Online detection and quantification of epidemics

Abstract

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).

Conclusion: 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.

Figure 1

Model selection algorithm. Graphical output of the model selection algorithm. Data and models are described in Table 2. Models selected through the algorithm pathway are in italics. The model finally kept is in bold italics.

Figure 2

Purge of the training period. Interactive selection of the method used to purge the training period of past epidemic outbreaks. Option 1 (delete the highest percentile of observations) was chosen. The percentile was set to 15% in a scrolling list ranging 0% to 60%.

Figure 3

Graphical output of the software. (a) Retrospective detection of influenza epidemics from monthly P&I mortality in France, 1968–1999. (b) Prospective analysis of gastrointestinal disease (2002–2007) and model-based extrapolation for 2008 with epidemic threshold. In all graphs: observed (grey), model (black), upper forecast limit (dashed).