A profile of the online dissemination of national influenza surveillance data

<p>Abstract</p> <p>Background</p> <p>Influenza surveillance systems provide important and timely information to health service providers on trends in the circulation of influenza virus and other upper respiratory tract infections. Online dissemination of surveillance data is useful for risk communication to health care professionals, the media and the general public. We reviewed national influenza surveillance websites from around the world to describe the main features of surveillance data dissemination.</p> <p>Methods</p> <p>We searched for national influenza surveillance websites for every country and reviewed the resulting sites where available during the period from November 2008 through February 2009. Literature about influenza surveillance was searched at MEDLINE for relevant hyperlinks to related websites. Non-English websites were translated into English using human translators or Google language tools.</p> <p>Results</p> <p>A total of 70 national influenza surveillance websites were identified. The percentage of developing countries with surveillance websites was lower than that of developed countries (22% versus 57% respectively). Most of the websites (74%) were in English or provided an English version. The most common surveillance methods included influenza-like illness consultation rates in primary care settings (89%) and laboratory surveillance (44%). Most websites (70%) provided data within a static report format and 66% of the websites provided data with at least weekly resolution.</p> <p>Conclusion</p> <p>Appropriate dissemination of surveillance data is important to maximize the utility of collected data. There may be room for improvement in the style and content of the dissemination of influenza data to health care professionals and the general public.</p

Digital Dashboard Design Using Multiple Data Streams for Disease Surveillance With Influenza Surveillance as an Example

Background: Great strides have been made exploring and exploiting new and different sources of disease surveillance data and developing robust statistical methods for analyzing the collected data. However, there has been less research in the area of dissemination. Proper dissemination of surveillance data can facilitate the end user's taking of appropriate actions, thus maximizing the utility of effort taken from upstream of the surveillance-to-action loop. Objective: The aims of the study were to develop a generic framework for a digital dashboard incorporating features of efficient dashboard design and to demonstrate this framework by specific application to influenza surveillance in Hong Kong. Methods: Based on the merits of the national websites and principles of efficient dashboard design, we designed an automated influenza surveillance digital dashboard as a demonstration of efficient dissemination of surveillance data. We developed the system to synthesize and display multiple sources of influenza surveillance data streams in the dashboard. Different algorithms can be implemented in the dashboard for incorporating all surveillance data streams to describe the overall influenza activity. Results: We designed and implemented an influenza surveillance dashboard that utilized self-explanatory figures to display multiple surveillance data streams in panels. Indicators for individual data streams as well as for overall influenza activity were summarized in the main page, which can be read at a glance. Data retrieval function was also incorporated to allow data sharing in standard format. Conclusions: The influenza surveillance dashboard serves as a template to illustrate the efficient synthesization and dissemination of multiple-source surveillance data, which may also be applied to other diseases. Surveillance data from multiple sources can be disseminated efficiently using a dashboard design that facilitates the translation of surveillance information to public health actions. © Calvin KY Cheng, Dennis KM Ip, Benjamin J Cowling, Lai Ming Ho, Gabriel M Leung, Eric HY Lau.link_to_OA_fulltex