Online detection and quantification of epidemics.
BACKGROUND
Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses.


RESULTS
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/. 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).


CONCLUSIONS
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.
Pelat
Camille
C
Université Pierre et Marie Curie-Paris 6, UMR-S 707, Paris, 75012 France. pelat@u707.jussieu.fr
Boëlle
Pierre-Yves
PY
Cowling
Benjamin J
BJ
Carrat
Fabrice
F
Flahault
Antoine
A
Ansart
Séverine
S
Valleron
Alain-Jacques
AJ
D000465:Algorithms; D003199:Computer Systems; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D005602:France / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D007251:Influenza, Human / Q000401:mortality; D009862:Online Systems; D011014:Pneumonia / Q000401:mortality; D016012:Poisson Distribution; D011159:Population Surveillance / Q000379:methods*; D040442:Public Health Informatics / Q000379:methods*; D012621:Seasons