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Epidémiosurveillance et médias sociaux

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MEDRXIV 01/09/20 Social Network Analysis of COVID-19 Transmission in Karnataka, India. EUROSURVEILLANCE 12/03/20 Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Prevention & Infection Control VIA YOUTUBE 11/09/19 ICPIC 2019 : Social media to support prevention measures Program: - Social media posts as early-warning system for MDRO outbreaks - Researcher perspective on Pros and Cons of social media - Do YouTube v. VENTUREBEAT 23/05/19 Chick-fil-A’s AI can spot signs of foodborne illness from social media posts with 78% accuracy. Information Processing & Management Available online 1 June 2018 Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification. Abadi, Barham, Chen, Chen, Davis, Dean, et al.

Information Processing & Management Available online 1 June 2018 Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., et al. (2016). Tensorflow: A system for large-scale machine learning. In Osdi (pp. 265–283). (vol. 16). Abdelhaq, Sengstock, Gertz, 2013 H. Proceedings of the VLDB Endowment, 6 (12) (2013), pp. 1326-1329 Aggarwal, Subbian, 2012 C.C. Sdm, 12, SIAM (2012), pp. 624-635 Aho, Corasick, 1975 A.V. ARXIV 27/11/18 Predicting the Flu from Instagram. Theoretical Biology and Medical Modelling December 2018, A review of influenza detection and prediction through social networking sites. Public health is an important issue.

Theoretical Biology and Medical Modelling December 2018, A review of influenza detection and prediction through social networking sites

Health care providers should be updated about the public health and disease outbreaks affecting their communities in order to make correct decisions at the right time. This would help them offer better services in an efficient way and at the perfect time. Most of the health care providers depend on the Center of Disease Control and Prevention (CDC) to be informed about disease outbreaks or to be notified about the flu season. The Center of Disease Control and Prevention (CDC) is a trusted department in the United States. It publishes weekly disease related reports. GEORGIA STATE UNIVERSITY via SCIENCE DAILY 19/01/17 Study uses social media, internet to forecast disease outbreaks. Journal of the American Medical Informatics Association, 10/01/18 Discovering foodborne illness in online restaurant reviews. Skip to Main Content Sign In Register Close Advanced Search Journals Career Network Online ISSN 1527-974X Print ISSN 1067-5027 Copyright © 2018 American Medical Informatics Association Connect Resources.

Journal of the American Medical Informatics Association, 10/01/18 Discovering foodborne illness in online restaurant reviews

UNIVERSITY OF NEVADA - MAY 2017 - Thèse en ligne : Determining the effects of social media monitoring to identify potential foodborne illness in Southern Nevada. ARIZONA INFECTIOUS DISEASES TRAINING 19/07/17 Présentation : Improving Foodborne Complaint and Outbreak Detection Using Social Media, New York City. Healthc Inform Res. 2017 Oct; 23(4): 343–348. Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks. PLOS 07/12/16 Social Media as a Sentinel for Disease Surveillance: What Does Sociodemographic Status Have to Do with It?

Dengue is a mosquito-borne disease transmitted between humans by infected Aedes mosquitoes1 and is a major cause of illness and death in many tropical and subtropical regions2.

PLOS 07/12/16 Social Media as a Sentinel for Disease Surveillance: What Does Sociodemographic Status Have to Do with It?

Despite improvements in disease surveillance and investments in mosquito control programs, dengue remains a major public health threat in many countries3,4,5,6. Efforts at improving surveillance have explored non-traditional data sources, including, crowd-generated approaches using mobile phones and social media7,8, and Internet search query data9,10,11.

