%0 Journal Article
%T Online detection and quantification of epidemics
%A Camille Pelat
%A Pierre-Yves Bo£¿lle
%A Benjamin J Cowling
%A Fabrice Carrat
%A Antoine Flahault
%A S¨¦verine Ansart
%A Alain-Jacques Valleron
%J BMC Medical Informatics and Decision Making
%D 2007
%I BioMed Central
%R 10.1186/1472-6947-7-29
%X We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at http://www.u707.jussieu.fr/periodic_regression/ webcite. The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea).The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners.The generalization of electronic data capture in health care has made time series data increasingly available for public health surveillance [1]. How to best analyse these data will likely be case dependent and require expert statistical advice. There is however a well agreed "good analysis practice" in particular classes of surveillance problems, so that less expert users may consider undertaking the analysis themselves. This requires making software available online and providing guidance on its use: this is exactly what was done with online tools for DNA sequences alignment (BLAST, FASTA), allowing biologists to successfully use these methods on their own data.Here, we focus on epidemic detection and quantification from time series data. There is a widely used approach for this purpose originating from Serfling's work on influenza [2]. He proposed calculating excess P&I mortality due to seasonal influ
%U http://www.biomedcentral.com/1472-6947/7/29