scholarly journals ‘Watch the Flu’: A Tweet Monitoring Tool for Epidemic Intelligence of Influenza in Australia

2020 ◽  
Vol 34 (09) ◽  
pp. 13616-13617
Author(s):  
Brian Jin ◽  
Aditya Joshi ◽  
Ross Sparks ◽  
Stephen Wan ◽  
Cécile Paris ◽  
...  
Keyword(s):  
Public Health ◽  
Language Processing ◽  
Visual Analysis ◽  
Disease Outbreaks ◽  
Monitoring Tool ◽  
Time Data ◽  
Web Based ◽  
Data Pipeline ◽  
Epidemic Intelligence ◽  
Health Experts

‘Watch The Flu’ is a tool that monitors tweets posted in Australia for symptoms of influenza. The tool is a unique combination of two areas of artificial intelligence: natural language processing and time series monitoring, in order to assist public health surveillance. Using a real-time data pipeline, it deploys a web-based dashboard for visual analysis, and sends out emails to a set of users when an outbreak is detected. We expect that the tool will assist public health experts with their decision-making for disease outbreaks, by providing them insights from social media.

scholarly journals Update on the CDC National Syndromic Surveillance Program

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Paula Yoon ◽  
Michael Coletta
Keyword(s):  
Public Health ◽  
Emergency Preparedness ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
Disease Outbreaks ◽  
Surveillance Program ◽  
Public Health Importance ◽  
Web Based ◽  
National Public Health ◽  
National Emergency

During the past decade, BioSense meant different things to different people. When BioSense was created to support national emergency preparedness, it was a Web-based software for collecting emergency department data for detecting and monitoring syndromes of public health importance. BioSense has evolved to become part of CDC's new National Syndromic Surveillance Program. This collaboration among local, state, and national public health programs will help improve local and nation-wide situational awareness and response to hazardous events and disease outbreaks. NSSP presents modernized technology and a broadened vision that includes people, partners, policies, information systems, standards, and resources. Join to learn more.

scholarly journals Web-based applications for response to acute infectious disease outbreaks in the community: a systematic review (Preprint)

2020 ◽  
Author(s):  
Emma Quinn ◽  
Kai Hsun Hsiao ◽  
Isis Maitland-Scott ◽  
Maria Gomez ◽  
Melissa T Baysari ◽  
...  
Keyword(s):  
Public Health ◽  
Information Exchange ◽  
Keyword Search ◽  
Communicable Disease ◽  
Evaluation Studies ◽  
Disease Outbreaks ◽  
Outbreak Detection ◽  
Public Health Action ◽  
Web Based ◽  
Early Detection Of Disease

BACKGROUND Web-based technology has dramatically improved our ability to detect communicable disease outbreaks, with the potential to reduce morbidity and mortality due to swift public health action. Applications accessible through the internet and on mobile devices create an opportunity to enhance our traditional indicator-based surveillance systems, which have high specificity but issues with timeliness. OBJECTIVE We sought to describe the literature on web-based apps for indicator-based surveillance and response to acute communicable disease outbreaks in the community, in regards to their design, implementation and evaluation. METHODS We conducted a systematic search of the published literature across four databases (Medline via OVID, via OVID, Web of Science Core Collection, ProQuest Science and Google Scholar) for peer-reviewed journal articles from January 1998 to October 2019 using a keyword search. Articles with full text available were extracted for review, and exclusion criteria applied to identify eligible articles. RESULTS From 6649 retrieved articles, a total of 23 remained, describing 15 web-apps. Apps were primarily designed to improve the early detection of disease outbreaks, targeted government settings, and comprised complex algorithmic and/or statistical outbreak detection mechanisms. We identified a need for these apps to have more features to support secure information exchange and outbreak response actions, with a focus on outbreak verification processes and staff and resources to support app operations. Evaluation studies (6/15 apps) were mostly cross-sectional with some evidence of reduction to time to notification of outbreak, but studies were lacking user-based needs assessments and evaluation of implementation. CONCLUSIONS Public health officials designing new or improving existing disease outbreak web apps should ensure that outbreak detection is automatic and signals are verified by users, the app is easy to use, and that staff and resources are available to support the operations of the app, as well as conduct rigorous and holistic evaluations. CLINICALTRIAL

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.

scholarly journals “Koman i lé” : An Online Self-Reported Symptoms Surveillance System in Reunion Island

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Nadège Marguerite ◽  
Pascal Vilain ◽  
Etienne Sévin ◽  
Farid Sahridji ◽  
Laurent Filleul
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Disease Outbreaks ◽  
Reunion Island ◽  
Web Based ◽  
Health Authorities ◽  
Major Outbreak ◽  
Policy Information ◽  
Réunion Island ◽  
Sentinel Population

In Reunion Island, the population is very sensitive to public health concerns. In this context, the health authorities implemented since April 2014 a web-based surveillance system, called “Koman i lé” and based on a volunteers' cohort in general population. This surveillance system allowed to follow the seasonal influenza epidemic in 2014 and the major outbreak of conjunctivitis from January to April 2015. In conclusion, the sentinel population allows the population of Reunion Island to take an active part in the health regional policy. Information reported by individuals can increase traditional public health methods for more timely detection of disease outbreaks.

scholarly journals Internet surveillance systems for early alerting of health threats

2009 ◽  
Vol 14 (13) ◽  
Author(s):  
J P Linge ◽  
R Steinberger ◽  
T P Weber ◽  
R Yangarber ◽  
E van der Goot ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance Systems ◽  
Web Based ◽  
Information Tools ◽  
Health Authorities ◽  
Epidemic Intelligence ◽  
National Public Health ◽  
Public Health Authorities ◽  
Comprehensive Picture ◽  
Official Sources