These systems have the potential to capture mild infections not requiring medical attention, and enable the ascertainment of the probable temporal and spatial distribution of cases prior to official reports of disease. To extract major features distinguishing irrelevant and relevant (i.e. suspected dengue disease) tweets, we considered emojis, location information (state, county and micro-region), unigrams, bigrams and trigrams. Sociodemographic Analysis John S. CCTS VIA YOUTUBE 23/03/16 The Future of Disease Monitoring: Wearable Biosensors, Social Media, and Smartphone Applications. American Journal of Infection Control Volume 44, Issue 12, 1 December 2016, Ebola virus disease and social media: A systematic review. Veille signaux faibles via GOOGLE TRENDS et TWITTER. WASHINGTON UNIVERSITY IN ST LOUIS - FEV 2016 - Présentation : Using Social Media to Detect Potential Foodborne Outbreaks.

RISQUES_GOUV_FR - 2016 - Utiliser les médias sociaux en situation d'urgence #MSGU Bonnes pratiques numériques en situation d’urgence. BRGM 17/11/16 Observatoire citoyen des risques naturels : collecter, informer et prévenir grâce aux réseaux sociaux. ISPRS 19/08/16 Use of Social Media for the Detection and Analysis of Infectious Diseases in China. 1.

ISPRS 19/08/16 Use of Social Media for the Detection and Analysis of Infectious Diseases in China

Introduction Since 2013, 2.35 million cases of dengue fever have been reported in the Americas, and 37,687 of these dengue fever cases were considered to be severe [1]. Identifying the geographical ranges helps the public understand the risk posed by infectious disease outbreaks [2]. Early detection of disease activity, followed by a rapid response, can largely reduce the impact of both seasonal and pandemic influenza [3]. Social media analytics enable the possibility of infectious disease surveillance at a fine scale and in a timely manner [2]. The geographical context of health research has shifted from a data-scarce to a data-rich environment [5].

Scholars have begun to analyze the relationship between social media and public health [2]. Internet-based surveillance systems and search engines provide a novel approach to monitoring public health issues [3,19]. The methods used for data acquisition and topic extraction are introduced in the following section. UNIVERSITY OF APPLIED SCIENCE BONN-RHEIN-SIEG - 2015 - Web Data Mining and Social Media Analysis for better Communication in Food Safety Cris.

FOOD SAFETY MAGAZINE - OCT 2014 - Emerging Risk Identification in the Age of Social Media. Signature Series | October 2014 By Adrienne M.

FOOD SAFETY MAGAZINE - OCT 2014 - Emerging Risk Identification in the Age of Social Media

Dunham and Forrest M. Hillery In recent years, a recurring discussion within the food industry concerning the use of a processed ground beef product became national news. ASPC 03/09/15 Les données massives et le Réseau mondial d'information en santé publique (RMISP) Pour partager cette page, veuillez cliquez sur le réseau sociale de votre choix.

ASPC 03/09/15 Les données massives et le Réseau mondial d'information en santé publique (RMISP)

ASPC 12/09/14 Utilisation des technologies numériques en santé publique. Pour partager cette page, veuillez cliquez sur le réseau sociale de votre choix.

ASPC 12/09/14 Utilisation des technologies numériques en santé publique

THE POLICY AND INTERNET BLOG 14/10/13 Can Twitter provide an early warning function for the next pandemic? 14 October 2013 With factors such as air travel act as a catalyst in the spread of new and novel viruses, the need to improve global population monitoring and enhance surveillance of infectious diseases is more pressing than ever.

THE POLICY AND INTERNET BLOG 14/10/13 Can Twitter provide an early warning function for the next pandemic?

Patty Kostkova (UCL) discusses how the real-time streams of user data generated on social networks like Twitter can be used for monitoring the health of large populations, providing a potential early warning function for pandemics, detecting flu spikes weeks before official surveillance systems. Watch Patty talk on this subject at the OII. Communication of risk in any public health emergency is a complex task for healthcare agencies; a task made more challenging when citizens are bombarded with online information.

Mexico City, 2009. Ed: Could you briefly outline your study? International Journal for Parasitology: Parasites and Wildlife Volume 2, December 2013, Networks and the ecology of parasite transmission: A framework for wildlife parasitology. Highlights Animal behaviour can generate heterogeneities in parasite transmission.

