Detecting flu and other disease outbreaks sooner – 2 September 2010

Posted on September 6, 2010. Filed under: Infectious Diseases, Influenza A(H1N1) / Swine Flu | Tags: , |

2 September 2010
Detecting flu and other disease outbreaks sooner

“New methods for detecting disease outbreaks earlier have been developed in a collaborative effort between CSIRO and NSW Health.
According to an article published recently in the journal Institute of Industrial Engineers Transactions, the new methodologies may enable health authorities to take action sooner to implement disease outbreak control measures.
“New methods developed by CSIRO statisticians have the potential to give an earlier-than-ever indication of whether a flu season is behaving normally or not,” says CSIRO Mathematics, Informatics and Statistics’ Chief, Dr Louise Ryan.”
…continues on the site

About the article mentioned:

Understanding sources of variation in syndromic surveillance for early warning of natural or intentional disease outbreaks 
Authors: Ross Sparksa; Chris Carterb; Petra Grahamc; David Muscatellod; Tim Churchesd; Jill Kaldord; Robyn Turnerd; Wei Zhengd; Louise Ryan
IIE Transactions, Volume 42, Issue 9 September 2010 , pages 613 – 631
DOI: 10.1080/07408170902942667
http://dx.doi.org/10.1080/07408170902942667

Abstract
Daily counts of computer records of hospital emergency department arrivals grouped according to diagnosis (called here syndrome groupings) can be monitored by epidemiologists for changes in frequency that could provide early warning of bioterrorism events or naturally occurring disease outbreaks and epidemics. This type of public health surveillance is sometimes called syndromic surveillance. We used transitional Poisson regression models to obtain one-day-ahead arrival forecasts. Regression parameter estimates and forecasts were updated for each day using the latest 365 days of data. The resulting time series of recursive estimates of parameters such as the amplitude and location of the seasonal peaks as well as the one-day-ahead forecasts and forecast errors can be monitored to understand changes in epidemiology of each syndrome grouping.

The counts for each syndrome grouping were autocorrelated and non-homogeneous Poisson. As such, the main methodological contribution of the article is the adaptation of Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) plans for monitoring non-homogeneous counts. These plans were valid for small counts where the assumption of normally distributed one-day-ahead forecasts errors, typically used in other papers, breaks down. In addition, these adaptive plans have the advantage that control limits do not have to be trained for different syndrome groupings or aggregations of emergency departments.

Conventional methods for signaling increases in syndrome grouping counts, Shewhart, CUSUM, and EWMA control charts of the standardized forecast errors were also examined. Shewhart charts were, at times, insensitive to shifts of interest. CUSUM and EWMA charts were only reasonable for large counts. We illustrate our methods with respiratory, influenza, diarrhea, and abdominal pain syndrome groupings.

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