In order to gather a comprehensive picture of potential epidemic threats, public health authorities increasingly rely on systems that perform epidemic intelligence (EI). EI makes use of information that originates from official sources such as national public health surveillance systems as well as from informal sources such as electronic media and web-based information tools.

scholarly journals Analytics for Investigation of Disease Outbreaks (AIDO)

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Ashlynn Daughton ◽  
Maneesha Chitanvis ◽  
Nileena Velappan ◽  
Forest M Altherr ◽  
Geoffery Fairchild ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Situational Awareness ◽  
Contextual Information ◽  
National Laboratory ◽  
Disease Outbreaks ◽  
Disease Manifestation ◽  
Data Set ◽  
Web Based ◽  
Public Health Decision

Objective: Analytics for the Investigation of Disease Outbreaks (AIDO) is a web-based tool designed to enhance a user’s understanding of unfolding infectious disease events. A representative library of over 650 outbreaks across a wide selection of diseases allows similar outbreaks to be matched to the conditions entered by the user. These historic outbreaks contain detailed information on how the disease progressed as well as what measures were implemented to control its spread, allowing for a better understanding within the context of other outbreaks.Introduction: Situational awareness, or the understanding of elemental components of an event with respect to both time and space, is critical for public health decision-makers during an infectious disease outbreak. AIDO is a web-based tool designed to contextualize incoming infectious disease information during an unfolding event for decision-making purposes.Methods: Public health analysts of the Biology Division at Los Alamos National Laboratory curated a diverse library of historic disease outbreaks from publicly available official reports and peer reviewed literature to serve as a representation of the range of potential outbreak scenarios for a given disease. Available outbreak metadata are used to identify properties that relate to the magnitude and/or duration of the outbreak. Properties vary by disease, as they are related to disease-specific characteristics like transmission, disease manifestation, risk factors related to disease severity, and environmental factors specific to the given location. These properties are then incorporated into a similarity algorithm (s in Figure 1) to identify outbreaks that are similar to user inputs.Results: AIDO currently includes libraries for 39 diseases that are diverse across pathogen type (viral, bacterial and parasitic) as well as transmission type (vectorborne (e.g., Dengue, Malaria), foodborne (e.g., Salmonella, Campylobacteriosis), waterborne (e.g., Cholera), and person-to-person transmitted (e.g., Measles)). In addition to providing a similarity score to the user’s outbreak, we provide aggregated comparisons to multiple historical outbreaks, descriptive statistics to show the distribution of property values for each disease, and extensive contextual information about each outbreak.Conclusions: The analytics provided by AIDO allow users to interact with a unique data set of historic outbreaks and the associated metadata to contextualize incoming information and generate hypotheses about appropriate decisions. The tool is continually updated with new functionalities and additional data.

scholarly journals Creating a web-based electronic tool to aid tuberculosis (TB) cluster investigation: data integration in TB surveillance activities in the United Kingdom, 2013 to 2016

2018 ◽  
Vol 23 (44) ◽  
Author(s):  
Jennifer A Davidson ◽  
Laura F Anderson ◽  
Victoria Adebisi ◽  
Leonardo de Jongh ◽  
Andy Burkitt ◽  
...  
Keyword(s):  
Public Health ◽  
United Kingdom ◽  
Data Integration ◽  
Local Level ◽  
Variable Number Tandem Repeat ◽  
Strain Typing ◽  
Time Data ◽  
Web Based ◽  
Tb Control ◽  
The United Kingdom

Molecular technology to identify relatedness between Mycobacterium tuberculosis complex isolates, representative of possible tuberculosis (TB) transmission between individuals, continues to evolve. At the same time, tools to utilise this information for public health action to improve TB control should also be implemented. Public Health England developed the Strain Typing Module (STM) as an integral part of the web-based surveillance system used in the United Kingdom following the roll-out of prospective 24 loci mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) strain typing. The creation of such a system required data integration and linkage, bringing together laboratory results and patient notification information. The STM facilitated widespread access to patient strain typing and clustering results for the public health community working in TB control. In addition, the system provided a log of cluster review and investigation decision making and results. Automated real-time data linkage between laboratory and notification data are essential to allow routine use of genotyping results in TB surveillance and control. Outputs must be accessible by those working in TB control at a local level to have any impact in ongoing public health activity.

UGLEO: A WEB BASED INTELLIGENCE CHATBOT FOR STUDENT ADMISSION PORTAL USING MEGAHAL STYLE

2018 ◽  
Vol 23 (3) ◽  
pp. 175-191
Author(s):  
Anneke Annassia Putri Siswadi ◽  
Avinanta Tarigan
Keyword(s):  
Markov Chain ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Need ◽  
Web Based ◽  
Markov Chain Method ◽  
Information Center

To fulfill the prospective student's information need about student admission, Gunadarma University has already many kinds of services which are time limited, such as website, book, registration place, Media Information Center, and Question Answering’s website (UG-Pedia). It needs a service that can serve them anytime and anywhere. Therefore, this research is developing the UGLeo as a web based QA intelligence chatbot application for Gunadarma University's student admission portal. UGLeo is developed by MegaHal style which implements the Markov Chain method. In this research, there are some modifications in MegaHal style, those modifications are the structure of natural language processing and the structure of database. The accuracy of UGLeo reply is 65%. However, to increase the accuracy there are some improvements to be applied in UGLeo system, both improvement in natural language processing and improvement in MegaHal style.

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