International Journal for Parasitology: Parasites and Wildlife Volume 2, December 2013, Networks and the ecology of parasite transmission: A framework for wildlife parasitology

Network models represent these heterogeneities as links (edges) among hosts (nodes). Journal of Biomedical Informatics Volume 54, April 2015, Utilizing social media data for pharmacovigilance: A review. Fig. 1 Sample search queries used for article retrieval from Medline. Fig. 2 A framework for ADR detection and extraction from social media data. Highlights •We present a review of pharmacovigilance techniques from social media (SM) data. Abstract Objective Automatic monitoring of Adverse Drug Reactions (ADRs), defined as adverse patient outcomes caused by medications, is a challenging research problem that is currently receiving significant attention from the medical informatics community.

Methods. Preventive Medicine Volume 67, October 2014 Online reports of foodborne illness capture foods implicated in official foodborne outbreak reports. CDC MMWR 15/08/14 Health Department Use of Social Media to Identify Foodborne Illness — Chicago, Illinois, 2013–2014. August 15, 2014 / 63(32);681-685 Jenine K. Harris, PhD1, Raed Mansour, MS2, Bechara Choucair, MD2, Joe Olson3, Cory Nissen, MS3, Jay Bhatt, DO2 (Author affiliations at end of text) An estimated 55 million to 105 million persons in the United States experience acute gastroenteritis caused by foodborne illness each year, resulting in costs of $2–$4 billion annually (1). NORTHWESTERN UNIVERSITY, EVANSTON - AOUT 2013 - Detecting and Tracking Disease Outbreaks by Mining Social Media Data.

PEDIATRICS 05/05/14 Social Media Methods for Studying Rare Diseases. Epidemics Volume 10, March 2015 Eight challenges for network epidemic models. Open Access Highlights Modelling transmission through networks is challenging. We overview most of the outstanding open problems. We identify topics ranging from theoretical to applied ends of modelling spectrum. Abstract Networks offer a fertile framework for studying the spread of infection in human and animal populations.

Keywords Infectious disease models; Transmission dynamics; Contact networks; Random graphs; Dynamic networks; Control measures. BLOG BMJ 05/01/15 Social media during epidemics: a poisoned chalice? 5 Jan, 15 | by Claire Bower, Digital Comms Manager, @clairebower Social networking is now the most popular online activity worldwide. Social networking sites account for nearly 1 in every 5 minutes spent online globally, reaching 82 percent of the world’s Internet population.

As such, sites such as Facebook and Twitter have become an unavoidable part of crisis communication. However, opinion is mixed as to whether this pervasiveness is a blessing or a curse to organisations charged with protecting public health. MEDPAGETODAY 02/04/14 Social Media and Disease: There's an App for That. Can social media help detect and track infectious disease outbreaks? Well, there's an app for that. In fact, there are several -- and more on the way. Call it the crowd-sourcing of epidemiology. "We're putting the public into public health," says Mark Smolinski, MD, director of global health threats for the San Francisco-based Skoll Global Threats Fund. Smolinski was talking about Flu Near You, his organization's Web- and smartphone-based app that asks people to report their flu symptoms -- or lack thereof -- on a weekly basis. But in an age of constant online chatter, he could have been talking about a host of projects that aim to cull useful information from all the gossip.

Take, for instance, Sina Weibo, the Chinese microblogging site. And researchers at the University of Rochester in New York are investigating whether Twitter feeds can actually predict if a person will get the flu. American Journal of Public Health - JULY 2010 - The Next Public Health Revolution: Public Health Information Fusion and Social Networks. JOURNAL OF MEDICAL INTERNET RESEARCH - 2013 - Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation.

