FluDetWeb: an interactive web-based system for the early detection of the onset of influenza epidemics

David Conesa,Francisco Verdejo,María Teresa Miralles,Antonio López-Quílez,Miguel Ángel Martínez-Beneito

doi:10.1186/1472-6947-9-36

Abstract

BackgroundThe 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.ResultsIn 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/.ConclusionThe 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.

Highlights

The early identification of influenza outbreaks has became a priority in public health practice
In what follows we present a case study to demonstrate how our web-based application allows users to obtain the posterior probability of being in an epidemic phase, and so rapidly detect when the annual flu epidemic period starts
We will use the data set introduced above, consisting of the thirteen time series formed by the weekly influenza-like illness (ILI) incidence rates provided by the Valencian Sentinel Network (VSN) during the seasons from 1996–1997 to 2008–2009

Summary

Introduction

The early identification of influenza outbreaks has became a priority in public health practice. Public Health agencies use disease surveillance tools in order to monitor the incidence or prevalence of specific health problems over time. This knowledge allows them to detect changes in the estimated incidence rates, which produces better planning and allocation of resources and the possibility of avoiding breakdowns in Health Care Systems. Several statistical methods have been proposed (and most of them applied) over recent decades for detecting outbreaks and informing health authorities of the presence and spread of disease (see LeStrat [1], Buckeridge [2] and Burkom [3] for comprehensive surveys of these kinds of methods and Bravata et al [4] for a critical evaluation of the potential utility of surveillance systems for illnesses and syndromes related to bioterrorism up to that date)

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Conclusion