Detection of Outbreaks from Time Series Data Using Wavelet Transform

@article{Zhang2003DetectionOO,
  title={Detection of Outbreaks from Time Series Data Using Wavelet Transform},
  author={Jun Zhang and Fu-Chiang Tsui and Michael M. Wagner and William R. Hogan},
  journal={AMIA ... Annual Symposium proceedings. AMIA Symposium},
  year={2003},
  pages={748-52}
}
In this paper, we developed a new approach to detection of disease outbreaks based on wavelet transform. It is capable of dealing with two problems found in real-world time series data, namely, negative singularity and long-term trends, which may degrade the performance of current approaches to outbreak detection. To test this approach, we introduced artificail disease outbreaks and negative singularities into a real world dataset and applied it and two other algorithms-autoregressive (AR) and… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 13 references

Using temporal context to improve biosurveillance.

Proceedings of the National Academy of Sciences of the United States of America • 2003
View 3 Excerpts

A study of the average run length characteristics of the national notifiable disease surveillance system

Ben Y. Reis, Kenneth D. Mandl
1999

Time series prediction using a multiresolution dynamic predictor

Fu-Chiang Tsui
Ph.D. dissertation, • 1996
View 1 Excerpt

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