Real-time outbreak and disease surveillance

Real-time outbreak and disease surveillance system (RODS) is a syndromic surveillance system developed by the University of Pittsburgh, Department of Biomedical Informatics.[1] It is "prototype developed at the University of Pittsburgh where real-time clinical data from emergency departments within a geographic region can be integrated to provide an instantaneous picture of symptom patterns and early detection of epidemic events."[2]

RODS uses a combination of various monitoring tools.[3]

  1. The first tool is a moving average with a 120-day sliding phase-I-window.
  2. The second tool is a nonstandard combination of CUSUM and EWMA, where an EWMA is used to predict next-day counts, and a CuSum monitors the residuals from these predictions.
  3. The third monitoring tool in RODS is a recursive least squares (RLS) algorithm, which fits an autoregressive model to the counts and updates estimates continuously by minimizing prediction error. A Shewhart I-chart is then applied to the residuals, using a threshold of 4 standard deviations.
  4. The fourth tool in RODS implements a wavelet approach, which decomposes the time series using Haar wavelets, and uses the lowest resolution to remove long-term trends from the raw series. The residuals are then monitored using an ordinary Shewhart I-chart with a threshold of 4 standard deviations.

References

  1. ^ RODS Laboratory Website
  2. ^ Public Health-Related Activities at the US HHS government website. Accessed December 2, 2010.
  3. ^ Shmueli, G., Burkom, H.S., "Statistical Challenges Facing Early Outbreak Detection in Biosurveillance", Technometrics (Special Issue on Anomaly Detection), vol. 52, issue 1, pp. 39–51, 2010


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