obesity can lead to the death of at least 2.8 million people each year1, yet the rate of obesity around the world has continuously increased over the past 30 years1. Societal changes, including increased food consumption and decreased physical activity, have been determined as two of the main drivers behind the current obesity pandemic2.

June 18, 2019

Correlation of Tweets Mentioning Influenza Illness and Traditional Surveillance Data

The use of social media as a syndromic sentinel for diseases is an emerging field of growing relevance as the public begins to share more online, particularly in the area of influenza. Several applications have been developed to predict or monitor influenza activity using publicly posted or self-reported online data; however, few have prioritized accuracy at the local level. In 2016, the Cook County Department of Public Health (CCDPH) collected localized Twitter information to evaluate its utility as a potential influenza sentinel data source.

January 21, 2018

Semantic Analysis of Open Source Data for Syndromic Surveillance

Social media messages are often short, informal, and ungrammatical. They frequently involve text, images, audio, or video, which makes the identification of useful information difficult. This complexity reduces the efficacy of standard information extraction techniques1. However, recent advances in NLP, especially methods tailored to social media2, have shown promise in improving real-time PH surveillance and emergency response3. Surveillance data derived from semantic analysis combined with traditional surveillance processes has potential to improve event detection and characterization.

August 10, 2017

Using a Bayesian Method to Assess Google, Twitter, and Wikipedia for ILI Surveillance

Traditional influenza surveillance relies on reports of influenzalike illness (ILI) by healthcare providers, capturing individuals who seek medical care and missing those who may search, post, and tweet about their illnesses instead. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia for influenza surveillance, but with conflicting findings, studies have only evaluated these web-based sources individually or dually without comparing all three of them1-5.

August 22, 2017

Situational Awareness of Health Events Using Social Media and the SMART Dashboard

 Numerous methods using social media for syndromic surveillance and disease tracking have been developed. Many websites use Twitter and other social media to track specific diseases or syndromes.1 Many are intended for public use and the extent of use by public health agencies is limited.2 Our work builds on 4 years of experience by our multi-disciplinary team3 with a focus on local surveillance of influenza. 4,5


September 18, 2017

Social Media Analytics for Post-Disasters Disease Detection in the Philippines

Previous research identifies social media as an informal source of near-real time health data that may add value to disease surveillance systems by providing broader access to health data across hard-toreach populations. This indirect health monitoring may improve public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. The Philippines consists of over 7,000 islands and is prone to meteorological (storms), hydrological (floods), and geophysical disasters (earthquakes and volcanoes).

September 18, 2017

ChatterGrabber: A Lightweight Easy to Use Social Media Surveillance Toolkit

Despite numerous successes in using social media to detect food borne illness and to predict influenza trends, the use of social media as a public health tool has yet to gain widespread adoption. While social media data cannot directly diagnose illness, aggregate trends in symptom proliferation may readily be observed. Such trends may allow a health agency to watch for signs and symptoms related to target conditions within its jurisdiction.

October 10, 2017

Using Twitter to Detect and Investigate Disease Outbreaks

Social media is of considerable interest as a sensor into the thoughts, interests and health of a population. We consider three types of health events that an analyst may wish to be made aware of:

- Given a known disease, such as MERS, SARS, Measles, etc., an event corresponds to individuals contracting the disease.

- Given a set of symptoms (fever, stomach pain, etc.), an event is an unusual number of individuals1 complaining of the symptoms.

December 29, 2017

Content Analysis of Tobacco-related Twitter Posts

Vast amounts of free, real-time, localizable Twitter data offer new possibilities for public health workers to identify trends and attitudes that more traditional surveillance methods may not capture, particu- larly in emerging areas of public health concern where reliable sta- tistical evidence is not readily accessible. Existing applications include tracking public informedness during disease outbreaks. Twitter-based surveillance is particularly suited to new challenges in tobacco control.

January 12, 2018


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