Software
FluDetWeb : an interactive web-based system for the early detection of the onset of influenza epidemics
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* Corresponding author: David Conesa david.v.conesa@uv.es
1 Departament d'Estadística i Investigació Operativa, Universitat de València, 46100 Burjassot (Valencia), Spain
2 Centro Superior de Investigación en Salud Pública, 46020 Valencia, Spain
3 Área de Epidemiología, Conselleria de Sanitat, Generalitat Valenciana, 46020 Valencia, Spain
4 Consultoría Promedio, 46006 Valencia, Spain
BMC Medical Informatics and Decision Making 2009, 9:36 doi:10.1186/1472-6947-9-36
Published: 29 July 2009Abstract
Background
The early identification of influenza outbreaks has became a priority in public health practice. A large variety of statistical algorithms for the automated monitoring of influenza surveillance have been proposed, but most of them require not only a lot of computational effort but also operation of sometimes not-so-friendly software.
Results
In this paper, we introduce FluDetWeb, an implementation of a prospective influenza surveillance methodology based on a client-server architecture with a thin (web-based) client application design. Users can introduce and edit their own data consisting of a series of weekly influenza incidence rates. The system returns the probability of being in an epidemic phase (via e-mail if desired). When the probability is greater than 0.5, it also returns the probability of an increase in the incidence rate during the following week. The system also provides two complementary graphs. This system has been implemented using statistical free-software (ℝ and WinBUGS), a web server environment for Java code (Tomcat) and a software module created by us (Rdp) responsible for managing internal tasks; the software package MySQL has been used to construct the database management system. The implementation is available on-line from: http://www.geeitema.org/meviepi/fludetweb/ webcite.
Conclusion
The ease of use of FluDetWeb and its on-line availability can make it a valuable tool for public health practitioners who want to obtain information about the probability that their system is in an epidemic phase. Moreover, the architecture described can also be useful for developers of systems based on computationally intensive methods.