Introduction Social media and search behavior produce vast new data sources of largely untapped scientific potential. The threat of a global pandemic posed by outbreaks of influenza H5N1 (1997) and Severe Acute Respiratory Syndrome (SARS, 2002), both diseases of zoonotic origin, provoked interest in improving early warning systems and reinforced the need for combining data from different sources. It led to novel ideas, for example, the use of search query data from search engines such as Google [,] and Yahoo! [] as an indicator of when and where influenza was occurring.

This methodology has subsequently been extended to other diseases and has led to experimentation with new types of social media for disease surveillance as they have become available. Methods. PLOS 02/06/14 The Contribution of Social Networks to the Health and Self-Management of Patients with Long-Term Conditions: A Longitudinal Study. Abstract Evidence for the effectiveness of patient education programmes in changing individual self-management behaviour is equivocal.

More distal elements of personal social relationships and the availability of social capital at the community level may be key to the mobilisation of resources needed for long-term condition self-management to be effective. Aim. NATURE 13/08/14 Online collaboration: Scientists and the social network. In 2011, Emmanuel Nnaemeka Nnadi needed help to sequence some drug-resistant fungal pathogens. A PhD student studying microbiology in Nigeria, he did not have the expertise and equipment he needed. PLOS 05/10/15 Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review. CDC EID – NOV 2015 – Au sommaire notamment: Use of Internet Search Queries to Enhance Surveillance of Foodborne Illness ; Gyung Jin Bahk , Yong Soo Kim, and Myoung Su Park Author affiliations: Kunsan National University, Gunsan, South Korea (G.J. INRA 30/07/15 Un tweet, un post, ça vous parle ? A l’heure où les réseaux sociaux prennent une place de plus en plus importante dans la société, la recherche n’est pas en reste !

BLOG PLOS 29/01/15 Researchers Changing the Way We Respond to Epidemics with Wikipedia and Twitter. “A global disease-forecasting system will change the way we respond to epidemics.” Papers in Physics, - 2013 - Epidemics on social networks. PLOS 30/10/14 Internet and Free Press Are Associated with Reduced Lags in Global Outbreak Reporting. Background: Global outbreak detection and reporting have generally improved for a variety of infectious diseases and geographic regions in recent decades. Nevertheless, lags in outbreak reporting remain a threat to the global human health and economy. In the time between first occurrence of a novel disease incident and public notification of an outbreak, infected individuals have a greater possibility of traveling and spreading the pathogen to other nations.

Shortening outbreak reporting lags has the potential to improve global health by preventing local outbreaks from escalating into global epidemics. Methods: Reporting lags between the first record and the first public report of an event were calculated for 318 outbreaks occurring 1996-2009. Am. J. Epidemiol. (2013) 178 (11): The Impact of Illness on Social Networks: Implications for Transmission and Control of Influe. CDC 25/11/13 CDC Competition Encourages Use of Social Media to Predict Flu. November 25, 2013 — CDC has launched the “Predict the Influenza Season Challenge,” a competition designed to foster innovation in flu activity modeling and prediction. UN GLOBAL PULSE 20/05/14 Can We Use Social Media For Disease Surveillance? Skip to main content. INTERNET RESEARCH - 2013 - The power of prediction with social media.

ARXIV 11/09/13 Containing epidemic outbreaks by message-passing techniques. CSIRO 10/10/12 Emergency Situation Awareness tool for social media. European Journal of Epidemiology (2006) 21: 103–111 Feasibility of using web-based questionnaires in large population-based epid. PLOS 18/07/11 Community Structure in Social Networks: Applications for Epidemiological Modelling. J Health Med Inform 2012, 3:4 Mining Social Media and Web Searches for Disease Detection.

Interdisciplinary Perspectives on Infectious Diseases Volume 2011 (2011) Pathogens, Social Networks, and the Paradox of Transmis. CONSOMMATEURS (perception des risques, baromètres) Réseaux sociaux et alimentation. EPJ Data Science 2013, 2:4 The Dynamics of Health Behavior Sentiments on a Large Online Social Network. PLOS 22/10/14 The Use of Google Trends in Health Care Research: A Systematic Review.